SIG DENGAN K-MEANS++ UNTUK KLASTERISASI PENGEMBANGAN UMKM KAIN TENUN (STUDI KASUS: KABUPATEN NAGEKEO) - Wulang, Maria Yasinta; Wibowo, Suryo Adi; Susanto, Eko Heri (Universitas Budi Luhur, 2026)
The woven cloth Small and Medium Enterprises (SMEs) in Nagekeo Regency possess significant economic and cultural potential; however, the current coaching process is executed uniformly without data-driven analysis, resulting in inefficient allocation of aid. This study aims to map the distribution of woven cloth SMEs, develop a web-based Geographic Information System (GIS) application, and implement the K-Means++ method to cluster the SMEs based on their productivity levels. The system was designed using Laravel and Leaflet.js, incorporating features for data management, interactive maps, and visualization of productivity clusters, which include Medium Productivity (PM), Low Productivity (PR), and Dense/Massive Productivity (PP). The research findings indicate that the system's clustering process achieved 100% accuracy compared to manual calculation using Excel, with a 0% error rate. A lift ratio of 7.69 (>1) signifies a strong relationship between variables and validates the clustering results. The algorithm's computation time was recorded at 0.464 seconds. Black-box and browser compatibility tests confirmed that all features functioned as intended across Chrome, Edge, and Firefox. Furthermore, user testing involving 10 respondents yielded a positive assessment, with percentages of 43% Strongly Agree, 41% Agree, 14.5% Neutral, and 1.5% Disagree. This system is capable of supporting more effective and objective spatial data-driven decision-making