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Nova Novitasari
STMIK IKMI Cirebon
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Journal : Jurnal Informatika Terpadu

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Penerapan Algoritma K-Means untuk Clustering Data Jumlah Penduduk Miskin Berdasarkan Kota/Kabupaten di Jawabarat menggunakan Rapidminer Nova Novitasari; Nisa Dienwati Nuris; Ruli Herdiana
Jurnal Informatika Terpadu Vol 9 No 1 (2023): Maret, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v9i1.660

Abstract

Poverty is currently the only problem every region faces, as happened in the city/district of Responsibilities, which is still visible. Poverty is a condition where a person's standard of living cannot meet the basic needs of his life. According to information obtained by the West Java open data website, there are 513 data in West Java cities/regencies from 2002 to 2020. The number of poor people in many cities/regencies in West Java has increased. Thus, the method used in this study to group data using the K-means clustering method will be implemented in Rapidminer because the k-means algorithm is effective for analyzing large amounts of data. It can be seen from the data that will be generated that there are several variables needed, such as the Province code, Province name, City/Regency code, City/Regency name, number of poor people, unit, and year. From the variable data, it will be easier to run it. So that this study will produce several clusters, namely the results obtained by the researcher are cluster 4 because this cluster is the result taken from the smallest and best DBI value of several clusters that the researcher is testing. It is from the cluster results that data on the number of poor people will be seen by city/district which has an increasing level of collapse or by period each year.
Co-Authors Nisa Dieanwati Nuris Ruli Herdiana