Penggunaan Metode K-Means Clustering: Pengelompokan Data Pemilih Tetap Di Kabupaten Bengkulu Tengah
DOI:
https://doi.org/10.53697/jkomitek.v5i2.2991Keywords:
Clustering, Usia, K-MeansAbstract
Penelitian ini membahas peran penting pemilihan umum (pemilu) dalam sistem perwakilan demokrasi Indonesia, di mana Komisi Pemilihan Umum (KPU) Kabupaten Bengkulu Tengah. Permasalahan dalam penelitian ini terkait dengan dalam mengelompokan usia pemilih yang dapat membantu jalannya sosialisasi pemilu Untuk mengatasi masalah ini, penelitian mengusulkan pengelompokan data pemilih tetap berdasarkan usia menggunakan metode data mining, khususnya clustering dengan metode k-means. Tujuan penelitian ialah pembentukan kelompok usia, memberikan informasi kepada KPU tentang variasi usia pemilih di wilayah tersebut, merencanakan strategi komunikasi yang lebih efektif, memahami distribusi usia pemilih seiring waktu, dan mengevaluasi proses k-means dalam pengelompokan data pemilih tetap. Dari hasil pengujian data pemilih tetap KPU Bengkulu Tengah memiliki hasil yiatu dari 10 kelurahan dengan 200 data pemilih tetap jumlah DPT usia remaja yaitu 53 orang, jumlah DPT usia dewasa yaitu 116 orang dan jumlah DPT usia lansia yaitu 31 orang. Dengan hasil pengujian sistem ini dapat membantu dalam pengelompokan data pemilih tetap berdasarkan usia.
References
Afiifah, K., Azzahra, Z. F., & Anggoro, A. D. (2022). Analisis Teknik Entity-Relationship Diagram dalam Perancangan Database Sebuah Literature Review. Intech, 3(2), 18–22. https://doi.org/10.54895/intech.v3i2.1682
Aneta, A., Podungge, A. W., Hunawa, R., & Nuna, M. (2021). The Difference of Political Participation of Inland Communities and Coastal Communities in Responding to Local Election: Synergy in Combating Covid-19 and Money Politic. Publik (Jurnal Ilmu Administrasi), 10(1), 169. https://doi.org/10.31314/pjia.10.1.169-180.2021
Anggraini, F., & Rahmatullah, S. (2024). Penerapan Data Mining Jumlah Penjualan Sepeda Motor Menggunakan Metode K- Means Clustering. 24(1), 42–51.
Anjana, R.M. (2020). Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: A data-driven cluster analysis: The INSPIRED study. BMJ Open Diabetes Research and Care, 8(1), ISSN 2052-4897, https://doi.org/10.1136/bmjdrc-2020-001506
Antar, S.A. (2023). Diabetes mellitus: Classification, mediators, and complications; A gate to identify potential targets for the development of new effective treatments. Biomedicine and Pharmacotherapy, 168, ISSN 0753-3322, https://doi.org/10.1016/j.biopha.2023.115734
Budiani, N. (2000). Data Flow Diagram: sebagai alat bantu desain sistem. Badan Pelayanan Kemudahan Ekspor Dan Pengolahan Data Keuangan Departemen Keuangan, April, 5–13.
Card, I., Rangka, D., Data, A., & Supriatna, A. (2014). Pembuatan Cetak Biru ( Blue Print ) Penomoran Penduduk Nasional Secara Elektronik ( E-National Keperluan Daftar Pemilih Tetap Pada Pemilu Di Indonesia Tahun 2014. 2009(semnasIF 2009), 1–9.
Columbia, B., & Va, C. (1992). Knowledge Discovery in Databases : An Attribute-Oriented Approach.
