Kamis, 03 Februari 2022

The Effect of Side Obstacles on Balang Tonjong Antang Traditional Market Activities, On-Road Performance

 by; Nur Khaerat Nur, Syahdan, Hasmar Halim

Department of Civil Engineering, Fajar University, Makassar, Indonesia 
Department of Civil Engineering, Politecnik State of Ujung Pandang, Makassar, Indonesia

Published under licence by Journal of Hunan University Natural Sciences 

Abstract

Balang Tonjong Antang Traditional Market is one of the traditional markets in the city of Makassar. The problem in this research area indicates a decrease in road performance that is characterized by frequent congestion on the road. This study aims to determine how the road performance affects side barriers depending on market activity. The components studied in this study are traffic flow, side barriers, vehicle speed, and average. The 1997 MKJI method was used in this study as an analytical technique to determine the traffic volume/hour (pcu/hour), the frequency of side barriers weights/hour, road capacity (C), degree of saturation (DS), and road service level (LOS), the results of the analysis at the location of this study indicate the average side obstacle class as High (H) (500-899), the road service level (LOS) is in class D. The average vehicle speed due to side barriers is 11.19 km/hour. Side barriers in this study greatly affect road performance on the Antang Raya road section, precisely in front of the Balang Tonjong traditional market. 

Keywords: side obstacle, traditional market, on-road performance.

侧面障碍物对巴朗通宗安塘传统市场活动、道路性能的影响 摘要:巴朗通宗安塘传统市场是望加锡市的传统市场之一。该研究领域的问题表明道路性能 下降,其特征是道路上频繁拥堵。本研究旨在确定道路性能如何因市场活动的侧面障碍而受到影 响。本研究中研究的组成部分是交通流量、侧障碍、车辆速度和平均值。本研究中使用的分析方 法使用 1997 MKJI 方法来确定交通量/小时 (控制器/小时), 侧障碍重量/小时的频率、道路通行能 力 (C)、饱和度 (DS) 和道路服务水平(服务水平),本研究地点的分析结果表明平均侧障碍等 级为高(H)(500-899),道路服务水平(服务水平)为 D 级。侧面障碍物的速度为 11.19 公 里/小时。本研究中的侧障极大地影响了 安塘开斋节路段的道路性能,正好位于 巴朗通宗传统市 场的前面。 关键词:侧面障碍,传统市场,公路性能。

1. Introduction 
Roads are the most important element in realizing economic growth in Indonesia to achieve dynamic stability; the performance of roads and road capacity in Indonesia really needs attention. What is meant by road performance is the ability of the road to the extent to which the road can carry out its role. The road service level provides an interest in traffic generation, which we can see with the standard of road capacity in the category of road service level [1], [2]. 
Antang Raya in Makassar is a very important and strategic road whose function is as a primary collector road. Primary collector roads connect provincial capital cities with district/city roads. The capacity of the road must be greater than the average traffic volume. 
Traffic jams that may often arise on the Antang highway in front of the Balang Tonjong traditional market can disrupt community activities. Congestion that may often occur at this time can negatively impact both on-road users and other vehicle drivers; congestion will definitely create tension (stress). Congestion will also appear as if there can be a negative impact from the economic side, the loss of time because the driver's journey is getting slower, besides that it will also be able to trigger negative effects such as vehicle noise disturbances on residents around the road that are experiencing congestion. Based on the author's initial follow-up, at the location to be researched, there are vehicle activities that stop on the road, parking on the road, pedestrian activities using the road, vehicles entering and leaving the market and slow-moving vehicles, vehicles that have the potential to cause side barriers and disrupt the performance of the Antang Raya road, which is located around the Balang Tonjong Antang traditional market. Seeing the potential losses that can be caused by the presence of side barriers to the activities of the Balang Tonjong Traditional Market on Jalan Antang Raya, the researchers then tried to take the title of the research on "The Effect of Side Barriers to Activities of Balang Tonjong Traditional Market, Antang on Road Performance".

2. Research Objectives and Problem Formulation 
2.1. Destination 
How is the performance of the Antang Raya road due to the side barriers of Balang Tonjong traditional market activities? 
2.2. Hypothesis 
To determine the performance of the Antang Raya road due to side barriers to the activities of the Balang Tonjong traditional market. 

3. Literature Review 
The road is a means of land transportation covering all parts of the road, including complimentary road buildings that have been designated for traffic, which are located above ground level and above ground, water, except for railway lines, lorry lines, and cable roads [3]. 
Traffic facilities and infrastructure, including road markings, traffic signal lights, and road user safety and road support facilities, are supporting infrastructure for the safety of road users, which are included in traffic infrastructure [12]. Roads are grouped according to their status, namely arterial, collector, local, and environmental roads [5]. 
hysically, a market is a place of concentration for several permanent and non-permanent traders. The market is also usually in an enclosed or open space or on the street. Traders usually occupy the market in their respective buildings available in the market area, temporary, semi-permanent, or permanent buildings. The markets can also be divided into general and special markets [11]. The traffic congestion factor that usually occurs when observed on-road services is considered according to traffic conditions that are not running stable. The average speed of traffic starts to slow because of obstacles that interfere with existing road capacity so that traffic vehicles freedom of movement is somewhat decreased. In this situation, the volume-capacity situation is greater than or equal to 0.75 V/C > 0.75; if the service level has reached E, the traffic flow becomes unstable, resulting in a long delay called a traffic jam [9]. Congestion increases when the current is so high that the vehicles are very close to each other. Total congestion occurs when the vehicle must stop or move slowly [8]. Speed is the speed of a trip which is generally expressed in km/hour. 
Speed and travel time are fundamental measures of traffic performance of the existing road system, and speed is a key factor in redevelopment or new construction. Traffic simulation evaluates travel speed and speed to measure performance, layout, demand, and monitoring of road systems [10]. Space differences are caused by traffic variations, geometric design, and traffic control. differences by vehicle type (intermodal) are caused by differences in driver plans, vehicle performance capabilities, and road segment performance [6]. 

The capacity of a road segment is defined as the highest number of vehicles that can traverse a road segment per hour, in one direction for a two-lane road with a median or two-way total for a two-way road, for a certain unit of time under road and traffic conditions [13]. From the degree of saturation, it can be known whether the road segment will have adequate capacity or not. According to the Indonesian Road Capacity Manual [1], The performance of a road segment can be determined, the extent to which the capacity of a road can carry out its functions [7], where according to the 1997 MKJI used as a parameter is the degree of saturation (DS). 

4. Research Methods 
4.1. Research Place and Time 
This research was carried out on Jalan Antang Raya, precisely in front of the Balang Tonjong Antang traditional market, Makassar City, South Sulawesi. 
The time that will be used for this research will be carried out when the preliminary survey is completed; data collection will be carried out for 1 (one) week, namely Monday to Sunday, research hours are carried out from 07:00 to 18:00 with an interval of 15 minutes, with a time of 07:00 – 18:00 WITA. 

4.2. Method of Data Collection 
Primary Data, namely data obtained when conducting a survey when research will be carried out in the field. This data includes a. Road geometric conditions, road cross-sections, road situation maps. b. Traffic volume and traffic speed. c. The side barriers of activities used to analyze road capacity, types of vehicles to be observed are pedestrians (PED), parking and stopping vehicles (PSV), vehicles entering or leaving the side of the road (EEV), slow vehicles (SMV). d. The condition of market activity used to analyze the performance of the road segment. Types of vehicles to be observed are motorcycles (MC), light vehicles (LV), medium-heavy vehicles (HV). 
Secondary data, supporting data in this study, namely population data that can be obtained from relevant agencies and regulatory books that apply in Indonesia, which are the reference in conducting this research, namely the Indonesian Road Capacity Manual [2]. 

5. Results and Discussion
5.1. Traffic Volume                                                                                                                                      The traffic volume that has been obtained in 1 week of research on Monday - Sunday, April 26, 2021 - May 2, 2021, can be seen in Figure 1.



The results showed that the highest traffic volume was on Saturday, May 1, 2021, at 2025 smp/hour, and the lowest was on Sunday, May 2, 2021, at 1531 smp/hour.

5.2. Side Barriers (SF) 
The total weight of side resistance (SF)/hour that has been obtained in 1 week of research on Monday - Sunday, April 26, 2021 - May 2, 2021, can be seen in Figure 2. 


The results showed that the highest side drag (SF) was on Thursday, April 29, 2021, at 534.700 frequencies of occurrence/hour, and the lowest on Tuesday, April 27, 2021, at 385.000 frequencies of occurrence/hour. 

5.3. Speed (km/h) 
The average speed that was obtained during one week of research on Monday-Sunday, April 26, 2021 - May 2, 2021, can be seen in Table 1.



