HYBRID DATA MINING METHODS TO SUPPORT MSME SUSTAINABILITY IN RURAL AND URBAN AREAS - Krisantus Jumarto Tey Seran; Debora Chrisinta; Yasinta Oktaviana Legu Rema; Hevi Herlina Ullu; Budiman Baso (JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 2026)
This study aims to analyze the factors influencing the sustainability of MSMEs in North Central Timor Regency by utilizing the K-Mode clustering method and Naive Bayes classification. The data used includes 550 MSMEs in North Central Timor Regency, East Nusa Tenggara Province, classified based on attributes such as location, product price, financial condition, innovation, technology utilization, and sustainability. The K-Mode method was employed to group MSMEs based on categorical similarities after the data was segmented by location attributes, while the Naive Bayes method was applied to classify MSME sustainability following clustering. The results indicate that in rural areas, MSMEs tend to dominate with high product prices and good financial conditions but show low levels of innovation and technology utilization. In contrast, MSMEs in urban areas are generally more innovative and technology-driven despite facing infra-structure challenges. The application of Naive Bayes demonstrated that a data training ratio of 70:30 yielded the best accuracy. Accordingly, the resulting model can be utilized to monitor the sustainability conditions of MSMEs. This study provides insights into sustainability patterns of MSMEs in both rural and urban areas and opens opportunities for further research on external factors affecting sustainability and barriers to technology adoption in rural areas.