Clustering Analysis of Cocoa-Producing Areas Using the Gaussian Mixture Model Algorithm (A Case Study in Southeast Aceh Regency) - Pathia Pathia; Taufiq Taufiq; Sujacka Retno (ITEJ (Information Technology Engineering Journals), 2026)
This study aims to identify and cluster agricultural areas in Southeast Aceh Regency using the Gaussian Mixture Model (GMM) algorithm. The dataset consists of village-level agricultural data, including land area, production volume, productivity, and the number of farmers. To ensure comparability across variables, Z-Score normalization was applied. The optimal number of clusters was determined using the Bayesian Information Criterion (BIC), resulting in three distinct groups: high, medium, and low production areas. Clustering performance was evaluated using the Silhouette Score (0.3893) and the Davies-Bouldin Index (0.8548), indicating moderate clustering quality with reasonable separation between clusters. To improve accessibility and practical use, a web-based information system was developed to visualize agricultural data, clustering outcomes, and evaluation metrics interactively. These findings highlight the value of GMM-based machine learning in supporting data-driven decision-making and prioritizing agricultural development efforts by local governments.