Applied Research in Science and Technology 5.
: 176Ae187 2025 Contents lists available at openscie.
E-ISSN: 2776-7205
Applied Research in Science and Technology DOI: 10.
33292/areste.
Journal homepage: https://areste.
org/index.
php/oai Evaluation and Ranking of Urban Drainage Systems Using SAW.
TOPSIS, and VIKOR Methods: A Case Study in Bantul Regency Muhamad Arifin1*.
Setya Winarno1.
Sri Kusumadewi2 Department of Civil Engineering.
Faculty of Civil Engineering and Planning.
Universitas Islam Indonesia.
Yogyakarta.
Indonesia Department of Informatics.
Faculty of Industrial Technology.
Universitas Islam Indonesia.
Yogyakarta.
Indonesia *Correspondence E-mail: nifira.
arkana@gmail.
ARTICLE INFO
ABSTRACT
Background: Urban flooding and waterlogging in Bantul Regency stem from inadequate drainage systems, exacerbated by rapid urbanization, land use changes, poor infrastructure planning, and intensified rainfall due to climate Therefore, an integrated risk management approach compassing both structural and non-structural solutionsAiis crucial for improving urban drainage Conversely, the comprehensive evaluation of drainage system performance continues to pose considerable challenges.
Assessments that Keywords:
concentrate solely on hydraulic or technical parameters while neglecting Drainage System, environmental, social, and economic factorsAioften result in suboptimal or Multi-Attribute Decision misdirected decisions.
As such, adopting a more integrative approach through Making, multi-criteria decision-making methods, such as Multi-Attribute Decision Making (MADM), emerges as a pertinent alternative.
Performance Evaluation.
Aims and Methods: The methods employed for MADM analysis in this study include the Simple Additive Weighting (SAW), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR).
Each of these approaches is designed to accommodate different data characteristics, levels of analytical complexity required, degrees of uncertainty involved, computational load, and the decision makerAos experience or expertise in applying the respective method.
Results: The analysis results indicate that, based on the SAW method, location A11 obtained the highest score .
, signifying the poorest drainage system performance and thus requiring top-priority intervention, whereas location A77 achieved the lowest score .
, indicating a well-functioning drainage Using TOPSIS, location A9 ranked first with a preference value (V.
7498, reflecting significant proximity to the ideal solution, while A6 recorded the lowest score .
Meanwhile, the VIKOR method identified location A99 as the top-ranked alternative with a VIKOR index of 16.
while A1 emerged as the lowest-ranked alternative with a VIKOR index of To cite this article: Arifin.
Winarno.
Kusumadewi.
Evaluation and ranking of urban drainage systems using saw, topsis, and vikor methods: A case study in Bantul Regency.
Applied Research in Science and Technology, 5.
, 176Ae187.
This article is under a Creative Commons Attribution-ShareAlike 4.
0 International (CC BY-SA 4.
License.
Creative Commons Attribution-ShareAlike 4.
0 International License Copyright A2025 by author/s Article History:
Received 3 July 2025 Revised 28 August 2025 Accepted 9 September 2025 Published 12 October 2025 Introduction Urban flooding and waterlogging have become pressing concerns in many parts of Indonesia, including Bantul Regency.
One of the key contributing factors is the inadequate performance of existing drainage systems, which fail to effectively manage water accumulation in several critical urban areas.
The challenges are further intensified by rapid urban expansion, shifts in land use, and insufficient planning and maintenance of drainage infrastructure (Hamdany & Saputra, 2.
As a result, these systems are unable to cope with the growing volume of stormwater runoff.
The combined effects of climate change and accelerating urbanization present significant obstacles to achieving sustainable urban planning and management (Rahma et al.
, 2.
In recent years, the occurrence of extreme rainfall events has significantly impacted numerous urban areas, leading to severe economic losses, injuries, and fatalities.
To effectively mitigate the adverse effects of flooding in the context of urban drainage management, it is essential to adopt a comprehensive risk management approach.
This approach should incorporate both structural and nonstructural measures aimed at prevention, mitigation, preparedness, response, and recovery from flood events (Arya & Kumar, 2.
Conversely, the comprehensive evaluation of drainage system performance continues to pose considerable challenges (Ahmad et al.
, 2.
Assessments that concentrate solely on hydraulic or technical parameters while neglecting environmental, social, and economic factorsAioften result in suboptimal or misdirected decisions.
As such, adopting a more integrative approach through multicriteria decision-making methods, such as Multi-Attribute Decision Making (MADM), emerges as a pertinent alternative.
