PDSIPDSI

Bulletin of Informatics and Data ScienceBulletin of Informatics and Data Science

Poverty is a multidimensional problem that requires prompt and appropriate handling to maintain a dignified human life. In Manyaran Sub-district, Semarang City, the distribution of social assistance often faces obstacles due to limited human resources and a manual selection process for recipients. Therefore, a Decision Support System (DSS) is needed to assist the selection process in a more objective and efficient manner. This study aims to develop a DSS for determining social assistance recipients in Manyaran Sub-district by combining the Simple Multi-Attribute Rating Technique (SMART) and Analytical Hierarchy Process (AHP) methods. AHP is utilized to determine the weight of each criterion, while SMART is used to calculate the final score of each recipient candidate. The combination of SMART and AHP allows for both expert-based prioritization and quantitative evaluation, enhancing transparency and consistency in the selection process. The research was conducted through stages of problem analysis, data collection, literature review, system design, and report writing. The results show that among the ten analyzed candidates, the individual coded P06 achieved the highest final score of 0.574. The top five candidates with the highest scores were declared eligible to receive social assistance, while the others were declared ineligible. The application of the SMART and AHP methods in this DSS effectively improves the accuracy, objectivity, and efficiency of the selection process for social assistance recipients in Manyaran Sub-district.

The developed Decision Support System (DSS) utilizing SMART and AHP methods provides a structured and transparent approach to social assistance recipient selection in Manyaran Subdistrict.The system successfully identified the five most eligible households based on multidimensional poverty indicators, aligning with expert judgment.While demonstrating strong validity, the study acknowledges limitations in sample size and weighting methods, suggesting future research focus on expanding data scope, incorporating dynamic weighting, and developing a user-friendly interface for practical implementation.

Penelitian lanjutan dapat dilakukan dengan memperluas cakupan data untuk mencakup lebih banyak rumah tangga di berbagai wilayah, sehingga meningkatkan generalisasi model dan akurasi identifikasi penerima bantuan. Selain itu, pengembangan sistem dengan integrasi data real-time dari sumber-sumber lapangan, seperti data kesehatan atau pekerjaan, dapat meningkatkan responsivitas sistem terhadap perubahan kondisi sosio-ekonomi rumah tangga. Selanjutnya, eksplorasi penggunaan metode Machine Learning (ML) bersamaan dengan MCDM dapat meningkatkan kemampuan sistem dalam mengenali pola dan mengklasifikasikan kelayakan penerima bantuan secara lebih akurat, serta menguji perbandingan performa dengan metode MCDM lainnya untuk menemukan pendekatan yang paling optimal dalam konteks penyaluran bantuan sosial.

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