5.4. Capacity 
The results of the capacity (C) data processing that were obtained during one week of research on MondaySunday, April 26, 2021 - May 2, 2021, are given in Table 2. 


The data processing resulted in the lowest capacity of 2169.780 smp/hour and the highest of 2321.160 smp/hour.

5.5. Degree of Saturation (DS) 
The results of data processing of the degree of saturation (DS) that were obtained during one week of research on Monday-Sunday, April 26, 2021 - May 2, 2021, are given in Table 3.


The analysis of the degree of saturation (DS) revealed the highest result on Monday, April 26, 2021, the value of the degree of saturation was 0.91, and the lowest (0.71) was on Sunday, May 2, 2021. 

5.6. Road Service Level (LOS) 
The results of the level of road service that were obtained during one week of research on MondaySunday, April 26, 2021 - May 2, 2021, are given below: 
a). Monday, April 26, 2021, 0.91, According to [1], if the value varies within 0.85 – 2.00, it is categorized as (E) a traffic jam, the speed is low. 
b). Tuesday, April 27, 2021, 0.71, According to [2], if the value varies within 0.45 – 0.74, it is categorized as a value of (C) stable flow, the traffic starts to get crowded with limited speed. 
c). Wednesday, April 28, 2021, 0.74, According to [2], if the value varies within 0.45 – 0.74, it is categorized as a value of (C) stable flow, the traffic starts to get busy with limited speed. 
d). Thursday, April 29, 2021, 0.75, According to [2], if the value varies within 0.75 – 0.84, it is categorized as a (D) traffic saturation value, the speed starts to lower. 
e). Friday, April 30, 2021, 0.81, According to [2], if the value varies within 0.75 – 0.84, it is categorized as a (D) traffic saturation value, the speed starts to lower. 
f). Saturday, May 1, 2021, 0.87, According to [2], if the value varies within 0.85 – 2.00, it is categorized as a (E) traffic jam, the speed is low. g). Sunday, May 2, 2021, 0.71, According to [2], if the value varies within 0.45 – 0.74, it is categorized as a (C) stable flow, the traffic starts to get crowded with limited speed. 

6. Conclusions
Jalan Antang Raya, in front of the Balang Tonjong Makassar traditional market, is experiencing problems with its performance due to side barriers that reduce road capacity, the results of this study are the highest average service level (LOS) of 0.91 on Monday, April 26, 2021, and the lowest on Monday, Sunday, May 2, 2021, is 0.71, the average speed of vehicles on Monday to Sunday is 11.19 km/hour, and the largest traffic volume is found on Saturday, May 1, 2021, with a total volume of 2.025 smp/hour and the lowest on Sunday with a total volume of 1.531 pcu/hour.

References 
[1] MINISTRY Of PUBLIC WORKS, Indonesian Road Capacity Manual (MKJI), 1997. Directorate General of Highways and Ministry of Public Works, Jakarta. 
[2] M.K.J.I 1997. Directorate General of Highways. Department of Public Works, Jakarta. 
[3] INDONESIAN STATE SECRETARIAT. Government Regulation of the Republic of Indonesia Number 34 of 2006 Concerning Roads. Jakarta. 
[4] KURNIAWAN S, & SRIHARYANI L. Analysis of the Effect of Parking on the Road to the Performance of Jalan Jendral Ahmad Yani in Metro City (Case Study in Front of the Putra Baru Supermarket Shopping Center). Tapak (Construction Application Technology). Journal of Civil Engineering Study Program, 2019, 8(1): 9-19. 
[5] NANDA AD. Determination of Priority Development of Beach Tourism Objects in the District of Koba Sub-District, Central Bangka Regency. Doctoral Dissertation, Bandung National Institute, 2021. [6] NUGRAHA AH. Performance Analysis of Roads Sp. Blusuh–Sp Damai–Barong Tongkok–Mentiwan, West Kutai Regency, East Kalimantan. Student Journal Curve, 2017, 11(2): 469-483. 
[7] SUWARDI. The Effect of Parking on Road Agencies on Traffic on Jalan Purwosari-Gladag Surakarta. Journal of Civil Engineering, 2008, 7(2) July 2010 
[8] TAMIN OZ. Transportation Planning & Modeling. Bandung ITB Publisher. 1997. 
[9] TJANDRA A. The Effect of Parking on Road Agencies on Road Performance (Case Study: Jalan Raya Kalitidu Bojonegoro). Detection-Journal of Civil Engineering Unigoro, 2017, 2(2): 1-11. 
[10] WARPANI SP. Traffic Engineering. Bhratara Aksara, Jakarta. 2002. 
[11] WIDYATAMA A. The Role of Village Market Local Wisdom in the Community Economy. Pay Journal of Finance and Banking, 2019, 1(2): 77-89. 
[12] WIEMINTORO W. Planning Analysis of Flexible Pavement Thickness Using the 1987 Bina Marga Component Analysis Method on the Banjaran-Balamoa Road Section. 2020. 
[13] YUNIANTA A. The Effect of Road Body Parking Vehicle Maneuvering on Traffic Characteristics on Jalan Diponegoro Yogyakarta. Doctoral Dissertation, Postgraduate Program at Diponegoro University. 2006. 

参考文: 
[1] 公共工程部,印度尼西亚道路通行能力手册 (MKJI), 1997 年。雅加达公路和公共工程部总局。
[2] M.K.J.I 1997. 公路总局。雅加达公共工程部。 
[3] 印度尼西亚国务卿。印度尼西亚共和国政府关于道路的 2006 年第 34 号法规。雅加达。 
[4] KURNIAWAN S, 和 SRIHARYANI L. 分析停车对大都 会城 惹兰詹德拉艾哈迈德·亚尼道路性能的影响(太子巴 鲁超市购物中心前的案例研究)。 塔帕克 (施工应用技术). 土木工程研究计划杂志, 2019, 8(1): 9-19. 
[5] NANDA AD。邦加中央摄政区科巴分区优先发展海滩 旅游对象的确定。博士论文,万隆国立研究所,2021。 
[6] NUGRAHA AH. 道路性能分析。 蓝苏–大麦–巴龙东角– 门提万,西库泰摄政,东加里曼丹。学生期刊曲线,2017, 11(2):469-483。 
[7] SUWARDI。停车对道路机构对 惹兰普沃萨里-格拉达 格苏拉卡塔 交通的影响。土木工程学报, 2008, 7(2) 2010 年 7 月 
[8] TAMIN OZ。交通规划与建模。万隆 ITB 出版社。 1997。 
[9] TJANDRA A. 道路机构停车对道路性能的影响(案例 研究:惹兰拉雅卡利蒂杜·博约内戈罗)。土木工程检测 学报 独五郎, 2017, 2(2): 1-11. 
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Study of Flood Disaster Mitigation Analysis for Transportation Routes in Panakukkang District, Makassar City

oleh : Nur Khaerat Nur, Andi Ibrahim Yunus, Andi Muhammad Dayri Satriawan 

Department of Civil Engineering, Faculty of Engineering, Fajar University, Makassar.

*corresponding author : enkha93@gmail.com

Published under licence by IOP Publishing Ltd
Citation N K Nur et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 921 012018

https://iopscience.iop.org/article/10.1088/1755-1315/921/1/012018/meta

Abstract; This study conducted an analysis study of flood disaster mitigation for transportation routes in the Panakukkang district of Makassar City. By using ArcGis software, the results of the simulation of safe and vulnerable zone levels based on color indicators are known. There are 5 villages in Panakukkang District which are flood safe zones, with the number of evacuation sites, namely 21 buildings. Then there are 4 villages which are flood alert zones with 2 evacuation sites, 2 buildings. On the first evacuation route there are 8 reference points namely Reference Point C with the distance to the nearest evacuation site 3.22 km and a travel time of 64.3 minutes. Then the reference point A with a distance to the nearest evacuation site is 2.85 km and a travel time of 57 minutes. While the reference point F is the closest point to the nearest evacuation distance 0.71 km and the travel time is 14.2 minutes. All these reference points require travel speeds of 3 km / h on foot. On the second evacuation route there are 6 Reference Points namely reference point A with distance to the nearest evacuation point 1.94 km and travel time 38.8 minutes, reference point E with distance to nearest evacuation location 1.23 km and travel time 24.6 minutes. Then at the reference point C is the closest point to the nearest evacuation distance 0.72 km and the travel time is 14.4 minutes.

