Application Of K-Means Clustering Algorithm On Population Growth In Simalungun Regency
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Published:
Sep 30, 2021
Keywords:
Data Mining, Social Problem, Population Growth, Clustering, K-means
Section
Computer Science
Statistics Article
Article View : 192 Times
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Abstract
Population growth is a condition when the population increases from previous years. Population growth has several variables, namely birth, death and migration rates. Positive population growth indicates an increase in population and vice versa. Population growth is caused by a high birth rate with a decrease in the death rate. The high rate of population growth and occurs in a fast period of time is what triggers a population explosion which is closely related to an increase in poverty, unemployment, crime, slum settlements, hunger and other social problems. An increase in the poverty rate occurs when high population growth is not matched by good economic growth accompanied by equitable distribution of income. An increase in unemployment occurs if the increase in population with reduced availability of adequate employment can lead to an increase in criminal cases. By knowing these problems, Data Mining is needed to classify aid receipts, build jobs. by using the K-Means method in clustering the population growth rate. The K-Means method can assist the Government in making decisions and the information needed to solve the problem of population growth and record all densely populated areas in an appropriate way.
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How to Cite
Rambe, M., Safii, M. ., & Irawan, I. (2021). Application Of K-Means Clustering Algorithm On Population Growth In Simalungun Regency. International Journal of Basic and Applied Science, 10(2), 61–69. https://doi.org/10.35335/ijobas.v10i2.55
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“Kemiskinan di Kabupaten Simalungun,” pp. 1–7, 2014.
M. Sangadji, “Analisis Faktor-Faktor Yang Mempengaruhi Kemiskinan Di Provinsi Maluku,” Media Trend, vol. 9, no. 2, pp. 162–180, 2014, [Online]. Available: https://docplayer.info/55399035-Analisis-faktor-faktor-yang-mempengaruhi-kemiskinan-di-provinsi-maluku-maryam-sangadji-universitas-pattimura-ambon-abstract.html.
M. Ikbal, “the Implementation of Discretion on Criminal Settlement in the Theft Cases,” IJCLS (Indonesian J. Crim. Law Stud., vol. 2, no. 1, pp. 90–101, 2017, doi: 10.15294/ijcls.v2i1.10818.
F. R. Pratiwi, “the Effect of Population Growth and Gross Regional Domestic Product ( Grdp ) on the Level of Unemployment in the City of Makassar,” vol. 3, no. 1, pp. 13–21, 2020.
B. P. Statistik, “Provinsi Sumatera Utara Dalam Angka Tahun 2020,” p. 368, 2020, [Online]. Available: https://sumut.bps.go.id/publication/2020/04/27/317f98717fcca50650c40477/provinsi-sumatera-utara-dalam-angka-2020.html.
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Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” J. Edik Inform., vol. 2, no. 2, pp. 213–219, 2017.
R. A. Asroni, “Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang,” Ilm. Semesta Tek., vol. 18, no. 1, pp. 76–82, 2015.
Y. D. Darmi and A. Setiawan, “Penerapan Metode Clustering K-Means Dalam Pengelompokan Penjualan Produk,” J. Media Infotama, vol. 12, no. 2, pp. 148–157, 2017, doi: 10.37676/jmi.v12i2.418.
M. Z. Hossain, M. N. Akhtar, R. B. Ahmad, and M. Rahman, “A dynamic K-means clustering for data mining,” Indones. J. Electr. Eng. Comput. Sci., vol. 13, no. 2, pp. 521–526, 2019, doi: 10.11591/ijeecs.v13.i2.pp521-526.
W. Dhuhita, “Clustering Menggunakan Metode K-Mean Untuk Menentukan Status Gizi Balita,” J. Inform. Darmajaya, vol. 15, no. 2, pp. 160–174, 2015.
W. Purba, S. Tamba, and J. Saragih, “The effect of mining data k-means clustering toward students profile model drop out potential,” J. Phys. Conf. Ser., vol. 1007, no. 1, 2018, doi: 10.1088/1742-6596/1007/1/012049.
E. M. Sipayung, H. Maharani, and B. A. Paskhadira, “Designing Customer Target Recommendation System Using K-Means Clustering Method,” IJITEE (International J. Inf. Technol. Electr. Eng., vol. 1, no. 1, 2017, doi: 10.22146/ijitee.25155.
A. F. Sallaby and E. Suryana, “Penerapan Data Mining untuk Menentukan Jumlah Pencari Kerja Terdaftar Berdasarkan Umur dan Pendidikan Menggunakan K-Means Clustering (Studi Kasus di Dinas Tenaga Kerja Dan Transmigrasi Provinsi Bengkulu),” J. Technopreneursh. Inf. Syst., vol. 1, no. 1, pp. 35–38, 2018, doi: 10.36085/jtis.v1i2.28.
U. B. Mulia, “Jumlah Ritel,” vol. XI, no. 1, pp. 32–44.
G. Abdillah et al., “Penerapan Data Mining Pemakaian Air Pelanggan Untuk Menentukan Klasifikasi Potensi Pemakaian Air Pelanggan Baru Di Pdam Tirta Raharja Menggunakan Algoritma K-Means,” Sentika 2016, vol. 2016, no. Sentika, pp. 18–19, 2016.
K. Simalungun, “RPI2JM Kabupaten Simalungun 2015 - 2019,” vol. 2, pp. 1–37, 2019.
S. K. Dini and A. Fauzan, “Clustering Provinces in Indonesia based on Community Welfare Indicators,” EKSAKTA J. Sci. Data Anal., vol. 1, no. 1, pp. 56–63, 2020, doi: 10.20885/eksakta.vol1.iss1.art9.
R. Oktavia, J. T. Hardinata, and I. Irawan, “Penerapan Metode Algoritma K-means Dalam Pengelompokan Angka Harapan Hidup Saat Lahir Menurut Provinsi,” Kesatria J. Penerapan …, vol. 1, no. 4, pp. 154–161, 2020, [Online]. Available: http://tunasbangsa.ac.id/pkm/index.php/kesatria/article/view/41.
S. Haryati, A. Sudarsono, and E. Suryana, “Implementasi Data Mining Untuk Memprediksi Masa Studi Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus: Universitas Dehasen Bengkulu),” J. Media Infotama, vol. 11, no. 2, pp. 130–138, 2015.
R. Nofitri and N. Irawati, “Analisis Data Hasil Keuntungan Menggunakan Software Rapidminer,” JURTEKSI (Jurnal Teknol. dan Sist. Informasi), vol. 5, no. 2, pp. 199–204, 2019, doi: 10.33330/jurteksi.v5i2.365.
Uska, M. Z., Wirasasmita, R. H., Usuluddin, U., & Arianti, B. D. D. (2020). Evaluation of Rapidminer-Aplication in Data Mining Learning using PeRSIVA Model. Edumatic: Jurnal Pendidikan Informatika, 4(2), 164-171.
M. A. W. K. MURTI, “Penerapan Metode K-Means Clustering Untuk Mengelompokan Potensi Produksi Buah – Buahan Di Provinsi Daerah Istimewa Yogyakarta,” Skripsi, 2017.
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