Penerapan Algoritma K-Means dalam Pengelompokan Keluarga Penerima Manfaat di Provinsi Lampung
DOI:
https://doi.org/10.55606/juisik.v6i2.2416Keywords:
Clustering, Data Mining, K-Means, KPM, RapidMinerAbstract
The distribution of social food assistance to Beneficiary Families (KPM) requires proper data management to ensure that assistance is delivered accurately and equitably. However, several challenges remain, including data inaccuracies and less effective data grouping processes. Based on these conditions, this study was conducted to cluster data on the number of beneficiary families and the allocation of social food assistance budgets across districts and cities in Lampung Province using the K-Means algorithm implemented through RapidMiner. The study utilized secondary data obtained from official publications of the Central Statistics Agency (BPS), covering 15 districts/cities with variables consisting of the number of beneficiaries and the amount of social assistance budgets allocated in each region. The results indicate that the data were grouped into three clusters with a satisfactory level of clustering quality. Three clusters were selected because they provide results that are easier to interpret. The clustering results reveal variations in the number of beneficiaries and the allocation of social assistance budgets across districts and cities, enabling a clearer analysis of regional characteristics and patterns of social assistance distribution.
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