This method facilitates the incorporation of diverse performance indicators, enabling more precise and evidence-based policy formulation in the management of urban drainage systems (Yang & Zhang, 2.
This study is intended to conduct a comprehensive assessment of the urban drainage system performance in Bantul Regency by employing the Multi-Attribute Decision Making (MADM) method.
This approach facilitates the identification of the current condition of the drainage infrastructure, enabling an objective evaluation of its performance and the determination of priority levels based on data-driven analysis.
The findings of this evaluation are expected to serve as a strategic basis for more effective decision-making in the planning and management of urban drainage infrastructure.
Several studies on drainage systems have primarily focused on technical aspects.
Although some research has addressed both technical and non-technical factors, it is generally limited to resilience in the context of disaster mitigation.
This study aims to evaluate and rank drainage infrastructure based on various factors related to condition and function, in order to identify poorly performing systems that require immediate intervention.
Methods 1 Research Location This research was conducted within the urban area of Bantul Regency, which includes the districts of Bantul.
Banguntapan.
Sewon, and Kasihan.
To support a comprehensive analysis, 100 drainage channel locations distributed across these four districts were purposively selected as representative sampling points.
The availability of sufficient sample data, along with accurate and well-distributed data across all locations, will enhance the accuracy and reliability of this research (Min & Tashiro.
Figure 1.
Research Location 2 Material and Tools The materials and tools used in this study included digital survey forms, measuring tapes.
GPS devices, smartphones, and digital cameras.
Data processing was carried out using data processing software such as Microsoft Excel, while modeling was conducted using SWMM (Storm Water Management Model.
3 Research Method The research employs a quantitative descriptive evaluative approach using the Multi-Attribute Decision Making (MADM) method to assess the performance of urban drainage systems in Bantul Regency.
This approach enables a comprehensive analysis of various technical and non-technical criteria within the drainage system.
Multi-Attribute Decision Making (MADM) plays a crucial role in supporting complex decision-making processes, particularly for policymakers.
This method enables them to systematically visualize various influencing factors, conduct objective measurements of each relevant criterion, and enhance accountability and transparency throughout every stage of the decisionmaking process.
Furthermore.
MADM assists in identifying and comparing multiple available decision alternatives, resulting in more targeted, data-driven policies that comprehensively consider multiple aspects (Axelsson et al.
, 2.
1 Data Collection Technique The data collection technique in this study was conducted systematically through two main sources of data: primary and secondary data, in order to obtain comprehensive and in-depth information regarding the performance of urban drainage systems within the study area.
Primary data were collected directly in the field using survey forms by observing the physical conditions of the drainage channels, such as the shape and dimensions of the channels, the presence of sediment or blockages, the extent of infrastructure damage, and the connectivity of the drainage network.
In addition, primary data were also obtained through interviews with relevant authorities, such as the Department of Public Works.
Spatial Planning.
Housing, and Settlement Areas (PUPKP) Bantul Regency, the Regional Disaster Management Agency (BPBD) Bantul Regency, and local community leaders, in order to gather information regarding policies on drainage system management and maintenance.
Furthermore, questionnaires were distributed to residents living in flood-prone areas to understand their perceptions, experiences, and levels of participation concerning the existing drainage system.
Secondary data were obtained from various official documents and written sources, including existing drainage network maps, historical rainfall data from the Regional Public Works.
Housing and Energy and Mineral Resources Office (PUPESDM) of the Special Region of Yogyakarta Province, flood and inundation incident records from the Regional Disaster Management Agency (BPBD), as well as technical planning documents such as the Master Plan for the Drainage System and the Regional Spatial Planning (RTRW) documents.
The collection of both primary and secondary data serves as a critical foundation for the analysis process using the Multi-Attribute Decision Making (MADM) method, as it enables more accurate weighting and assessment of each factor influencing the performance of the urban drainage system.
2 Data Analysis Technique The methods employed for MADM (Multi-Attribute Decision Makin.
analysis in this study include the Simple Additive Weighting (SAW), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR).
Each of these approaches is designed to accommodate different data characteristics, levels of analytical complexity required, degrees of uncertainty involved, computational load, and the decision makerAos experience or expertise in applying the respective method (Axelsson et al.
, 2.
The Simple Additive Weighting (SAW) method is employed due to its simplicity in calculating aggregate scores across multiple criteria.
This method facilitates policymakers in understanding and interpreting the performance evaluation results of the drainage system.
SAW is particularly effective in situations where the criteria have clearly defined weights and do not conflict with one another (Chen.
In the Simple Additive Weighting (SAW) method, the decision-making process involves summing the values of each alternative after they have been normalized and multiplied by the respective weights of each criterion.
