Penerapan Data Mining untuk Mengcluster Data Penduduk Miskin Menggunakan Algoritma K-Means di Dusun Bagik Endep Sukamulia Timur

Abstract

The occurrence of poverty in the community is caused by a condition of the economic inability of the head of the family to meet the primary / basic needs of his family, namely the need for clothing, food, shelter and education. The poor community itself can be found in almost every country, city and region, for example in one of the Bagik Endep hamlets of East Sukamulia Village. Based on these conditions, it is necessary to carry out clustering to assist the village government in grouping poor families, so that assistance can be distributed appropriately. By observing the above problems, Data Mining is needed to classify aid recipients using the K-Means method in clustering the poor. Where the K-Means Clustering Algorithm method aims to classify population data in the East Sukamulia region who are said to be classified as poor. The data used is data on the population of East Sukamulia in 2019, amounting to 200 data with 9 attributes, namely the name of the population, occupation, income / month, the number of children attending elementary school, the number of children attending junior high school, the number of children attending high school, the number of children attending college , the number of children who are not in school and the number of family members. Based on the results of tests carried out by applying the K-Means algorithm, the results obtained are Cluster 1 totaling 18 residents with the criteria of high economic population, Cluster 2 totaling 72 residents with moderate economic population criteria, and Cluster 3 totaling 110 residents with low economic population criteria. The K-Means method is expected to be able to assist the government of Sukamulia Timur Village in making decisions and finding the information needed to solve problems in recording the poor population accurately

Clustering Penduduk Miskin Untuk Penerima Bantuan Sosial Menggunakan Algoritma K-Means

“…Kajian-kajian terdahulu tersebut memberikan kontribusi penting dalam pemahaman yang lebih mendalam terkait isu yang sedang dibahas dalam penelitian ini. Seperti penelitian yang dilakukan oleh Suhartini dan Ria Yuliani [11] berjudul "Penerapan Data Mining untuk Mengcluster Data Penduduk Miskin Menggunakan Algoritma K-Means di Dusun Bagik Endep Sukamulia Timur" menggunakan data mining dengan algoritma K-means untuk mengelompokkan penduduk miskin di Sukamulia Timur ke dalam cluster . Mereka mengumpulkan data tahun 2019 sebanyak 200 entri data yang menampilkan sembilan atribut seperti nama penduduk, pekerjaan, pendapatan per bulan, jumlah anak bersekolah, jumlah anggota keluarga, dan masih banyak lagi.…”

Klasterisasi Data Jenis-Jenis Pekerjaan Penduduk Dengan Menggunakan Algoritma K-Means

“…Pekerjaan dapat mencakup berbagai jenis kegiatan, baik yang bersifat fisik maupun mental, dan melibatkan berbagai tingkat keterampilan,pekerjaan ini juga dipengaruhi oleh yaitu lapangan kerja dan padatnya penduduk [3]. Desa Babakan Mulya, Kecamatan Jalaksana, Kabupaten Kuningan merupakan salah satu daerah yang memiliki potensi sumber daya manusia yang perlu dianalisis lebih lanjut terkait dengan jenis pekerjaan yang ada di desa tersebut [4].…”

Analisis Segmentasi Pengunjung Mall Menggunakan Algoritma K-Means Clustering

“…Kesamaan didefinisikan dalam istilah bagaimana tutup benda-benda tersebut di angkasa. Algoritme K-means di salah satu sentroid paling populer algoritma berbasis (Kurniawan & Susanto, 2019); (Astuti, 2017). Misalkan kumpulan data, D, berisi n benda di luar angkasa.…”