Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi
Proverty is one of the problems that inhibits national and regional growth. This research uses data mining techniques. In this study tha data used were sourced from the 2012-2018 statistical center. The research uses data mining techniques. In the data processing using k-means method. K-means method is a method of grouping existing data into several groups where the data in one group has the same characteristics with each other and has different characteristics from the data in other groups. The number of records used is 34 provinces which are divided into 2 clusters namely high and low clusters. The purpose of this study is divided into 2 parts, namely the provincial group with a high proverty rate and the provincial group with the lowest proverty level. From the result of grouping there were 8 provinces of high cluster and 26 low clusters. It is hoped that this research can provide input to the government so that it can give more attention to provinces that are categorized as high in proverty
N. Nurwati, “Kemiskinan : Model Pengukuran , Permasalahan dan Alternatif Kebijakan,” vol. 10, no. 1, pp. 1–11, 2008.
N. I. Febianto and N. D. Palasara, “Analisis Clustering K-Means Pada Data Informasi Kemiskinan Di Jawa Barat Tahun 2018,” vol. 08, no. September, pp. 130–140, 2019.
W. Nengsih, “Descriptive Modelling Menggunakan K-Means Untuk Pengclusteran Tingkat Kemiskinan Di Propinsi Riau,” no. January, 2017.
M. G. Sadewo, A. P. Windarto, and D. Hartama, “PENERAPAN DATAMINING PADA POPULASI DAGING AYAM RAS PEDAGING DI INDONESIA BERDASARKAN PROVINSI MENGGUNAKAN K-MEANS CLUSTERING,” InfoTekJar (Jurnal Nas. Inform. dan Teknol. Jaringan), vol. 2, no. 1, pp. 60–67, 2017.
M. Ridwan, H. Suyono, and M. Sarosa, “Penerapan Data Mining Untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier,” Eeccis, vol. 7, no. 1, pp. 59–64, 2013.
I. Parlina, A. P. Windarto, A. Wanto, and M. R. Lubis, “MEMANFAATKAN ALGORITMA K-MEANS DALAM MENENTUKAN PEGAWAI YANG LAYAK MENGIKUTI ASESSMENT CENTER UNTUK CLUSTERING PROGRAM SDP,” CESS (Journal Comput. Eng. Syst. Sci., vol. 3, no. 1, pp. 87–93, 2018.
R. W. Sari and D. Hartama, “Data Mining : Algoritma K-Means Pada Pengelompokkan Wisata Asing ke Indonesia Menurut Provinsi,” Semin. Nas. Sains Teknol. Inf., pp. 322–326, 2018.
E. Buulolo, Data Mining Untuk Perguruan Tinggi. Deepublish, 2020.
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