1. Introduction
One of the natural phenomena that occurs in some parts of Indonesia in the form of floods, causing huge losses that can threaten human life. Based on the value of losses and the frequency of flood disasters is strongly influenced by natural factors such as the amount of rainfall above normal conditions and high tides. In addition, the factor of human behavior is one of the causes of floods [1]. Flooding is an event that occurs when an excessive flow of water submerges land. Directing flooding The European Union defines flooding as temporary immersion by water on land which is usually not submerged in water. In the meaning of "running water", this word can also mean the entry of tides. Flooding is caused by the volume of water in a body of water such as a river or lake that overflows or overflows from a dam so that water comes out of the river. Floods are often known in two forms, in the form of inundation in zones that are usually dry or not swampy, and flooding as a result of runoff from river channels caused by discharge in the river exceeds its drainage capacity [2]. Puddles have different characteristics depending on the area of the puddle, the depth of the puddle, the length of the puddle and the frequency of the occurrence of puddles if not immediately addressed will cause greater harm to the community [3]. Flood Mitigation in Nusukan sub-district, Banjarsari Sub-district, Surakarta City, Drainage such as sewers and culverts that are still not wide enough and siltation occurs, so that if there is heavy rainfall, the water volume will increase and can affect the swift flow of water in these waterways and water can overflow to the surface [4].
Flooding is a common problem found in several large cities, such as Makassar city which is the center of the capital of the province of South Sulawesi. The tragedy of the flood disaster on January 21, 2019 which had hit most of the South Sulawesi Province, especially in Makassar City and Gowa Regency, still left a deep sorrow and also a huge loss for the flood victims. Panakukkang District is one of 14 sub-districts in Makassar. Panakukkang District can be said to be the focal point of Makassar city because there are many Government agency offices, corporate offices, hotels, entertainment venues, shopping venues, and there are 2 (two) malls located in this district. But behind the grandeur that all is not supported by adequate infrastructure, from the lack of green land, and the poor drainage / drainage system in this zone. As it is known that besides functioning as a supporting factor for life, green land also functions as a water catchment zone. Likewise, the sewer / drainage does not function properly so it cannot drain water smoothly in the event of prolonged rain. Case in point: as can be felt if there is prolonged rain, Andi Pangeran Pettarani street road becomes one of the zones that become a puddle point, where the height of the puddle sometimes reaches the knees of adults. Likewise with Street Sukaria, and Andi Pangeran Pettarani Street I also have the same case. Different from what happened in the Pampang village, if it is rained with rain it has a high intensity it is certain that the zone will be immediately flooded and the inundation height reaches approximately 100 cm of data according to [5]. Such is the picture that occurs at several points in Panakukkang Sub district, which is one area that has a large population, so it is important for the community to be more important to understand mitigation / mitigation efforts againstflooding, by knowing which zones are the points prone to puddles and which zones are safe points if later Panakukkang sub-district is affected by the tidal / flood waves that inundate the zone. Mitigation is an action taken to reduce the impact caused by a disaster. Mitigation measures consist of structural mitigation and non-structural mitigation. Structural mitigation is an action to reduce or avoid the possibility of physical disaster impacts. While non-structural mitigation is an action to reduce disaster risk through policies, knowledge development, regulations and security of dangerous objects. Mitigation is the most efficient measure to reduce the impact caused by a disaster [6]. 
Based on the Makassar City Regional Disaster Management Agency (Zone Disaster Management Agency) data that there are 3 (three) villages that are flood-prone points in Panakukkang sub-districts, namely: Panaikang, Paropo, and Tello Baru. However, the data from the the Makassar City Regional Disaster Management Agency is data from 2014, therefore re-calibration of data is needed to validate the data from the above the Makassar City Regional Disaster Management Agency [5].
Departing from the above, it is deemed necessary to conduct a flood disaster mitigation analysis study for transportation routes in the Panakukkang district of Makassar city, as a reference to obtain the latest data on flood disaster prone zones in the city of Makassar especially in the Panakukkang district 

2. Materials and Methods 
2.1. Disaster Mitigation 
Disaster mitigation is a series of efforts to reduce disaster risk, both through physical development and awareness and enhancement of the ability to face the threat of disaster [7] concerning the Implementation of Disaster Management. Mitigation is defined as an effort aimed at reducing the impact of a disaster. Mitigation is a series of efforts to reduce disaster risk, both through physical development and awareness and enhancing the ability to face the threat of disaster [8], Chapter I General Provisions [9]. 

2.2. Geographic Information System (GIS) 
According to [10] GIS is an information system that has spatial data taken based on the geographical location of an area for the process of analysis, storage and visualization. GIS or GIS is a special information system that manages data that has spatial information (spatial reference), or in a more narrow sense, is a computer system that has the ability to build, store, manage and display geographical reference information, for example data identified by location , in a database. Utilization of Geographic Information Systems and Simple Additive Weighting Methods in Determining the Locations of Flood Evacuation in Surakarta City. There are 6 best locations. The six locations have the same criteria and sub criteria, namely to determine the evacuation location in order to become a temporary shelter when floods hit the city of Surakarta [11]. 

2.3. Determine the Safe Zone and Flood Prone Zone
To identify critical zones of urban flood puddles using GIS-based software by combining some data that is automatically processed by the system with its own aggregate count. 1. Determine Coordinate Points 2. Flow Direction Serves as data to determine the direction of water flow along the research object. 3. Flow Accumulation Get the value of water accumulation in each runoff. 4. Watershed / River Flow Zone To find out which water runoff will be upstream and downstream. 5. Basin Create drainage basins that connect all water flow and other substances carried by water to public channels as concentrated drainage / ponds.6. Reclassify After all processes are completed, it is known which zones are at risk of being flooded. 

2.4. Evacuation Area and Shelter Point (Refugee Camp) 
The refugee camp is an area that is close to the centers of the residential neighborhoods which, in the event of a disaster, becomes a meeting point for residents who want to be evacuated to safer places. The criteria used as places of refuge in this study are: 1. The place / building used as the evacuation location of the ground level must be at a safe point, according to [5], the safe distance from the ground level is the middle of the highest elevation point in the zone. 2. The location must have direct access to the primary road (district road) or secondary road (local road) with a distance or range of approximately 30 meters. 3. The building must have a large area that can accommodate large numbers of people, or at least 225 square meters. Data Architect, said that the standard size of space needed for each person at the evacuation site is 2.25 m2 in standing (1.5 max 1.5 m) and 1.08 m2 in a state of lying (1.8 max 0.6 m). In this case, the highest size of space is taken, is standing 2.25 m2 [12-13]. 

2.5. Designing Evacuation Routes 
2.5.1. PTV Vissim
PTV Vissim is a transportation modeling program to analyze the existing traffic conditions, such as: the creation of new road infrastructure, the changeover or diversion to the use of public transportation, the development of new land use zones, the development of policies regarding signs, markers, and other road completeness facilities, demographic developments and changes, developments and changes in the use and expenditure of funds for fuel, and others. In the guideline for using Vissim, Vissim is a PTV software from Germany. Vissim itself is an acronym for Verkehr In Städten Simulations Modell which means a simulation model of city traffic [14]. Simply put, making a model using VISSIM PTV is divided into 5 stages, namely: 1. Identify the Scope of the Area to Be Modeled 2. Data Collection As for the data needed, namely: the size of the road segment, vehicle volume, road segment conditions, and others. 3. Network Coding Stages of making a model based on data that has been obtained. 4. Error Checking Review if something goes wrong, as is usual with vehicle routes or vehicle inputs. 5. Model Validation / Simulation 

2.5.2. Evacuation route 
Evacuation Route is a path that is specifically intended to connect all areas to a safe area as a Gathering Point. In an emergency, the Evacuation Route becomes very important and absolutely must be placed as a direction. [15] in [16] Evacuation route is a path that is used as a direct and fast transfer of people who will move away from threats or events that can be dangerous. There are two types of evacuations that can be distinguished, namely small-scale evacuation and large-scale evacuation. Examples of small-scale evacuation that is a rescue carried out from a building caused by a bomb or fire threat. Examples of large-scale evacuation that is saving from an area due to flooding, eruption volcano or storm. Geraldo, et al, [17], Flood Tragedy in Manado City. Evacuation routes and evacuation sites in flood-prone areas in Manado City are spread in 10 sub- districts, namely: Bunaken District with 6 lanes and 4 evacuation sites, Tuminting District with 31 routes and 17 evacuation sites, Singkil District with 18 routes and 10 evacuation sites, Wenang District 16 routes and 8 evacuation sites, Paal Dua District 27 routes and 18 evacuation sites, Mapanget District 6 routes and 3 evacuation sites, Tikala District 20 routes and 13 evacuation sites, Sario District 15 routes and 7 evacuation sites, Wanea District as many as 25 routes and 22 evacuation sites, and Malalayang District as many as 8 routes and 3 evacuation sites. 