Czétány, L. (2021). Development of electricity consumption profiles of residential buildings based on smart meter data clustering. Energy and Buildings, 252, ISSN 0378-7788, https://doi.org/10.1016/j.enbuild.2021.111376
Dikarya, F., & Muharni, S. (2022). Penerapan Algoritma K-Means Clustering Untuk Pengelompokan Universitas Terbaik Di Dunia. Jurnal Informatika, 22(2), 124–131. https://doi.org/10.30873/ji.v22i2.3324
Egbueri, J.C. (2023). Extent of anthropogenic influence on groundwater quality and human health-related risks: an integrated assessment based on selected physicochemical characteristics. Geocarto International, 38(1), ISSN 1010-6049, https://doi.org/10.1080/10106049.2023.2210100
Evans, R.A. (2021). Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study. Lancet Respiratory Medicine, 9(11), 1275-1287, ISSN 2213-2600, https://doi.org/10.1016/S2213-2600(21)00383-0
Evans, R.A. (2022). Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study. Lancet Respiratory Medicine, 10(8), 761-775, ISSN 2213-2600, https://doi.org/10.1016/S2213-2600(22)00127-8
Gargot, T. (2020). Acquisition of handwriting in children with and without dysgraphia: A computational approach. Plos One, 15(9), ISSN 1932-6203, https://doi.org/10.1371/journal.pone.0237575
Gea, J. (2023). Implementasi Framework Flask Pada Modul Beta-App Pada Aplikasi Sistem Informasi Helpdesk (Sih) Studi Kasus Pt Xyz. Jurnal Informatika, 23(2), 243–258. https://doi.org/10.30873/ji.v23i2.3673
Grant, R.W. (2020). Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles. JAMA Network Open, 3(12), ISSN 2574-3805, https://doi.org/10.1001/jamanetworkopen.2020.29068
Hebbi, C. (2023). Comprehensive Dataset Building and Recognition of Isolated Handwritten Kannada Characters Using Machine Learning Models. Artificial Intelligence and Applications, 1(3), 163-174, ISSN 2811-0854, https://doi.org/10.47852/bonviewAIA3202624
Indrakumari, R. (2020). Heart Disease Prediction using Exploratory Data Analysis. Procedia Computer Science, 173, 130-139, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.06.017
Kalloo, G. (2020). Exposures to chemical mixtures during pregnancy and neonatal outcomes: The HOME study. Environment International, 134, ISSN 0160-4120, https://doi.org/10.1016/j.envint.2019.105219
Liang, S. (2020). Biotypes of major depressive disorder: Neuroimaging evidence from resting-state default mode network patterns. Neuroimage Clinical, 28, ISSN 2213-1582, https://doi.org/10.1016/j.nicl.2020.102514
Liu, C. (2021). A Hybrid Intrusion Detection System Based on Scalable K-Means+ Random Forest and Deep Learning. IEEE Access, 9, 75729-75740, ISSN 2169-3536, https://doi.org/10.1109/ACCESS.2021.3082147
Mahendra, M. D., & Eviyanti, A. (2022). Informatics and Business Institute Darmajaya SISTEM INFORMASI PENGGAJIAN BERBASIS WEBSITE (STUDY KASUS PT XYZ). Jurnal Informatika, 22(2), 111–123.
Responsivitas, P., Dalam, B., Publik, P., Kantor, P., Perikanandan, D., Kota, K., & Halimah, P. E. (2021). Jurnal Sosio Sains. 7(2), 108–115. https://doi.org/10.37541/sosiosains.v7i2.632
Seymour, C.W. (2019). Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis. JAMA Journal of the American Medical Association, 321(20), 2003-2017, ISSN 0098-7484, https://doi.org/10.1001/jama.2019.5791
Singh, H. (2024). Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan. Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 12(1), ISSN 2168-1163, https://doi.org/10.1080/21681163.2024.2378734
Sulistyowati, Ketherin, B. E., Arifiyanti, A. A., & Sodik, A. (2018). Analisa Segmentasi Konsumen Menggunakan Algoritma K-Means Clustering Jurusan Sistem Informasi , Institut Teknologi Adhi Tama Surabaya. March 2021.
Talakua, M. W., Leleury, Z. A., & Taluta, A. W. (2017). Analisis Cluster Dengan Menggunakan Metode K-Means Untuk Pengelompokkan Kabupaten/Kota Di Provinsi Maluku Berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2014. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, 11(2), 119–128. https://doi.org/10.30598/barekengvol11iss2pp119-128
Yang, J. (2020). Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city. Future Generation Computer Systems, 108, 976-986, ISSN 0167-739X, https://doi.org/10.1016/j.future.2017.12.012
Yassin, S.S. (2020). Road accident prediction and model interpretation using a hybrid K-means and random forest algorithm approach. SN Applied Sciences, 2(9), ISSN 2523-3971, https://doi.org/10.1007/s42452-020-3125-1
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Nagita Efprillia, Dedy Abdullah, Ardi Wijaya, Muntahanah
This work is licensed under a Creative Commons Attribution 4.0 International License.