The general formula to calculate the final score .
reference scor.
using the SAW method is as follows:
ycu ycOycn = Oc ycyc .
ycycnyc yc=1 With Vi is total score or final value of alternative i.
Wj is the weight of criterion j, rij is the normalized value of alternative i with respect to criterion j, and n is the total number of criteria.
Considering that each criterion can be classified as either a benefit .
here higher values are more desirabl.
or a cost .
here lower values are more desirabl.
, a normalization process must be conducted beforehand.
This ensures that all criterion values are converted to a uniform scale and can be compared fairly.
The normalization formulas for both benefit and cost criteria are as follows.
Normalization formula for benefit criteria:
ycycnyc = ycUycnyc ycAycaycu.
cUy.
Normalization formula for cost criteria:
ycycnyc = min.
cUyc ) ycUycnyc The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solutio.
method is employed in this study as a multi-criteria decision-making approach to evaluate and rank the performance of drainage channels.
This method was selected due to its intuitive logic, clarity of interpretation, and high computational efficiency (Bakhshipour et al.
, 2.
The calculation in the TOPSIS method requires the performance value of each alternative yaycn for each criterion yayc to be normalized first, using the following formula.
ycycnyc = ycuycnyc ocycuycn=1 ycuycnyc In the implementation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solutio.
method, the determination of the positive ideal solution (AA) and the negative ideal solution (AA) is based on the normalized weighted rating values .
enoted as yA).
These values result from the normalization and weighting processes applied to the initial decision matrix, ensuring a fair comparison among alternatives across all criteria.
The positive ideal solution (AA) represents the most optimal condition, which corresponds to the maximum value for benefit-type criteria and the minimum value for cost-type criteria.
In contrast, the negative ideal solution (AA) reflects the least desirable condition-defined by the minimum value for benefit-type criteria and the maximum value for cost-type The formulas used to determine AA and AA are as follows.
ycycnyc = ycyc ycycnyc ya = .
c1 , yc2 .
U , ycyco O.
yaOe = .
c1Oe, yc2Oe.
U , ycyco Where :
max ycycnyc .
ycyc = { ycn ycnyce yc ycnyc yca ycayceycuyceyceycnyc ycaycycycycnycaycycyce min ycycnyc .
if j is a cost attribute min ycycnyc .
if j is a benefit attributet max ycycnyc .
if j is a cost attribute ycn ycycOe = { ycn ycn The distance between alternative yaycn and the positive ideal solution is formulated as follows:
yco yaycn = oc.
cyc Oe ycycnyc ) .
yc=1 The distance between alternative yaycn and the negative ideal solution is formulated as follows:
yco yaycnOe = oc.
cycnyc Oe ycycO.
yc=1 The preference value for each alternative .
cOyc.
is determined as follows:
yaycnOe ycOycn = Oe yaycn yaycn The final result of this method indicates that a higher ycOycn value reflects a more preferred alternative yaycn .
The Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is employed to identify the optimal alternative by emphasizing a compromise solution that is closest to the ideal This method is particularly useful when no single alternative excels across all criteria VIKOR focuses on balancing group utility and individual regret, thereby facilitating the selection of the most suitable option in situations involving trade-offs among criteria.
This approach is highly relevant for decision-makers who must consider such trade-offs when formulating optimal urban drainage policies (Zhu et al.
, 2.
The first step in implementing the VIKOR method is to construct a decision matrix that represents the relationship between the selected alternatives and In this matrix, the element Xycnyc denotes the performance rating of alternative yaycn with respect to criterion yayc, derived from field measurements or expert assessments.
ycU11 ycU = [ ycU21 ycUyco1 ycU12 ycU22 ycUyco2 A ycU1ycu A ycU2ycu ] A ycUycoycu The normalization process is carried out by applying the following equation.
yceycnyc = ycuycnyc ocycuycn=1.
cuycnyc ) Identify the positive ideal point .
cej*) and the negative ideal point .
for each criterion using the following calculation:
yceycO = ycoycaycu.
ceycnyc ) For benefit criteria: { Oe yceyc = ycoycnycu.
ceycnyc ) yceycO = ycoycnycu.
ceycnyc ) For cost criteria: Oe yceyc = ycoycaycu.
ceycnyc ) The next step is to calculate the values of profitability (S) and regret (R).
