Clustering of Regions in Lampung Province based on social and economic aspects using the K-Means algorithm with PCA optimization - Agustine, Verlina; Ashari, Ilham Firman; Permana, Raden Chrisna Trie Hadi (Universitas Muhammadiyah Yogyakarta, 2025)
Regional planning aims to create balanced and sustainable development by considering each region’s unique socio-economic characteristics. Lampung Province, as a resource-rich area in Sumatra, faces regional disparities and underdeveloped districts despite its potential. This study applies regional clustering to support development strategies by categorizing districts/cities based on social and economic factors. A quantitative machine learning approach is employed using the K-Means algorithm optimized with Principal Component Analysis (PCA) to enhance clustering accuracy. The research utilizes secondary data from 2019 to 2023, encompassing economic indicators such as Gross Regional Domestic Product (GRDP) and inflation rates, alongside social factors including the Human Development Index (HDI) and poverty rates. The results reveal two distinct clusters that is urban-centered districts with higher economic growth but greater income inequality, and rural-oriented districts with slower economic development yet relatively stable social conditions. This classification highlights the necessity for inclusive development policies tailored to regional characteristics, emphasizing investment in productive sectors, human resource development, and infrastructure improvement to bridge socio-economic disparities. The study underscores the importance of localized policy interventions to foster balanced regional development in Lampung Province.