Decision Support System for Determining Social Assistance Recipients in Petuaran Hilir Village Using the SMART Method

The distribution of social assistance in rural areas is a strategic government effort to reduce social inequality and improve the welfare of underprivileged communities. However, in Petuaran Hilir Village, the process of determining aid recipients is still conducted manually, leading to various issues such as a lack of objectivity, potential unfairness, and mistargeting. Therefore, this study aims to design and implement a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method to determine social assistance recipients in a more systematic and transparent manner. The SMART method was chosen due to its effectiveness in simplifying multi-criteria decision-making and its practicality for implementation at the village level. The system was developed as a web-based application and tested using the black-box method, as well as validated against the manual selection results conducted by village officials. Testing results showed that the system can objectively identify and rank aid recipients based on final scores from five main criteria: income, number of dependents, home ownership status, housing condition, and type of employment. The system achieved 100% consistency with manual selection results and reduced the selection process time by up to 70%, enabling a fairer and more targeted distribution of aid based on systematically calculated scores. By eliminating manual bias in the selection process, the system significantly improves the accuracy of recipient rankings. This study also opens opportunities for further development, such as integrating real-time population data and advanced analytical features to support more responsive social policies.

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Publication Details

Journal
J-INTECH
Published
2025-06-30
DOI
https://doi.org/10.32664/j-intech.v13i01.1945
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Decision Support System for Determining Social Assistance Recipients in Petuaran Hilir Village Using the SMART Method

Debi Yandra Niska, Azura Calista Sitorus, Syafira Istiara, Rahma Hidayanti
J-INTECH
Data Mining and Machine Learning Applications
article

Decision Support System for Determining Social Assistance Recipients in Petuaran Hilir Village Using the SMART Method

Debi Yandra Niska, Azura Calista Sitorus, Syafira Istiara, Rahma Hidayanti
article en

Abstract

The distribution of social assistance in rural areas is a strategic government effort to reduce social inequality and improve the welfare of underprivileged communities. However, in Petuaran Hilir Village, the process of determining aid recipients is still conducted manually, leading to various issues such as a lack of objectivity, potential unfairness, and mistargeting. Therefore, this study aims to design and implement a Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method to determine social assistance recipients in a more systematic and transparent manner. The SMART method was chosen due to its effectiveness in simplifying multi-criteria decision-making and its practicality for implementation at the village level. The system was developed as a web-based application and tested using the black-box method, as well as validated against the manual selection results conducted by village officials. Testing results showed that the system can objectively identify and rank aid recipients based on final scores from five main criteria: income, number of dependents, home ownership status, housing condition, and type of employment. The system achieved 100% consistency with manual selection results and reduced the selection process time by up to 70%, enabling a fairer and more targeted distribution of aid based on systematically calculated scores. By eliminating manual bias in the selection process, the system significantly improves the accuracy of recipient rankings. This study also opens opportunities for further development, such as integrating real-time population data and advanced analytical features to support more responsive social policies.

J-INTECHVol. 13(01)
State University of Medan (ID)
Openalex Percentile: Top 74%
Data Mining and Machine Learning Applications
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