3. Results and Discussion 
3.1. Mapping Flood Areas 
Based on the results of data analysis of flood area mapping in Panakukkang Subdistrict, the results of 5 district which are very safe flood zones are: some Panaikang District, Tello Baru District, Paropo district, Pandang District, and Masale District; 6 district which are flood safe zones, namely: aportion of Panaikang Village, Tello Baru Village, Paropo Village, Karampuang Village, Pandang Village, and Masale Village; 6 district which are flood-prone zones, namely: a portion of Tello Baru Village, Paropo Village, Karampuang Village, Tamamaung Village, Masale Village, and Karuwisi Village; 4 district which are very flood-prone zones, namely: a portion of Pampang Village, Panaikang Village, Tamamaung Village, and North Karuwisi Village; and 4 district which are flood alert zones, namely: Sinri Jala District, parts of Pampang District, Tamamaung District, and North Karuwisi District.



In table 1 shows data on land elevation, the value of water accumulation, the basin zone where the data is a parameter to determine the condition of the safe, vulnerable, and alert zones. The condition of land with high or low elevation in the form of hills or valleys is one of the factors where the zone is said to be safe or prone to flooding due to lack of availability of runoff / river flow zones and water catchment zones so that it becomes a pool of water that causes flooding in the area. 

In table 2 shows the categories of floods, ranging from very safe flooding, safe flooding, flood prone, very prone to flooding and flood alert with a total area of affected zones based on wards, that is, for very safe categories covering 257,802ha covering the villages of Panaikang and Tello Baru, Pandang and Paropo , Pampang, Panaikang, Tello Baru, Paropo, Masale. For the Safe Category covering an area of 321,488ha, covering Panaikang, Karampuang, Paropo, Tello Baru, Pandang and Masale villages. For the Prone category covering an area of 274,821ha covering the villages of Karuwisi, Tamamaung, Karampuang, Masale, Paropo, Tello Baru and North Karuwisi. For the Very Prone category covering an area of 287,344ha, covering the villages of Tamamaung, Pampang, Panaikang and North Karuwisi. Then for the vulnerable category covering an area of 379.52ha, covering the villages of North Karuwisi, Pampang, Tamamaung, and Sinri Jala. So the total area affected by flooding (prone, very vulnerable, and alert) in the Panakukkang sub-district is 9.41 km² with the total area of the Panakkukang sub-district amounting to 17.05 km². 

3.2. Evacuation Area and Shelter Point (evacuation site) 
According to [12], the area or building used as an evacuation area with safe area conditions, is said to be a safe flood area if the height of the surface of the puddle is in the middle of the height of the evacuation area. Evacuation areas and evacuation sites must have direct access to primary roads (district roads) or secondary roads (local roads) with a distance or range of approximately 30 meters and buildings must have a large enough area that can accommodate many people, or have an area of more or less 225 square meters. 


In Table 3 shows the area, the distance from the main road to the evacuation area with a capacity based on the condition of the area and the evacuation area. There are 2 regional conditions that can be used as evacuation areas, namely: safe areas and vulnerable areas. For safe flood evacuations, namely: South Sulawesi Governor's Office, Baiturahman Mosque, Panaikang Police Dormitory, Kodam XIV Hasanuddin, Batua Sulsel SPN, Litha and Co Terminal, BLK Makassar Building, SMAN 5 Makassar Building, MaxOne Hotel Makassar Building, Camba Field, Camba Field, Makassar Fajar University Building, Makassar Sultan Alauddin Mosque, Electric Futsal Field, BBIH Estate Building, STMIK Handayani Makassar Building, SwissBel Hotel, Myko Hotel, Panakukkang Mall, and PT. Sinar Galesong Mandiri. As for the conditions of flood-prone areas, namely: Building of the Muslim University of Indonesia and Makassar Nipah Mall. 

3.3. Flood Evacuation 
Transportation Routes After doing the data it is known that the Panakukkang District is one of the areas with high flood-prone levels, it can be seen in the table that the total area of the flood area is 9.41km² to the area of Panakukkang District which is 17.05km². With the data obtained from safe areas and flood-prone areas, the data becomes a reference to determine which routes will be evacuation routes to get to floodsafe areas. In determining the evacuation route, the transportation route is divided into 2 flood evacuation routes. 

3.4. First Flood Evacuation Route 
In the first evacuation route, there were determined 8 (eight) roads that would serve as the evacuation destination route. All roads that have been evacuation routes have been surveyed to determine their suitability with the required evacuation criteria. After that, a road network analysis is carried out to obtain an evacuation route as shown in Figure 2.



In Figure 2, there are 8 (eight) reference points which are flood-prone areas, where the reference point is the farthest point from the evacuation site of each evacuation route which will be a benchmark of how long the mileage will be required from each reference point to go to the evacuation site. In the Decree of the Director General of Land Transportation No. : SK.43 / AJ 007 / DRJD / 97, walking speed in normal situations is 1.5 km/hour, in this case a speed sample is taken in an emergency situation (evacuation) of 3 km / hour. 

Based on Table 4, it is known that there are 8 Reference Points on the first evacuation route. Reference Point C becomes the farthest point that must be reached with the distance to the nearest evacuation site as far as 3.22km and the travel time of 64.3 minutes. Then reference point A with the distance to the nearest evacuation site is 2.85 km and travel time for 57 minutes. While the reference point F is the closest point that must be traveled with the distance to the nearest evacuation site as far as 0.71 km and travel time for 14.2 minutes. All of these reference points on the first evacuation route equire a travel speed of 3 km / h on foot. 

3.5. Second Flood Evacuation Route 
In the first evacuation route, it was determined that there were 6 (six) roads that would serve as the evacuation destination route. All roads that have been evacuation routes have been surveyed to determine their suitability with the required evacuation criteria. After that, a road network analysis is carried out to obtain an evacuation route as shown in Figure 3.


In Figure 3, there are 6 reference points which are flood-prone areas, where the reference point is the farthest point from the evacuation site of each evacuation route which will be a benchmark of how much distance will be needed from each reference point to get to the evacuation site. In the Decree of the Director General of Land Transportation No. : SK.43 / AJ 007 / DRJD / 97, walking speed in normal situations is 1.5 km / hour, in this case a speed sample is taken in an emergency situation (evacuation) of 3 km / hour.



Based on Table 5, there are 6 Reference Points found on the second evacuation route. Reference Point A becomes the farthest point that must be reached with the distance to the nearest evacuation site as far as 1.94 km and travel time of 38.8 minutes. Then the reference point E with the distance to the nearest evacuation site is 1.23 km and the travel time is 24.6 minutes. While the reference point C is the closest point that must be traveled with the distance to the nearest evacuation site as far as 0.72km and travel time for 14.4 minutes. 

4. Conclusion 
Based on the results of the analysis and discussion of the data, the following conclusions are obtained:
The total area affected by flooding (Prone, very vulnerable, and alert) in Panakukkang District is 9.41 km² of the total area of 17.05 km². The category of flooding based on district, that is, for the very safe category of 257,802 ha includes Panaikang and Tello Baru, Pandang and Paropo, Pampang, Panaikang, Tello Baru, Paropo, Masale district. For the Safe Category covering an area of 321,488ha, covering Panaikang, Karampuang, Paropo, Tello Baru, Pandang and Masale villages. For the Prone category covering an area of 274,821ha covering the villages of Karuwisi, Tamamaung, Karampuang, Masale, Paropo, Tello Baru and North Karuwisi. For the Very Prone category covering an area of 287,344ha, covering the villages of Tamamaung, Pampang, Panaikang and North Karuwisi. Then for the Waspada category covering an area of 379.52ha, covering the villages of North Karuwisi, Pampang, Tamamaung, and Sinri Jala. Places that can be used as evacuation areas (safe zones), namely: the Office of the Governor of South Sulawesi (20,331 people), Baiturrahman Mosque (658 people), Panaikang Police Dormitory(12,502 people), Kodam XIV Hasanuddin (17,361 people), SPN Batua Sulsel (8,262 people), Litha & Co Terminal (4,402 people), BLK Makassar Building (2600 people), SMAN 5 Makassar Building (2,080 people), MaxOne Makassar Hotel Building (4,914 people), Cambajawayya Lapland (1,646 people), Makassar Fajar University Building (4,192 people), Sultan Alauddin Makassar Mosque (392 people), Electric Futsal Field (320 people), Large Plantation Industry Hall (2,050 people), STMIK Handayani Makassar Building (1,566 people), SwissBel Hotels (4,057 people), Hotels Myko (1,685 people), Panakukkang Mall (5,614 people), and PT. Sinar Galesong Mandiri (1,800 people), with a total capacity of 123,131 people. For transportation routes divided into 2 routes: - The first route is centered north of Panakukkang District, there are 8 reference points on the first evacuation route, Reference Point C with the distance to the nearest evacuation site as far as 3.22km and travel time of 64.3 minutes. Then reference point A with the distance to the nearest evacuation site is 2.85km and travel time for 57 minutes. While the reference point F is the closest point to the nearest evacuation distance of 0.71 km and the travel time of 14.2 minutes. All of these reference points on the first evacuation route require a travel speed of 3 km / h on foot. - The second route is centered south of Panakukkang District. There are 6 Reference Points on the second evacuation route, namely reference point A with distance to the nearest evacuation site 1.94 km and travel time for 38.8 minutes, reference point E with distance to the nearest evacuation location 1.23 km and travel time for 24.6 minutes and then at reference point C is the closest point with distance to the nearest evacuation site as far as 0.72km and travel time for 14.4 minutes. 