The value of S reflects the cumulative relative distance of alternative yaA from the positive ideal point, while the value of R represents the maximum individual regret of alternative yaA in relation to the positive ideal point.
yco yceycO Oe yceycnyc ycIycn = Oc ycyc ( O yceyc Oe yceycOe yc=1 yceycO Oe yceycnyc ycIycn = ycoycaycu .
cyc ( O yceyc Oe yceycOe Calculate the VIKOR index (Q) for each alternative based on the predetermined formula as follows.
ycIycn Oe ycI O ycIycn Oe ycI O ycEycn = yc [ Oe Oe yc ycI Oe ycIO ycIOe Oe ycIO To determine the criteria that influence the performance of the urban drainage system, an analysis was conducted using the Consensus Method by involving respondents from various groups, including policymakers, water resources experts, and academics.
The Consensus Method is highly suitable for this study because the respondents represent a diverse and hierarchical group of decision-makers .
ulti-stakeholder.
There are ten criteria identified as influential to the performance of drainage channels, namely: .
channel capacity, .
type of drainage channel, .
channel material, .
catchment area size, .
topographical conditions, .
climate change, .
regulations and planning, .
cost, .
rainfall intensity, and .
channel condition (Arifin et al.
, 2.
Results and Discussions 1 Determination of Weight Values Using the Consensus Method This study utilizes three Multi-Attribute Decision-Making (MADM) methodsAiSimple Additive Weighting (SAW).
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)Aito evaluate the performance of the urban drainage system in Bantul Regency.
The analysis is conducted based on ten key criteria derived from a weighting process using the Consensus Method, which incorporates responses collected from stakeholders, including policymakers, technical experts, and academic professionals (Axelsson et , 2.
Through the distribution of questionnaires to respondents, weights were obtained that reflect the relative importance of each criterion in evaluating the performance of the urban drainage system.
The final results of this process indicate that the criterion Auchannel capacityAy received the highest weight, followed by Autype of channelAy and Auchannel material,Ay highlighting the technical urgency in assessing the systemAos effectiveness and efficiency.
This approach not only enhances the validity of the analytical outcomes but also ensures that the decisions made are grounded in both scientific and practical consensus.
The results of the normalized weight analysis using the Consensus Method are presented in the following table.
Table.
1 Results of ranking and weighting of criteria using the Consensus Method No.
Attribute Criteria Name Normalized Weight .
Category Channel Capacity (Dimensio.
Cost
Type of Channel
Benefit
Channel Material Benefit
Catchment Area
Benefit
Topographical Condition Factor
Cost
Climate Change Benefit
Planning and Regulation Benefit
Required Cost
Cost
Rainfall Benefit
C10
Channel Quality/Condition Benefit 2 Simple Additive Weighting (SAW) Method The Simple Additive Weighting (SAW) method is employed to assign an aggregate score to each alternative by summing the normalized and weighted values of all criteria.
Each drainage location alternative is evaluated based on its performance across ten criteria.
The results of the SAW method applied to 100 sample locations indicate that location A11 obtained the highest score of 0.
signifying that this site has the poorest drainage system performance and thus requires the most immediate intervention based on all criteria.
Conversely, location A77 recorded the lowest score of 313, suggesting that no improvements are necessary due to its satisfactory condition and The top 10 priority rankings based on the SAW method are presented in the following Table 2.
Results of priority analysis using the SAW method
Rank
Alternative Location Preference Score
A11
Drainage Dsn.
Gatak.
Rt.
03 (Jln.
Rukema.
Tamantirto.
Kasihan.
Bantul Drainage Dsn.
Sonosewu.
Rt.
Ngestiharjo.
Kasihan.
Bantul
A79
Drainage Dsn.
Karasan RT.
05 - 06
A68
Drainage Dsn.
Wiyoro (Jl.
Ngipi.
dari arah Barat
A85
Drainage Dsn.
Priyan
A12
Drainage Dsn.
Jadan.
Rt.
Tamantirto.
Kasihan A22 Drainage Jl.
Sonopakis.
Sonopakis Lor.
Ngestiharjo.
Kasihan A55 Drainage Dsn.
Kragilan A27 Drainage Dsn.
Wojo.
Rt.
04 (Utara Pengadilan Tinggi Samping Ringroad Selata.
A32 Drainage Dsn.
Ngireng-ngireng.
Rt.
07 (Jln.
Sewon Inda.
3 Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is utilized to assess the relative closeness of each alternative to the ideal positive solution while simultaneously distancing from the ideal negative solution.
The TOPSIS analysis results indicate that alternative A9 ranks first with a preference value (V.
75, reflecting a significant proximity to the ideal solution.
Meanwhile, location A6 recorded the minimum score of 0.
The top 10 priority rankings based on the TOPSIS method are presented in the following table.
Table 3.
