Pengembangan Model Decision Tree Menggunakan Particle Swarm Optimization untuk Klasifikasi Popularitas, Infrastruktur, dan Potensi Pasar Wilayah Jabodetabek
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Erlin Windia Ambarsari
*
Universitas Indraprasta PGRI, Jakarta,
Indonesia
- Nunu Kustian Universitas Indraprasta PGRI, Jakarta, Indonesia
- Putri Dina Mardika Universitas Indraprasta PGRI, Jakarta, Indonesia (*) Corresponding Author
Abstract
Traditional markets play a vital role in local economies; however, they face challenges related to competitiveness, infrastructure quality, and legal operational status. This study aims to develop a classification model for traditional markets in the Greater Jakarta (Jabodetabek) region based on three main aspects: popularity, infrastructure readiness, and market potential. The model utilizes a Decision Tree (DT) algorithm optimized with Particle Swarm Optimization (PSO) to enhance classification accuracy while maintaining model interpretability. The dataset comprises 1,253 market entries with 15 predictive features. The classification model categorizes markets into popular or unpopular, infrastructure-ready or not-ready, and potential or non-potential groups. Experimental results demonstrate that the model achieves an average accuracy of 97.48%. Key factors influencing the classification outcomes include the number of vendors, the availability of basic facilities (electricity, clean water, toilets, and drainage), the age of the market, and the presence of an official operating license (IUP2T). The findings provide valuable insights for local governments and policymakers to prioritize market revitalization efforts based on data-driven classification results. Furthermore, this study opens future research opportunities to integrate spatial data and real-time market analytics to improve classification accuracy further and support more adaptive and effective policy-making.
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