Classification of Generation By Population by Region in Indonesia Using K-Means Algorithm

Ririn Restu Aria(1*), Susi Susilowati(2),

(1) Universitas Bina Sarana
(2) Universitas Bina Sarana
(*) Corresponding Author

Abstract


Population growth caused by the year of birth led to the classification of population groups into several generations. Classification is important because in each generation there is based on population growth has different characteristics and traits in each generation. This research was conducted to try to group generations based on provinces in Indonesia based on the number of residents owned. When researchers analyzed the data obtained from population census data conducted by the central statistics agency (BPS). The method used in generation classification grouping uses the K-Means algorithm method based on 3 clusters. Based on the results of calculations carried out for 3 clusters obtained cluster 1 has 25 provinces, cluster 2 has 3 provinces and cluster 3 has 6 provinces. Based on the 2020 census that has been conducted, the current population is generation Z, generation and Pre Boomer generation is last in line so that from the available data can provide information about mapping in 34 provinces to be able to improve communication patterns between generations and fulfill public facilities that can be used every generation

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References


B. P. Statistik, “Sensus Penduduk 2020,” 2021. https://sensus.bps.go.id/topik/tabular/sp2020/85/175748/0.

M. Y. Rizki, S. Maysaroh, and A. P. Windarto, “Implementasi K-Means Clushtering dalam Mengelompokkan Minat Membaca Penduduk Menurut Wilayah,” Just IT J. Sist. Informasi, Teknol. Inf. dan Komput., vol. Vol. 11, N, pp. 41–49, 2021.

P. Marpaung and R. F. Siahaan, “Penerapan Algoritma K-Means Clustering Untuk Pemetaan Kepadatan Penduduk Berdasarkan Jumlah Penduduk Kota Meda,” J. Sains Komput. Inform., vol. Volume 5 N, pp. 503–521, 2021.

I. Ali, A. R. Dikananda, F. A. Ma’ruf, and M. Abdurohman, “Pengelompokan Jumlah Penduduk Berdasarkan Kategori Usia 0-18 Tahun dengan menggunakan Algoritma K-Means Untuk Menentukan Pengembangan Potensi desa Wisata diDes Wisata Di Kabupaten Cirebon,” J. Manaj. Inform., vol. Vol. 8 No., pp. 25–31, 2021.

L. Rahmawati, S. W. Sihwi, and E. Suryani, “Analisa Clustering Menggunakan Metode K-Means dan Hierarchical Clustering (Studi Kasus : Dokumen Skripsi Jurusan Kimia, FMIPA, Universitas Sebelas Maret),” ITSMART J. Teknol. dan Inf., vol. Vol.3 No.2, 2014.

E. Prasetyo, Data Mining : Konsep dan Aplikasi Menggunakan MATLAB. 2012.

M. Fauzi and Yudi, “Penerapan Algoritma K-Means Clustering untuk Mendeteksi Penyebaran penyakit TBC (Studi Kasus : Di Kabupaten Deli Serdang),” J. Tek. Inform. Kaputama (JTIK), vol. Vol 1 No 2, pp. 1–7, 2017.

F. L. Sibue and A. Sapta, “Pemetaan Siswa Berprestasi Menggunakan metode K-Means Clustering,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. Vol. IV No, pp. 85–92, 2017.




DOI: https://doi.org/10.30645/ijistech.v5i4.160

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