References 
[1] Muchlis A 2017 Analysis of Flood Disaster Management in the District of Ganra, Soppeng          Regency,2017.
[2] Siswoko 1985 Flood Control Patterns on the River. Irrigation Bulletin 
[3] Bunganean W et al 2017 Analysis of Areas Affected by Puddles Using GIS-Based Data Management, Journal of Civil Engineering 232 pp 231-240 
[4] Evita D L 2014 Flood Mitigation in Nusukan Sub-District, Banjarsari Sub-District, Surakarta City Final Project of the Undergraduate Program Muhammadiyah University, Surakarta 
[5] Makassar City Regional Disaster Management Agency 2018 Disaster Data 2018. 
[6] Pribadi K S, et al 2008 Disaster Preparedness Teacher's Handbook. (Bandung: ITB Disaster Mitigation Center) 
[7] PP No. 21 of 2008, Article 1 paragraph 6, concerning the Implementation of Disaster Management. [8] Law No. 24 of 2007, concerning Disaster Management. 
[9] PP No. 21 of 2008, concerning the Implementation of Disaster Management 
[10] Nico N 2019 Geographic Information System 
[11] Juliana and Charitas 2017 Utilization of Geographic Information Systems and Simple Additive Weighting methods in Determining Flood Evacuation Locations in Surakarta City 
[12] Pranoto S et al 2015 Analysis of the Effectiveness of the Flood Evacuation Route 
[13] Sunarto T 1996 Data Architect Volume 1 (ERNST NEUFERT) 
[14] Hakiim L Guidelines for the Use and Introduction of the Vissim PTV Program, 14.01.0732C/DIV Land Transportation, Ground Transportation College 
[15] Abrahams J 1994 Fire Escape in Difficult Circumstances Design Against Fire. United State of America 
[16] Geraldo B S, Hanny P, and Suryono 2016 Analysis Of Flood Disaster Evacuation Path In Manado City Student Thesis Study Program In The Area & City Planning Sam Ratulangi University, 2016. 71, 70-79 
[17] Geraldo et al 2014 Flood Tragedy in Manado City using ARC GIS 10.3 

Acknowledgements 
On this occasion, we thank you and highest appreciation for the assistance and guidance to the Rector of the Fajar University and staff, two loving parents who are always sending prayers and encouragement, the whole family as well as civil engineering students Fajar University who helped in the data survey. We look forward to feedback and constructive criticism for the sake of the development of this paper. Hopefully this article can be useful, especially for our personal and the public at large.

Kamis, 24 Agustus 2017

Studi Preferensi Tansformasi Moda Angkutan Pribadi Berdasarkan Karakteristik Perjalanan dan Perilaku Pengguna, oleh : Nur Khaerat Nur1*, Lawalenna Samang2, M. Isran Ramli3 and Sumarni Hamid4


ABSTRAK
Pada masa sekarang ini umumnya masyarakat lebih memilih untuk menggunakan kendaraan pribadi dibandingkan kendaraan umum dengan berbagai alasan seperti kenyamanan, waktu tempuh perjalanan lebih cepat, kapasitas angkutan umum tidak dioperasikan sebagaimana mestinya dan lain sebagainya sehingga berdampak pada  kemacetan dengan pertambahan jumlah kendaraan yang tidak sebanding dengan pertambahan volume jalan. Kajian ini merupakan studi preferensi untuk menjajaki persepsi transformasi pengguna angkutan pribadi (roda empat dan roda dua) berbasis karakteristik perjalanan dan perilaku pengguna. Rancangan pendekatan analisis terdiri dari metode statistik deskriptif untuk mendapatkan karakteristik perjalanan dan perilaku pengguna angkutan pribadi dalam hal biaya perjalanan, biaya operasi kendaraan, jarak tempuh dan waktu tempuh, aksesibilitas, mobilitas, pelayanan serta menggunakan pemodelan SEM Partial Least Square (PLS) untuk mendapatkan pengaruh interaktif karakteristik perjalanan pereferensi angkutan pribadi, kualitas pelayanan angkutan umum terhadap transformasi moda. Hasil penelitian berdasarkan analisa data diketahui yakni karkteristik perjalanan komuter bagi pengguna kendaraan pribadi dengan variabel indicator jarak tempuh dan waktu tempuh diharapkan dapat mempengaruhi penentuan jenis moda transportasi yang cepat dan waktu yang efisien. Faktor gengsi dan budaya berlalulintas dalam menggunakan moda angkutan pribadi komuter sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap Preferensi Angkutan Pribadi (PAP) komuter. Begitu pula halnya dengan persepsi responden pengguna angkutan pribadi komuter terhadap kualitas pelayanan angkutan umum bahwa faktor gengsi dalam menggunakan moda angkutan umum sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap kualitas pelayanan angkutan umum. Persepsi responden terhadap Transformasi moda yang di dimanifestasikan dengan variabel indikator  dalam  hal kesediaan berpindah menggunakan moda angkutan umum responden lebih memprioritaskan faktor waktu perjalanan, waktu tunggu dan total tarif  untuk bersedia pindah ke moda angkutan umum  kemudian disusul faltor-faktor lainnya.

Berdasarkan hasil uji hipotesis ditemukan bahwa Kualitas Pelayanan Angkutan Umum (KPAU) tidak signifikan berpengaruh terhadap Transformasi (TRF) sebesar 1,644. Karakteristik Perjalanan Komuter (KaerPerj)) tidak signifikan berpengaruh terhadap variabel endogen Preferensi Angkutan Pribadi (PAP) sebesar 0.973. Karakteristik Perjalanan Komuter (KaerPerj) tidak signifikan berpengaruh langsung terhadap Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 1,471. Karakteristik Perjalanan Komuter (KarPerj) signifikan berpengaruh langsung terhadap Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 5.461. Preferensi Angkutan Pribadi (PAP) tidak signifikan berpengaruh terhadap Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 0,224. Preferensi Angkutan Pribadi (PAP) signifikan berpengaruh terhadap Transformasi (TRF) sebesar 7,401.
Kata Kunci : transformasi, moda angkutan pribadi, komuter, preferensi, karakteristik perjalanan,     perilaku pengguna

1.   PENDAHULUAN
Kota Makassar dengan luas wilayah 175,77 km2 sebagai kota inti Kawasan Metropolitan Mamminasata dan berfungsi sebagai Pusat Kegiatan Nasional di Kawasan Timur Indonesia yang berpenduduk lebih kurang 1.5 juta jiwa mengalami laju pertumbuhan 9 % per tahun. Indikator ekonomi kota dengan PDRB didominasi oleh sektor perdagangan dan jasa. Oleh karena itu, Kota Metropolitan Makassar berdaya tarik sangat tinggi, tetapi menghadapi berbagai permasalahan perkotaan, yaitu urbanisasi dan kemiskinan, kawasan kumuh, transportasi, banjir, air bersih, sanitasi, dan persampahan.Pada suatu pergerakan antar kota, faktor pemilihan moda memegang peranan yang cukup penting, seseorang yang akan bergerak dari satu kota ke kota lain tentu akan mempertimbangkan banyak hal yaitu apakah pergerakan yang dilakukannya.

Masalah Transportasi yang dihadapi antara lain adalah: (1) tidak seimbangnya perkembangan prasarana jalan dihandingkan dengan pertumbuhan kendaraan, (2) pertumbuhan prasarana jalan kurang lebih 4% per tahun, dan (3) pertumbuhan kendaraan kurang lebih 14-15% per tahun. Survei yang dilakukan oleh ARSDS menyimpulkan bahwa perbandingan penggunaan kendaraan pribadi dan angkutan umum adalah 611:35. Hal ini jelas-jelas menunjukkan adanya ketidak efisienan penggunaan ruang jalan oleh pola penggunaan kendaraan. Pertumbuhan penggunaan kendaraan di Makassar menunjukkan adanya kecenderungan pemanfaatan kendaraan bermotor dan lenyapnya kendaraan tidak bermotor. Hal ini, akan menyebabkan tekanan pada lingkungan baik penggunaan energi maupun pencemaran lingkungan. Suatu kebijaksanaan yang diambil oleh Pemerintah, selain membangun infrastruktur penunjang transportasi tersebut adalah usaha pembatasan terhadap angkutan pribadi, dengan harapan pertambahan perjalanan dengan menggunakan angkutan umum.

