PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI PENDUDUK MISKIN DI PROVINSI BANTEN

  • Frisma Handayanna (1*) Universitas Nusa Mandiri

  • (*) Corresponding Author
Keywords: Clustering, K-Means, Penduduk Miskin

Abstract

Abstract People with low incomes are unable to obtain education and other government services. The problem of poverty faced by the government is closely related to people with low incomes who cannot meet their basic needs. The Central Bureau of Statistics describes poverty as the inability to meet basic food and non-food needs as measured by expenditure. This study aims to classify Banten province based on poverty levels, by dividing the number of poor people into high, medium, and low categories. The K-Means clustering method is very fast and easy to use in the K-Means algorithm clustering process. Where the grouping results are formed, namely group one has a moderate number of poor people in three districts/cities, Pandeglang Regency, Lebak Regency, and Tangerang Regency. The second group has the lowest population in one district/city, namely Tangerang City. The third group has the highest number of poor people in the four districts/cities, namely Serang Regency, Cilegon Regency, Serang City, and South Tangerang City. The clustering results show that the Provincial Government of Banten will give priority and special attention to poverty alleviation efforts in the district/city. This will allow for increased revenues and earnings, as well as improved livelihoods and the economy in the area. the K-Means algorithm can classify the poor based on the number of people per district or city in Banten Province.

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Published
2023-08-09
How to Cite
Handayanna, F. (2023). PENERAPAN ALGORITMA K-MEANS UNTUK KLASTERISASI PENDUDUK MISKIN DI PROVINSI BANTEN. INTI Nusa Mandiri, 18(1), 93 - 99. https://doi.org/10.33480/inti.v18i1.4399
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