Published: 2025-04-01

Penerapan Data Mining untuk Klasterisasi Data Anggaran Pendapatan dan Belanja Daerah Menggunakan Algoritma K-Means

DOI: 10.35870/jtik.v9i2.3425

Widianto, M. Rifqy Zakaria, Irvan
  • Widianto: Universitas Panca Sakti Bekasi , Affiliation name not available , Indonesia
  • M. Rifqy Zakaria: Universitas Panca Sakti Bekasi , Affiliation name not available , Indonesia
  • Irvan: Universitas Panca Sakti Bekasi , Affiliation name not available , Indonesia
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Abstract

To continue the development relay and fulfill the transition period until the simultaneous elections are held, it is necessary to prepare a Regional Development Plan (RPD) for regional heads whose terms of office end in 2022. The K-Means algorithm approach can be applied in analyzing the level of potential regional income and expenditure based on regional income and expenditure clusters that have results in the K-Means algorithm testing process, two clusters cluster 1 (C0) is a category of high spending potential consisting of and cluster 2 (C1) and is a low spending potential. The applied K-Means algorithm model has results that show a new insight, namely the grouping of regional income and expenditure budget data for the Tolikara Regency BPKAD based on 2 clusters has cluster results of 47 and 3. In analyzing the level of potential regional income and expenditure, the results of the test have centroid results 1 192973008, 16700000 and centroid results 2 7000000 and 225000000.

Keywords

Income ; Regional Expenditure ; Clustering ; Web based

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How to Cite

Widianto, Zakaria, M. R., & Irvan. (2025). Penerapan Data Mining untuk Klasterisasi Data Anggaran Pendapatan dan Belanja Daerah Menggunakan Algoritma K-Means. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 9(2), 622-630. https://doi.org/10.35870/jtik.v9i2.3425
Article Information

This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 9 No. 2 (2025)

  • Section: Computer & Communication Science

  • Published: %750 %e, %2025

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Author Biographies
Widianto
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Panca Sakti Bekasi, Kota Bekasi, Provinsi Jawa Barat, Indonesia.

M. Rifqy Zakaria
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Panca Sakti Bekasi, Kota Bekasi, Provinsi Jawa Barat, Indonesia.

Irvan
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Panca Sakti Bekasi, Kota Bekasi, Provinsi Jawa Barat, Indonesia.

References
  • Aljumah, A. A., Ahamad, M. G., & Siddiqui, M. K. (2013). Application of data mining: Diabetes health care in young and old patients. Journal of King Saud University-Computer and Information Sciences, 25(2), 127-136. https://doi.org/10.1016/J.Jksuci.2012.10.003
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Aris, F. (2019). Penerapan Data Mining untuk Identifikasi Penyakit Diabetes Melitus dengan Menggunakan Metode Klasifikasi. Router Research, 1(1), 1-6.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Darmi, Y. D., & Setiawan, A. (2016). Penerapan metode clustering k-means dalam pengelompokan penjualan produk. Jurnal Media Infotama, 12(2). https://doi.org/10.37676/jmi.v12i2.418.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Efendi, M. S., & Wibawa, H. A. (2018). Prediksi penyakit diabetes menggunakan algoritma ID3 dengan pemilihan atribut terbaik. JUITA: Jurnal Informatika, 6(1), 29-35.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Gaol, I. L. L., Sinurat, S., & Siagian, E. R. (2019). Implementasi Data Mining Dengan Metode Regresi Linear Berganda Untuk Memprediksi Data Persediaan Buku Pada Pt. Yudhistira Ghalia Indonesia Area Sumatera Utara. KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer), 3(1).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hamdani, R. (2020). Pengelompokkan Loyalitas Pelanggan Dengan Menggunakan Kombinasi Rfm Dan Algoritma K-Means. Journal Of Informatic Pelita Nusantara, 5(1), 7-13.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Hasanah, H., & Larasati, W. (2019). Pemanfaatan Data Mining untuk Mengelompokkan Kategori Penjualan Produk. Seminar Nasional GEOTIK 2019.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • INDONESIA, F. I. (2019). Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 6(2).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Jabat, J. T., & Murdani, M. (2019). Penerapan Data Mining Pada Penjualan Produk Retail Menggunakan Metode Clustering. Pelita Informatika: Informasi dan Informatika, 8(1), 26-32.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Khormarudin, A. N. (2016). Teknik data mining: Algoritma K-Means clustering. J. Ilmu Komput, 1-12.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Lestari, M. E. I. (2015). Penerapan algoritma klasifikasi Nearest Neighbor (K-NN) untuk mendeteksi penyakit jantung. Faktor Exacta, 7(4), 366-371. http://dx.doi.org/10.30998/faktorexacta.v7i4.290.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nurmasani, A., & Pristyanto, Y. (2021). Algoritme Stacking Untuk Klasifikasi Penyakit Jantung Pada Dataset Imbalanced Class. Pseudocode, 8(1), 21-26. https://doi.org/10.33369/pseudocode.8.1.21-26.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Panggabean, D. S. O., Buulolo, E., & Silalahi, N. (2020). Penerapan Data Mining Untuk Memprediksi Pemesanan Bibit Pohon Dengan Regresi Linear Berganda. JURIKOM (Jurnal Riset Komputer), 7(1), 56-62.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Putra, P. D., & Rini, D. P. (2019). Prediksi Penyakit Jantung dengan Algoritma Klasifikasi. Pros. Annu. Res. Semin, 5(1), 978-979.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Rizky, F., Syahra, Y., & Mariami, I. (2019). Implementasi Data Mining Untuk Memprediksi Target Pemakaian Stok Barang Menggunakan Metode Regresi Linier Berganda. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), 18(2), 167-175. https://doi.org/10.53513/jis.v18i2.156.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Rohman, A. (2016). Komparasi Metode Klasifikasi Data Mining Untuk Prediksi Penyakit Jantung. Neo Teknika, 2(2). https://doi.org/10.37760/Neoteknika.V2i2.766.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Siddik, M. A., Novamizanti, L., & RAMATRYANA, I. N. A. (2019). Deteksi level kolesterol melalui citra mata berbasis hog dan ann. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 7(2), 284. https://doi.org/10.26760/Elkomika.V7i2.284
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Sitohang, S. (2021). PENERAPAN DATA MINING DALAM PEMILIHAN LAPTOP MENGGUNAKAN METODE ALGORITMA C4. 5. Computer and Science Industrial Engineering (COMASIE), 5(5), 63-70.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Syaikhuddin, M. M., & Prihandoko, P. (2017). Penerapan Algoritma K-Means dan Cure Dalam Menganalisa Pola Perubahan Belanja Dari Retail ke E-Commerce. Energy: Jurnal Ilmiah Ilmu-Ilmu Teknik, 7(2), 44-49.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Wijaya, A. E., & Alfian, D. (2018). Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering. Jurnal Computech & Bisnis, 12(1), 11-27.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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