Dengan demikian, praktisnya adalah mendorong pengguna kendaraan pribadi berganti menjadi pengguna angkutan umum. Namun, Makassar mengalami pertumbuhan ekonomi dan pertambahaan penduduk yang cepat. Pertumbuhan ekenomi yang pesat berdampak semakin tingginya pendapatan masyarakat, yang cenderung mendorong masyarakat untuk memiliki dan mengendarai kendaraan pribadi.

Semakin baiknya mutu prasarana angkutan dan kurang baiknya alat dan layanan sarana angkutan umum, juga semakin mendorong orang untuk memiliki dan mengendarai kendaraan pribadi. Hal-hal tersebut, akan menjadi kendala bagi upaya "memasyarakatkan" penggunaan angkutan umum. Di samping kemampuan ekonomis seseorang dan tersedianya sarana angkutan umum yang cukup jumlahnya, kiranya faktor-faktor lingkungan lainnya berpengaruh pada kecenderungan seseorang untuk memilih moda angkutan.

2.   METODE  PENELITIAN
a. Rancangan penelitian
Rancangan atau desain penelitian adalah perencanaan terinci yang digunakan sebagai pedoman studi penelitian yang mengarah pada tujuan dari penelitian tersebut, Aaker et al. (2001: 24). Metode penelitian yang digunakan adalah deskriptif analitis, yakni menggambarkan suatu peristiwa kemudian melakukan analisis terhadap masalah yang timbul. Studi ini dimulai dengan mengumpulkan literatur dan data sekunder yang berkaitan dengan penelitian yang dilakukan, kemudian menentukan teknik survai yang digunakan. Dalam penelitian ini peristiwa yang akan diobservasi adalah pemilihan moda angkutan pribadi. Adapun variabel-variabel yang diteliti adalah atribut karakteristik perjalanan dan perilaku pengguna pribadi. Teknik pengumpulan data adalah dengan wawancara atau penyebaran kuesioner kepada pengguna kendaraan pribadi pada orang bekerja (pegawai) dengan sisitim acak.  Bentuk pertanyaan formulir survai direncanakan meliputi dua hal. Pertama, pertanyaan difokuskan untuk mengetahui kondisi eksisting dari karakteristik pengguna kendaraan pribadi saat ini. Dalam hal ini ingin diketahui  informasi perjalanan yang dilakukan dengan menggunakan moda angkutan pribadi. Pertanyaan diarahkan untuk mengetahui preferensi responden seandainya kondisi hipotesis ditawarkan seperti aksesibilitas, mobilitas, pelayanan, biaya perjalanan dan waktu tempuh perjalanan. Dengan menggunakan data persepsi responden tersebut kemudian dilakukan analisis untuk mengetahui preferensi angkutan pribadi pada perjalanan komuter moda di Makassar. Penelitian ini dikembangkan berdasarkan permasalahan moda angkutan komuter dengan penekanan pada pengaruh prefensi angkutan pribadi terhadap transformasi moda angkutan komuter dengan pemodelan Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS).

b.   Lokasi dan waktu penelitian
Penelitian ini dilaksanakan di wilayah kota Makassar, khususnya pengguna kendaraan pribadi yakni pegawai negeri sipil (PNS) yang bekerja di Kantor Gubernur Propinsi Sulawesi Selatan, yang dilakukan langsung secara personal kepada responden terutama pada saat penyebaran kuisioner. Penelitian ini dilakukan pada setiap hari kerja.

c. Instumen pengumpul data
1. Data Primer
Data primer dalam penelitian ini diperoleh dari hasil survai wawancara, dalam hal ini berupa penyebaran kuisioner. Kriteria responden yang dipilih adalah sebagai berikut :
  •  Responden merupakan penduduk Kota Makassar atau bukan penduduk Kota Makassar tetapi melakukan aktifitas kerja selaku pegawai negeri sipil (PNS) di kantor Gubernur Propinsi Sulawesi Selatan.
  • Responden menggunakan angkutan pribadi sebagai sarana angkutan menuju ke tempat kerja
  • Mengisi kuisioner dengan lengkap.
  • Tanggapan responden atas konsistensi dan pengaruhnya akan diuji dengan sesuai logika Structural Equation Modeling (SEM) berbasis Partial Least Square (PLS)..
2. Data Sekunder
Data sekunder diperoleh dari :
  • Badan Kepegawaian Daerah (BKD) Popinsi Sulawesi Selatan berupa jumlah pegawai negeri sipil (PNS) yang bekerja di kantor Gubernur Propinsi Sulawesi Selatan.
  • Makassar dalam angka tahun 2014 di kantor BPS Kota Makassar.
d.   Teknik analisis data
Metode analisis data yang digunakan dalam penelitian ini secara umum terbagi 3 yaitu :
1.    Metode Statistik Deskriptif

     Metode ini dipergunakan untuk mengetahui preferensi angkutan pribadi perjalanan komuter dalam bentuk table dan diagram, sehingga mudah dipahami. Data yang dipergunakan dalam metode ini bersumber pada kuisioner yang dibagikan kepada sampel pengguna angkutan pribadi mobil dan motor dengan responden pegawai kantor Gubernur Propinsi Sulawesi Selatan. Adapun metode pengambilan sampel menggunakan Simple Random Sampling.

2.    Metode Deskriptif Kualitatif

     Metode ini dipergunakan untuk mengetahui persepsi transformasi moda pengguna angkutan pribadi berdasarkan preferensi angkutan pribadi perjalanan komuter dengan responden pegawai kantor Gubernur propinsi Sulawesi Selatan.

3.    Metode Model SEM PLS

     Metode ini digunakan untuk menganalisis pengaruh interaktif karakteristik perjalanan pereferensi angkutan pribadi, kualitas pelayanan angkutan umum terhadap transformasi moda.

3.  ANALISA DAN PEMBAHASAN

A. Analisis  Karakteristik Perjalanan, Kualitas Pelayanan Angkutan Umum, Preferensi Angkutan Pribadi dan Transformasi Moda

Keseluruhan variabel jarak dan waktu tempuh perjalanan komuter dalam menjalankan akitifitasnya sehari-hari dari rumah ke kantor , sebagian besar indikator yang mendapatkan respon pada skala 2 dan 3 yakni dengan menempuh jarak sepanjang 10 – 15 km sebesar 43.5 % dengan menggunakan waktu tempuh rata-rata selama 30 menit sampai dengan 1 jam sebesar 55.6%.
Berdasarkan kondisi objektif tersebut, dapat di asumsikan bahwa karkteristik perjalanan komuter bagi pengguna kendaraan pribadi dengan variabel indikator  jarak tempuh dan waktu tempuh diharapkan dapat mempengaruhi penentuan jenis moda transportasi yang cepat dan waktu yang efisien.

Tabel 1. Karakteristik variabel jarak dan waktu tempuh (JdW) dan variabel indikator (JdW1 dan JdW2)

No.
Indikator variabel
Distribusi Responden (%)
1
2
3
4
5
1
Jarak dan Waktu tempuh (JdW)
-
Jarak tempuh (JdW1)
3.3
22.9
43.5
21.9
8.5
-
Waktu tempuh (JdW2)
20.9
55.6
8.2
12.1
3.3
 Sumber Data: Pengolahan Data Primer.
                  Gambar 1. Distribusi Frekuensi Jawaban Variabel laten jarak tempuh (JdW1) dan
                                    Waktu tempuh (JdW2)

2)  Preferensi Angkutan Pribadi

Berdasarkan kondisi objektif pada tabel 2, dapat di asumsikan bahwa faktor Preferensi Angkutan Pribadi (PAP) komuter pada umumnya indikator yang mendapatkan respon pada skala 5 dan 4 dengan nilai rata-rata sebesar 85.0% menunujukan pentingnya indikator-indikator tersebut dari variabel-variabel yang terkait dengan kualitas pelayanan angkutan pribadi. Namun terdapat pula indikator  yang mendapatkan respon yang  lebih besar memilih  skala 3   yakni pada indikator budaya (image) yang mana responden menganggap penggunaan kendaraan pribadi dianggap kurang  bergengsi dan budaya (Image) dalam hal kedisiplinan berlalulintas dianggap kurang disiplin dengan nilai rata-rata sebesar 45.95%. Hal ini dimaknai bahwa faktor gengsi dan budaya berlalulintas dalam menggunakan moda angkutan pribadi komuter sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap Preferensi Angkutan Pribadi (PAP) komuter.