Results of priority analysis using the TOPSIS method Rank Alternative Location Preference Score Sistem drainase Dsn.
Sonosewu.
Rt.
Ngestiharjo.
Kasihan.
Bantul
A12
Sistem drainase Dsn.
Jadan.
Rt.
Tamantirto.
Kasihan A13 Sistem drainase Dsn.
Sengotan.
Rt.
Ring Road Selata.
Tirtonirmolo.
Kasihan A11 Sistem drainase Dsn.
Gatak.
Rt.
03 (Jln.
Rukema.
Tamantirto.
Kasihan.
Bantul Sistem drainase Dongkelan Kauman (Jln prapanc.
Tirtonirmolo.
Kasihan.
Bantul Sistem drainase Ngebel.
Jl.
Puntadewa.
Tamantirto.
Kasihan.
Bantul
A40
Sistem Drainase Jl.
Yudhistira
A27
Sistem Drainase Dsn.
Wojo.
Rt.
04 (Utara Pengadilan Tinggi Samping Ringroad Selata.
A22 Sistem drainase Jl.
Ngestiharjo.
Kasihan Sonopakis.
Sonopakis Lor.
A21 Sistem drainase Jl.
Soragan (Rel keret.
Ngestiharjo.
Kasihan 4 VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) The VIKOR method is applied to evaluate alternatives using a compromise-based approach.
The aggregate utility value, maximum regret, and compromise index are calculated based on the positive and negative ideal values for each criterion.
With v = 0.
5 as the compromise weighting factor, the analysis results indicate that location A100 represents the best condition with a Q value of 0.
whereas A12 obtained the highest Q value of 16.
53, indicating the poorest performance.
The top 10 priority rankings based on the VIKOR method are presented in the following table.
Table 4.
Results of priority analysis using the VIKOR method
Rank
Alternative Location VIKOR Index
A99
Sistem Drainase Dsn.
Klodran
A98
Sistem Drainase Dsn.
Melikan Kidul
A100
Sistem Drainase Dsn.
Keyongan A96 Sistem Drainase Dsn.
Mandingan A97 Sistem Drainase Dsn.
Gandekan A78 Sistem Drainase Dsn.
Babadan (Jl.
Pemud.
A35 Sistem Drainase Dsn.
Glugo (Jl.
Ring Road Selatan/ sebelah Utara Ringroa.
A89 Sistem Drainase Dsn.
Code
A95
Sistem Drainase Dsn.
Mandingan A91 Sistem Drainase Dsn.
Bogoran The analysis results using the three methods indicate that the SAW and TOPSIS methods exhibit similar trends, whereas the VIKOR method produces significantly different outcomes.
This discrepancy arises from VIKORAos approach, which incorporates the concept of compromise and the imbalance among criteria, rather than relying solely on the aggregate scores or average distances.
comparison of the results from the three methods is presented in Figure 2.
Figure 2.
Comparison of Analysis Results Using the SAW.
VIKOR, and TOPSIS Methods Conclusions This study reveals that the application of SAW.
TOPSIS, and VIKOR methods yielded varying results in prioritizing urban drainage system improvements.
Based on the SAW method, location A11 obtained the highest score .
, signifying the poorest drainage system performance and thus requiring top-priority intervention, whereas location A77 achieved the lowest score .
, indicating a well-functioning drainage condition.
Using TOPSIS, location A9 ranked first with a preference value (V.
75, reflecting significant proximity to the ideal solution, while A6 recorded the lowest score .
Meanwhile, the VIKOR method identified location A99 as the top-ranked alternative with a VIKOR index of 16.
532, while A1 emerged as the lowest-ranked alternative with a VIKOR index of The SAW method identified A11.
A9, and A79 as the most critical locations, whereas the TOPSIS method highlighted A9.
A12, and A13.
In contrast, the VIKOR method produced significantly different priorities, namely A99.
A98, and A100.
While SAW and TOPSIS showed relatively similar preference rankings.
VIKOR demonstrated a distinct deviation.
These discrepancies suggest the need for further investigation by disaggregating each criterion into more detailed sub-criteria to enhance the objectivity and robustness of the decision-making outcomes.
Acknowledgment The author expresses profound appreciation to all individuals and institutions that provided support throughout the course of this research.
Special acknowledgment is extended to the local government, from the regency level to the sub-district level, for their invaluable assistance and cooperation in facilitating data collection and field activities essential to the successful completion of this study.
Authors Note The author declares that there are no conflicts of interest related to the publication of this article.
Furthermore, the author affirms that the manuscript is an original work, has not been published elsewhere, and is free from any form of plagiarism.
References