Community Temporary Direct Assistance (BLSM) Decision Support System with the Profile Matching Method

Authors

  • Mita Ariffiani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Irfan Sudahri Damanik STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Ika Okta Kirana STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Primatua Sitompul STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.55123/jomlai.v2i1.1033

Keywords:

DSS, Direct Help, Public, Profile Matching, Gunung Bayu

Abstract

Community Temporary Direct Assistance (BLSM) is a Government Program. The process of assessing and making decisions in BLSM is usually subjective, especially if there are prospective BLSM recipients who have criteria that are not much different. The application made in this study is a Decision Support System for Community Temporary Direct Assistance (BLSM) in the Panguluh Nagori Gunung Bayu Office with the Profile Matching method. This application is used to assist in assessing the competence of prospective BLSM recipients and providing recommendations in decision making. The assessment criteria used include aspects of the condition of the house and economic aspects. This Profile Matching method will compare participant profiles with the ideal profile of prospective BLSM recipients. The smaller the gap, the greater the chance to pass the assessment. This system was built using the WEB programming language and MySQL as the database. It is hoped that the decision support system for receiving community temporary direct assistance (BLSM) at the Panguluh Nagori Gunung Bayu Office can assist the Village Head in determining potential beneficiaries who are entitled to be recommended for BLSM with a process of multi-criteria weighting and assessment that is faster, more accurate and more effective.

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Published

2023-03-31

How to Cite

Ariffiani, M., Damanik, I. S., Kirana, I. O., & Sitompul, P. (2023). Community Temporary Direct Assistance (BLSM) Decision Support System with the Profile Matching Method. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2(1), 75–82. https://doi.org/10.55123/jomlai.v2i1.1033

Issue

Vol. 2 No. 1 (2023): March

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Copyright (c) 2023 Mita Ariffiani, Irfan Sudahri Damanik, Ika Okta Kirana, Primatua Sitompul

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