Tabel 2. Karakteristik Preferensi Angkutan Pribadi (PAP) dengan variabel  laten KSL,KAM,KNY,BDY)      
             dan  variabel indikator

No.
Indikator variabel
Distribusi responden (%)
1
2
3
4
5
1
 Keselamatan (KSL)
-     Jaminan keselamatan (KSL1)
1.6
10.5
13.1
43.8
31.0
-    Kelengkapan kendaraan (KSL2)
1.0
2.0
12.4
35.9
48.7
2
Keamanan (KAM)
-    Tindakan kriminal (KAM1)
1.0
2.6
10.5
41.8
44.1
-    Gangguan alam (KAM2)
0.0
1.0
10.8
30.4
57.8
3
Kenyamanan (KNY)
-    Kesesuaian tempat duduk (KNY1)
1.0
0.3
1.6
40.5
56.5
-    Keempukan tempat duduk (KNY2)
0.0
0.0
2.9
36.6
60.5
-    Sirkulasi udara (KNY3)
0.0
1.6
7.2
25.8
65.4
-    Perilaku mengemudi (KNY4)
0.0
3.9
5.6
34.0
56.5
-    Umur kendaraan (KNY5)
1.0
8.8
34.6
24.5
31.0
4
Budaya (BDY)
-    Gengsi (BDY1)
1.0
34.0
38.6
7.8
18.6
-    Kedisiplinan berlalulintas (BDY2)
0.0
15.0
53.3
15.7
16.0
                 Sumber Data: Pengolahan Data Primer

              Gambar 2. Karakteristik Preferensi Angkutan Pribadi (PAP) dengan  variabel laten dan  variable
                                   indikator.

4)    Kualitas Pelayanan Angkutan Umum

Berdasarkan kondisi objektif pada tabel 3, dapat di asumsikan bahwa persepsi responden pengguna angkutan mobil pribadi terhadap faktor kualitas pelayanan angkutan umum pada umumnya indikator yang mendapatkan respon pada skala 5 dan 4 dengan nilai rata-rata sebesar 72.2%, menunujukan bahwa pada umumnya indikator-indikator tersebut terkait penting dengan variabel kualitas pelayanan angkutan umum. Namun terdapat pula indikator  yang mendapatkan respon yang rendah  yakni pada indikator faktor gengsi dalam hal setuju sebesar 46.5%, sedangkan responden yang menganggap bahwa faktor gengsi  kurang mempengaruhi atau bahkan tidak memempengaruhi responden  dalam menggunakan moda angkutan umum yakni sebesar 53.2%. Hal ini dimaknai bahwa faktor gengsi dalam menggunakan moda angkutan umum sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap kualitas pelayanan angkutan umum.

Tabel 3. Karakteristik Variabel Laten Kualitas Pelayanan Angkutan Umum  (KPAU) dan Variabel
              Indikator

No.
Indikator variabel
Distribusi responden (%)
1
2
3
4
5
1
Kualitas pelayanan angkutan umum (KPAU))






-       Waktu tunggu (AKSES1)
0.7
4.2
2.6
30.4
62.1

-       Waktu perjalanan (MOBIL1)
0.0
1.3
5.6
31.4
61.7

-       Waktu akses (AKSES2)
0.0
3.3
7.5
35.6
53.6

-       Perilaku pengemudi (PLYN1)
0.0
0.0
11.1
31.4
57.5

-       Okupansi angkutan umum (AKSES3)
0.0
4.2
28.4
28.4
38.9

-       Kemungkinan duduk (AKSES4)
0.3
5.9
36.3
28.4
29.1

-       Total tarif (PLYN2)
0.0
7.8
41.5
32.4
18.3

-       Waktu berjalan kaki (MOBIL2)
0.0
7.8
32.4
34.3
25.5

-       Ganti kendaraan (AKSES5)
0.0
8.2
31.4
24.2
36.3

-       Tidak berdesakan (AKSES6)
0.0
9.8
37.6
28.4
24.2

-       Resiko keselamatan (PLYN3)
0.0
3.3
21.9
27.1
47.7

-       Faktor keamanan (PLYN4)
1.0
1.3
7.8
30.4
59.5

-       Faktor kenyamanan (PLYN5)
1.0
1.6
10.1
19.3
68.0

-       Faktor gengsi (PLYN6)
8.5
27.1
17.6
19.6
26.9
                   Sumber Data: Pengolahan Data Primer



Gambar 3. Distribusi Frekuensi Jawaban Variabel Laten Kualitas Pelayanan AU (KPAU) dan
                                        Indikator.

5)    Karakteristik Variabel Transformasi Moda Perjalanan (Y)

Berdasarkan kondisi objektif pada tabel 4, dapat di asumsikan bahwa persepsi responden pengguna angkutan mobil pribadi komuter terhadap faktor yang menjadi prioritas utama mempengaruhi bersedia pindah menggunakan moda angkutan umum dari ke sembilan indikator yang ada tidak terdapat yang indicator yang sangat dominan, namun ada tiga indicator yang tertinggi dipilih oleh responden adalah faktor waktu perjalanan sebesar 38.2% disusul dengan faktor waktu tunggu sebesar 16.7% dan faktor total tariff perjalanan sebesar 12.7% sehingga jika di totalkan mencapai 67.6% responden yang memilih masing-masing ke tiga faktor tesebut. Kondisi ini menunjukkan bahwa pengguna angkutan pribadi komuter lebih memprioritaskan faktor waktu perjalanan, waktu tunggu dan total tarif  untuk bersedia pindah ke moda angkutan umum  kemudian disusul faltor-faktor lainnya. Hal tersebut dapat dimaklumi bahwa para pegawai yang bekerja di instansi pemerintah sangat mengutamakan untuk dapat cepat sampai ketempat kerja kaitannya dengan tuntutan kedisiplinan pegawai   berdasarkan peraturan pemerintah yang ditetapkan kepada pegawai, selain hal itu juga menginginkan adanya biaya tarif moda angkutan umum yang murah menuju  ke tempat kerja.

Tabel 4. Karakteristik Variabel Laten Transformasi Moda Perjalanan (TRF) dan Variabel
              Indikator (TRF1 dan TRF2)

No.
Indikator variabel
Distribusi responden (%)
1
2
3
4
5
6
7
8
9
1
Transformasi moda










-  Kesediaan berpindah   
   ke angkutan umum
10.8
28.1
22.2
20.6
18.3
 
-  Faktor prioritas     
   utama berpindah ke AU
16.7
38.2
3.6
4.2
12.7
6.5
9.8
7.8
0.3
                    Sumber Data: Pengolahan Data Primer


      Gambar 4. Distribusi Frekuensi Jawaban Variabel Laten Transformasi moda perjalanan (TRF)
                                           dan Indikator (TRF1 dan TRF2).

B. Pengaruh Interaksi Perjalanan dan Perilaku Pengguna terhadap Transformasi Moda Angkutan Pribadi
a. Konseptual Model
Perancangan jalur hubungan prediktif (Path Diagram) menggambarkan pengaruh konstruk eksogen terhadap konstruk endogen. Sedangkan outer model menggambarkan hubungan antara variabel manifest dengan variabel laten seperti terlihat pada gambar 4 di sebelah halaman ini.

Gambar 5. Path Diagram Full Model



Keterangan :

Karakteristik Perjalanan Komuter (KPK)
-   Alokasi    Biaya Transportasi (ABT)
-   Jarak dan Waktu (JdW)

Kualitas   Pelayanan Angkutan Umum (KPAU)
-   Aksesibilitas (AKSES)
-   Mobilitas (MOBIL)
-   Pelayanan (PLYN)
Prefensi Angkutan Pribadi (PAP)
-   Keselamatan (KSL)
-   Keamanan (KAM)
-   Kenyamanan (KNY)
-   Budaya (BDY)

Transfornasi (TRF)

b. Evaluasi (Goodness of Fit)

1) Outer Model

                                               

Gambar 6. Output Outer Model Dropped Algoritma
Pada Gambar 6. terlihat nilai loading factor sebagian besar indikator hasil eksekusi model sudah di atas 0.50 (memenuhi kriteria validitas konvergen).
Pada table 6 terlihat bahwa nilai korelasi indicator terhadap variabelnya lebih besar dari pada nilai korelasi terhadap konstruk lainnya. Hasil ini menunjukkan validitas diskriminan yang baik.
Pada table 5.  Laten variabel correlation, validitas diskriminan konstruk untuk semua variabel yakni KarPerj, ABT, JdW, dan Transformasi telah terpenuhi,  karena memiliki nilai akar average variance extracted (AVE) sebesar 1.0 lebih besar dari nilai yang ada di bawahnya.

