A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the  Rural Problem

Muhammad Faisal; Titik Khawa Abd Rahman; Ida Mulyadi; Komang Aryasa; . Irmawati; Musdalifa Thamrin

doi:10.31181/dmame7220241023

Authors

Muhammad Faisal Department of Computer Science, STMIK Profesional, Makassar, Indonesia https://orcid.org/0000-0003-1469-9468
Titik Khawa Abd Rahman Department of Ph.D ICT, AeU University, Kuala Lumpur, Malaysia https://orcid.org/0000-0002-7174-7625
Ida Mulyadi Department of Computer Science, STMIK Profesional, Makassar, Indonesia https://orcid.org/0000-0002-5992-1686
Komang Aryasa Department of Informatics Engineering, DIPA University, Makassar, Indonesia https://orcid.org/0000-0001-8531-7805
. Irmawati Department of Computer and Network Engineering, Politeknik Negeri Ujung Pandang, Indonesia https://orcid.org/0009-0003-0483-1901
Musdalifa Thamrin Department of Informatics Management, STMIK Profesional, Makassar, Indonesia https://orcid.org/0009-0002-4484-4195

DOI:

https://doi.org/10.31181/dmame7220241023

Keywords:

Rural, Indexing, Clustering, Classification, Recommendation, Village Assistants

Abstract

Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy C-Means (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues.

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