Segmentasi Wilayah Bali dan Nusa Tenggara Berdasarkan Indikator Sosial-Ekonomi sebagai Dasar Strategi Pengembangan Bisnis Digital
DOI:
https://doi.org/10.61912/jeinsa.v5i1.444Kata Kunci:
Regional Segmentation,, Digital Business, K-means clustering, Human Development Index, Digital DivideAbstrak
Interregional socio-economic disparities in Indonesia present both obstacles and opportunities for the expansion of digital business. This research aims to perform regional segmentation based on the Human Development Index (HDI), unemployment rate, and poverty percentage in order to formulate targeted recommendations for digital business strategies. The analytical method employed is K-Means Clustering, applied to data from 41 regencies and municipalities across Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). The dataset was sourced from Statistics Indonesia (BPS) for the year 2025. The findings reveal four distinct clusters with unique characteristics. Cluster 1 (advanced regions) features an HDI exceeding 83 and a poverty rate below 4%, making it suitable for premium digital services and on-demand business models. Cluster 2 (developing regions) has an HDI between 75 and 80 and poverty under 5%, indicating strong potential for e-commerce and fintech platforms. Cluster 3 (transitional regions) demonstrates an HDI of 70–75 with a poverty rate of approximately 12%, aligning well with digital education and healthcare services. Cluster 4 (lagging regions) reports an HDI below 70 and poverty exceeding 22%, necessitating an inclusive strategy focused on digital services that address basic needs. In conclusion, socio-economically driven regional segmentation proves effective in designing digital business strategies that are responsive to the distinct profiles of each cluster.
Keywords : Regional Segmentation, Digital Business, K-means clustering, Human Development Index, Digital Divide
Referensi
Badan Pusat Statistik. (2024). Indeks Pembangunan Manusia 2024. Jakarta: Badan Pusat Statistik.
Badan Pusat Statistik. (2025). Statistik Indonesia 2025. Jakarta: Badan Pusat Statistik.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Massachusetts: Morgan Kaufmann.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). New York: Springer.
Indonesia.go.id. (2024). Perkembangan Ekonomi Digital Indonesia 2024. Portal Informasi Indonesia. https://indonesia.go.id
Kementerian Komunikasi dan Informatika. (2024). Status Literasi Digital Indonesia 2024. Jakarta: Kominfo.
Kotler, P., & Keller, K. L. (2021). Marketing Management (16th ed.). London: Pearson Education.
Putra, A. D., & Rahman, M. (2022). Digital transformation in higher education institutions: Challenges and opportunities in Indonesia. Journal of Educational Technology, 8(3), 112–125.
Putra, I. M. A. S., & Dewi, N. L. P. A. K. (2024). Pemetaan daerah tertinggal di Sulawesi menggunakan algoritma K-Means. Jurnal Ilmiah Teknologi Informasi, 12(2), 89-102.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
Sari, R. P., & Wijaya, A. (2023). Clustering kabupaten/kota di Jawa Timur berdasarkan indikator kemiskinan menggunakan K-Means. Jurnal Matematika dan Sains, 8(1), 45-58.
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