Implementasi Metode K-Means Clustering Dengan Davies Bouldin Index Pada Analisis Faktor Penyebab Perceraian
Abstract
Basically, divorce is the release of the marital relationship between partners. In this country, the number of divorce cases has reached its peak in the last six years. Many reasons can lead to divorce, such as financial problems, leaving a partner, domestic violence, or polygamy. In this study, the K-Means clustering method, which is assisted by the Davies Bouldin index, shows an advantage in solving clustering problems. Rapid Studio software is used to process secondary data. The data were tested with the values k=3, k=5, and k=7. The results showed that the k=3 group obtained a value of -0.419, the k=5 group obtained a value of -0.423, and the k=7 group obtained a value of -0.337. Thus, it can be concluded that the K-Means clustering method using the Davies Bouldin index has a value of k=7, which is the best cluster compared to the values of k=3 and k=5. The following clusters were generated from research conducted on the K-Means method with a value of k = 7 using the Davies Bouldin Index: Cluster_0 consists of "Provinsi Jawa Barat", Cluster_1 consists of "Kota Tasikmalaya", Custer_2 consists of "Cirebon" and "Indramayu", Cluster_3 consists of "Tasikmalaya", "Kuningan" and "Subang", Cluster_4 consists of "Bogor", "Cianjur", "Sumedang"References
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[2] E. windarman; sapri; suryana, “Implementation of the Naïve Bayes Algorithm for Divorce Prediction at the Tais Religious Court Implementasi Algoritma Naïve Bayes Untuk Prediksi Perceraian Pada Pengadilan Agama Tais,†J. Komputer, Inf. dan Teknol., vol. 2, no. 2, pp. 501–510, 2022.
[3] E. F. Santika, “Kasus Perceraian di Indonesia Melonjak Lagi pada 2022, Tertinggi dalam Enam Tahun Terakhir,†2023. [Online]. Available: https://databoks.katadata.co.id/datapublish/2023/03/01/kasus-perceraian-di-indonesia-melonjak-lagi-pada-2022-tertinggi-dalam-enam-tahun-terakhir#:~:text=Menurut laporan Statistik Indonesia%2C jumlah,2021 yang mencapai 447.743 kasus. [Accessed: 01-Mar-2023].
[4] M. K. Yontem, K. Adem, Ilhan;, and S. T. Ve Kilicarslan, “Divorce Prediction Using Correlation Based Feature Selection and Artificial Neural Networks,†International Congress on Politic, Economic and Social, vol. 9, no. 1. pp. 259–273, 2019.
[5] N. Nurhayati, F. Azzahra, S. Ramadani, S. D. Hastuti, and E. Irawan, “Analisis Metode Klastering Pada Kasus Penyebab Perceraian Berdasarkan Provinsi Dengan Teknik K-Means,†KOMIK (Konferensi …, vol. 4, no. 1, pp. 278–284, 2020.
[6] Y. Sopyan, A. D. Lesmana, and C. Juliane, “Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan,†Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1464–1470, 2022.
[7] M. Irfan, W. Uriawan, O. T. Kurahman, M. A. Ramdhani, and I. A. Dahlia, “Comparison of Naive Bayes and K-Nearest Neighbor methods to predict divorce issues,†IOP Conf. Ser. Mater. Sci. Eng., vol. 434, no. 1, 2018.
[8] W. J. Andari and E. Buulolo, “Implementasi Algoritma C4.5 Mengetahui Penyebab Perceraian Dalam Pernikahan (Studi Kasus: Pengadilan Agama Medan Kelas I-A),†JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 3, p. 365, 2020.
[9] F. Amin, D. S. Anggraeni, and Q. Aini, “Penerapan Metode K-Means dalam Penjualan Produk Souq.Com,†Appl. Inf. Syst. Manag., vol. 5, no. 1, pp. 7–14, 2022.
[10] D. Ayu, M. Wati, D. Puspitasari, and E. Purwaningsih, “Metode Clustering Pada Model Algoritma K-Means Untuk Pemilihan Alat Kontrasepsi,†vol. 3, no. 2, pp. 129–138, 2019.
[11] E. Purwaningsih, “Analisis Kecelakaan Berlalu Lintas Di Kota Jakarta Dengan Menggunakan Metode K-Means,†JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, pp. 139–144, 2019.
[12] W. A. Eko, “Implementasi data mining dalam pengelompokan data peserta didik di sekolah untuk memprediksi calon penerima beasiswa dengan menggunakan algoritma k- means (studi kasus sman 16 bekasi),†vol. 21, no. 3, 2016.
[13] R. Tri Vulandari, Data Mining Teori dan Aplikasi Rapidminer, 1st ed. surakarta: GAVA MEDIA, 2017.
[14] W. Wu and M. Peng, “A Data Mining Approach Combining K-Means Clustering with Bagging Neural Network for Short-Term Wind Power Forecasting,†IEEE Internet Things J., vol. 4, no. 4, pp. 979–986, 2017.
Published
2023-06-29
How to Cite
PURWANINGSIH, Esty; NURELASARI, Ela.
Implementasi Metode K-Means Clustering Dengan Davies Bouldin Index Pada Analisis Faktor Penyebab Perceraian.
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management, [S.l.], v. 7, n. 2, p. 134-143, june 2023.
ISSN 2548-3331.
Available at: <https://460290.0x60nl4us.asia/index.php/IMBI/article/view/2307>. Date accessed: 01 dec. 2024.
doi: https://doi.org/10.51211/imbi.v7i2.2307.
Section
Articles
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