Implementasi Data Mining Kluster Pada Rumah Tangga Yang Memiliki Akses Hunian Layak Berdasarkan Provinsi
DOI:
https://doi.org/10.30865/klik.v1i5.182Keywords:
K-Means Clustering; Liveable and Affordable HouseholdsAbstract
The purpose of this study is to classify household residents who have access to decent housing that can be occupied by the community by province. In this study, the K-Means Clustering Algorithm is used, which is a method that partitions data into one or more clusters that have the same characteristics as each other based on the results obtained. Sources of data in this study were obtained from the website of the Central Statistics Agency (BPS) with the url address https://www.bps.go.id/. The data used in this study is data on the percentage of households that have access to decent and affordable housing according to provinces in 2015-2018, which consists of 34 provinces. The variable used is the average percentage of households that have access to decent and affordable housing by province. The data will be processed by dividing the clusters into 2 parts, namely clusters with low-level status and clusters with high-level status. It is hoped that the results of this study can provide input for the leadership regarding the policy of budget allocation in the APBN to be more effective in contributing to overcoming the problem of decent and affordable housing to live in.
Downloads
References
Butarbutar, N. et al. (2016) ‘Komparasi Kinerja Algoritma Fuzzy C-Means Dan K-Means Dalam Pengelompokan Data Siswa Berdasarkan Prestasi Nilaiakademik Siswa’, Jurasik (Jurnal Riset Sistem Informasi & Teknik Informatika), 1(1), pp. 46–55.
Dhuhita, W. M. P. (2015) ‘Clustering Menggunakan Metode K-Means Untuk Menentukan Status Gizi Balita’, Jurnal Informatika, 15(2), pp. 160–174.
Fatmawati, K. and Windarto, A. P. (2018) ‘Data Mining: Penerapan Rapidminer Dengan K-Means Cluster Pada Daerah Terjangkit Demam Berdarah Dengue (Dbd) Berdasarkan Provinsi’, CESS (Journal of Computer Engineering System and Science), 3(2), pp. 173–178.
Metisen, B. M. and Sari, H. L. (2015) ‘Analisis Clustering Menggunakan Metode K-Means Dalam Pengelompokkan Penjualan Produk Pada Swalayan Fadhila Benri’, Jurnal Media Infotama Vol., 11(2), pp. 110–118.
Nasari, F. and Darma, S. (2015) ‘Penerapan K-Means Clustering Pada Data Penerimaan Mahasiswa Baru (Studi Kasus?: Universitas Potensi Utama)’, Seminar Nasional Teknologi Informasi dan Multimedia 2015, 2(1), pp. 73–78.
Z, Z. A. and Sarjono (2016) ‘Analisis Data Mining Untuk Menentukan Kelompok Prioritas Penerima Bantuan Bedah Rumah Menggunakan Metode Clustering K-Means (Studi Kasus?: Kantor Kecamatan Bahar Utara)’, Jurnal Manajemen Sistem Informasi Vol, 1(2), pp. 159–170.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Data Mining Kluster Pada Rumah Tangga Yang Memiliki Akses Hunian Layak Berdasarkan Provinsi
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2021 Lisa Novia Ningsi, Poningsih Poningsih, Heru Satria Tambunan

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).