          Tabel 5. Tabel laten variabel correlation


ABT
Akses
Bdy
JdW
KPAU
KPK
Kam
Kny
Ksl
Mobilitas
PAP
Pelayanan
Trf
ABT
1.0












Aksesibilitas
0.2
1.0











BDY
0.3
0.0
1.0










JdW
0.2
0.2
0.1
1.0









KAM
-0.1
-0.2
0.2
0.2
1.0








KNY
-0.2
0.2
0.1
0.1
0.5
1.0







KPAU
0.3
0.9
0.0
0.2
0.0
0.3
1.0






KSL
-0.2
-0.3
0.4
0.0
0.5
0.3
-0.3
1.0






ABT
Akses
Bdy
JdW
KPAU
KPK
Kam
Kny
Ksl
Mobilitas
PAP
Pelayanan
Trf
KarPerj
0.7
0.3
0.3
0.8
0.0
0.0
0.3
-0.1
1.0




Mobilitas
0.2
0.4
0.1
0.1
0.2
0.2
0.6
-0.1
0.2
1.0



PAP
0.0
-0.1
0.6
0.2
0.8
0.6
0.0
0.8
0.1
0.2
1.0


Pelayanan
0.3
0.6
0.0
0.2
0.1
0.3
0.9
-0.3
0.3
0.5
0.0
1.0

Transformasi
0.3
-0.1
0.4
0.3
0.3
0.0
0.0
0.2
0.3
0.1
0.4
0.1
1.0
          Sumber Data: Pengolahan Data Primer.

Reliabilitas blok indikator yang mengukur konstruk dilihat dari nilai composite reliability. Composite reliability akan menunjukkan nilai yang baik jika di atas 0.70.

Tabel 6.  Hasil Uji Reabilitas

Konstruk
Composite Reliability
ABT
0.8
Aksesibilitas
0.8
BDY
0.8
JdW
0.8
KAM
0.8
KNY
0.8
KPAU
0.8
KSL
0.8
 KarPerj
0.7
Mobilitas
0.7
PAP
0.8
Pelayanan
0.8
Transformasi
1.0


2) Inner Model
    Evaluasi model structural (inner model) dilakukan dengan melihat nilai R-square, Q-square, dan T-test.

            Tabel 7. Nilai Koefisien Determinasi
 
Variabel
R Square
KPAU
0.993
KarPerj
1.000
PAP
0.988
Transformasi
0.260
                                                   Sumber Data: Pengolahan Data Primer.

Berdasarkan Tabel 7 Nilai R-square untuk variabel Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 0,993, berarti secara simultan dapat dijelaskan oleh variabel Kualitas Pelayanan Angkutan Umum (KPAU) dengan ke tiga variable faktor dalam model yaitu Aksesibilitas, Mobilitas dan Pelayanan. Untuk variabel Karakteristik Perjalanan Komuter (KarPerj) sebesar 1,000, berarti secara simultan dapat dijelaskan oleh variabel Karakteristik Perjalan Komuter (KarPerj) dengan ke-empat variabel faktor dalam model yaitu, Alokasi Biaya Transportasi (ABT), Jarak tempuh dan Waktu tempuh (JDW). Untuk variable Preferensi Angkutan Pribadi (PAP) sebesar 0.988, berarti secara simultan dapat dijelaskan oleh variabel Preferensi Angkutan Pribadi (PAP) dengan ke empat variabel faktor dalam model yaitu Keselamatan (KSL), Keamanan (KAM), Kenyamanan (KNY) dan Budaya (BDY). Kemudian untuk variabel Transformasi (TRF) sebesar 0.260. Jadi dengan demikian sebesar 0.7% untuk KPAU,  sebesar 0.0% untuk KarPerj, sebesar 1.2% untuk PAP dan 74% untuk Transformasi dijelaskan oleh faktor lain yang tidak diteliti dalam model ini. Dari hasil analisis didapatkan nilai Q²  untuk KPAU sebesar 0.9, KarPerj sebesar 1.0, PAP sebesar 0.9 dan untuk Transformasi sebesar 0.2. Hal ini menunjukkan bahwa model yang dihasilkan memiliki tingkat prediksi yang baik dan cukup baik . Dimana nilai Q² terbentang dari 0 s/d 1. Semakin mendekati 1 berarti predictive relevance semakin baik.


2. Pengujian Hipotesis
Pengujian hipotesis dilakukan dengan metode resampling bootstrapping yang didasarkan pada signifikansi koefisien jalur model struktural. Tingkat kepercayaan yang digunakan pada penelitian ini adalah 95% sehingga taraf signifkansi (α) = 5% atau 0.05 dengan nilai t-tabel 1.96.

Tabel 8. Hasil Uji Hipotesis
                           
     Hipotesis
T Statistics
P Values
Ket.
KPAU -> Transformasi
1.644
  0.101
H1 ditolak
KarPerj -> PAP
0.973
  0.331
H1 ditolak
KarPerj -> KPAU
1.471
  0.142
H1 ditolak
KarPerj -> Transformasi
5.461
  0.000
  H1 diterima
PAP -> KPAU
0.224
  0.823
H1 ditolak
PAP -> Transformasi
7.401
  0.000
  H1 diterima
                               Sumber Data: Pengolahan Data Primer.

Berdasarkan Tabel 8. terlihat bahwa tidak semua hipotesis  mempunyai nilai t-hitung lebih besar dari nilai t-tabel (1,96) yakni Kualitas Pelayanan Angkutan Umum (KPAU) terhadap Transformasi (TRF) sebesar 1,644, Karakteristik Perjalanan Komuter (KarPerj) terhadap Preferensi Angkutan Pribadi (PAP) sebesar 0,973, Karakteristik Perjalanan Komuter (KarPerj) terhadap Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 1.471, Preferensi Angkutan Pribadi (PAP) terhadap Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 0.224. Hal ini berarti hipotesis tersebut tidak signifikan berpengaruh langsung terhadap variabel endogen yang dituju. Kemudian ada 2 (dua) hipotesis yang diterima karena di atas 1,96 yakni Karakteristik Perjalanan Komuter (KarPerj) terhadap Transformasi (TRF) sebesar 5,461 dan Preferensi Angkutan Pribadi (PAP) terhadap Transformasi (TRF) sebesar 7,401. Hal ini berarti hipotesis tersebut signifikan berpengaruh langsung terhadap variabel endogen yang dituju.



4. KESIMPULAN

Berdasarkan hasil penelitian  dan analisis data  dalam  pembahasan, maka dapat disimpulkan sebagai berikut :

1. Hasil penelitian berdasarkan analisa data diketahui yakni karkteristik perjalanan komuter bagi pengguna kendaraan pribadi dengan variabel indikator  jarak tempuh dan waktu tempuh diharapkan dapat mempengaruhi penentuan jenis moda transportasi yang cepat dan waktu yang efisien. Faktor gengsi dan budaya berlalulintas dalam menggunakan moda angkutan pribadi komuter sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap Preferensi Angkutan Pribadi (PAP) komuter. Begitu pula halnya dengan persepsi responden pengguna angkutan pribadi komuter terhadap kualitas pelayanan angkutan umum bahwa faktor gengsi dalam menggunakan moda angkutan umum sebagian besar responden menganggap kurang memberikan dampak pengaruh yang besar terhadap kualitas pelayanan angkutan umum.

2. Persepsi responden terhadap Transformasi moda yang di dimanifestasikan dengan variabel indikator  dalam  hal kesediaan berpindah menggunakan moda angkutan umum responden lebih memprioritaskan faktor waktu perjalanan, waktu tunggu dan total tarif  untuk bersedia pindah ke moda angkutan umum  kemudian disusul faltor-faktor lainnya.

3. Berdasarkan hasil uji hipotesis ditemukan bahwa :

-   Kualitas Pelayanan Angkutan Umum (KPAU) tidak signifikan berpengaruh langsung terhadap variabel endogen Transformasi (TRF) sebesar 1.644.

-   Karakteristik Perjalanan Komuter (KaerPerj)) tidak signifikan berpengaruh langsung terhadap variabel endogen Preferensi Angkutan Pribadi (PAP) sebesar 0.973

-   Karakteristik Perjalanan Komuter (KaerPerj) tidak signifikan berpengaruh langsung terhadap variabel endogen Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 1.471.

-   Karakteristik Perjalanan Komuter (KarPerj) signifikan berpengaruh langsung terhadap variabel endogen Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 5.461.

-   Preferensi Angkutan Pribadi (PAP) tidak signifikan berpengaruh langsung terhadap variabel endogen Kualitas Pelayanan Angkutan Umum (KPAU) sebesar 0.224.

-   Preferensi Angkutan Pribadi (PAP) signifikan berpengaruh langsung terhadap variabel endogen Transformasi (TRF) sebesar 7.401.



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