Data Mining Klasifikasi Penduduk Penerima BST Menerapkan Metode K-Means
Keywords:
Classification; BST Recipient Population; K-Means Clustering Method; Rapid MinerAbstract
The Cash Social Assistance (BST) program is one of the government's efforts to provide assistance to community groups in need. In Medan Polonia District, BST distribution still faces challenges in efficiently registering and categorizing the recipient population. Therefore, this study aims to classify the BST recipient population using the K-means clustering method based on last education, monthly expenses, number of dependents and employment. This research method involves collecting data on the BST recipient population from the sub-district office and using the Rapid Miner application to carry out clustering analysis. The results of data grouping were identified into three clusters with different priority levels. Cluster 1 consists of residents with the highest priority level, while Cluster 2 and Cluster 3 have a lower priority level. So the names included in Cluster 1 consisting of Ahmad Surya, Joko Santoso, Eko Setiawan, Dewi Lestari, Agus Wijaya, Jaya Pratama, Rudi Hermawan and Andi Wijaya are considered as BST recipients with the highest priority.
References
E. Redy Susanto and A. Savitri Puspaningrum, “Rancang Bangun Rekomendasi Penerima Bantuan Sosial Berdasarkan Data Kesejahteraan Rakyat,” Tekno Kompak, vol. 15, no. 1, pp. 1–12, 2019.
D. N. Alfiansyah, V. R. S. Nastiti, and N. Hayatin, “Penerapan Metode K-Means pada Data Penduduk Miskin Per Kecamatan Kabupaten Blitar,” J. Repos., vol. 4, no. 1, pp. 49–58, 2022, doi: 10.22219/repositor.v4i1.1416.
A. P. Windarto, “Penerapan Datamining Pada Ekspor Buah-Buahan Menurut Negara Tujuan Menggunakan K-Means Clustering Method,” Techno.Com, vol. 16, no. 4, pp. 348–357, 2017, doi: 10.33633/tc.v16i4.1447.
N. Rofiqo, A. P. Windarto, and D. Hartama, “Penerapan Clustering Pada Penduduk Yang Mempunyai Keluhan Kesehatan Dengan Datamining K-Means,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 2, no. 1, pp. 216–223, 2018, doi: 10.30865/komik.v2i1.929.
S. Widaningsih, “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan Svm,” J. Tekno Insentif, vol. 13, no. 1, pp. 16–25, 2019, doi: 10.36787/jti.v13i1.78.
H. Maulidiya and A. Jananto, “Asosiasi Data Mining Menggunakan Algoritma Apriori dan FP-Growth sebagai Dasar Pertimbangan Penentuan Paket Sembako,” Proceeding SENDIU 2020, vol. 6, pp. 36–42, 2020.
A. S. L. T. T. H. Hafizah, “Data Mining Estimasi Biaya Produksi Ikan Kembung Rebus Dengan Regresi Linier Berganda,” J. Sist. Inf. Triguna Dharma (JURSI TGD), no. Vol 1, No 6 (2022): EDISI NOVEMBER 2022, pp. 888–897, 2022, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jsi/article/view/5732/1938
Y. L. Nainel, E. Buulolo, and I. Lubis, “Penerapan Data Mining Untuk Estimasi Penjualan Obat Berdasarkan Pengaruh Brand Image Dengan Algoritma Expectation Maximization (Studi Kasus: PT. Pyridam Farma Tbk),” JURIKOM (Jurnal Ris. Komputer), vol. 7, no. 2, p. 214, 2020, doi: 10.30865/jurikom.v7i2.2097.
M. Azhari, Z. Situmorang, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 640, 2021, doi: 10.30865/mib.v5i2.2937.
N. Salsabila, “Klasifikasi Barang Menggunakan Metode Clustering K-Means Dalam Penentuan Prediksi Stok Barang,” Cent. Libr. Maulana Malik Ibrahim State Islam. Univ. Malang, p. 89, 2018.
F. Harahap, “Perbandingan Algoritma K Means dan K Medoids Untuk Clustering Kelas Siswa Tunagrahita,” TIN Terap. Inform. Nusant., vol. 2, no. 4, pp. 191–197, 2021.
M. A. Rofiq, A. Qoiriah, S. Kom, and M. Kom, “Pengelompokan Kategori Buku Berdasarkan Judul Menggunakan Algoritma Agglomerative Hierarchical Clustering Dan K-Medoids,” J. Informatics Comput. Sci., vol. 2, no. 03, pp. 220–227, 2021.
B. Harli Trimulya Suandi As and L. Zahrotun, “PENERAPAN DATA MINING DALAM MENGELOMPOKKAN DATA RIWAYAT AKADEMIK SEBELUM KULIAH DAN DATA KELULUSAN MAHASISWA MENGGUNAKAN METODE AGGLOMERATIVE HIERARCHICAL CLUSTERING (Implementation Of Data Mining In Grouping Academic History Data Before Students And Stud,” J. Teknol. Informasi, Komput. dan Apl., vol. 3, no. 1, pp. 62–71, 2021, [Online]. Available: http://jtika.if.unram.ac.id/index.php/JTIKA/
P. Metode, K. U. Clustering, M. Berdasarkan, and N. Akademik, “PROGRAM STUDI TEKNOLOGI INFORMASI Fakultas Teknik Universitas Muhammadiyah Yogyakarta,” pp. 1–21, 2015.
M. A. K-means, “1 , 2 , 3 1,” vol. 1, no. 2, pp. 161–166, 2021.
W. Purba, W. Siawin, and . H., “Implementasi Data Mining Untuk Pengelompokkan Dan Prediksi Karyawan Yang Berpotensi Phk Dengan Algoritma K-Means Clustering,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 2, no. 2, pp. 85–90, 2019, doi: 10.34012/jusikom.v2i2.429.
. F., F. T. Kesuma, and S. P. Tamba, “Penerapan Data Mining Untuk Menentukan Penjualan Sparepart Toyota Dengan Metode K-Means Clustering,” J. Sist. Inf. dan Ilmu Komput. Prima(JUSIKOM PRIMA), vol. 2, no. 2, pp. 67–72, 2020, doi: 10.34012/jusikom.v2i2.376.
S. A. Rahmah, “KLASTERISASI POLA PENJUALAN PESTISIDA MENGGUNAKAN METODE K-MEANS CLUSTERING ( STUDI KASUS DI TOKO JUANDA TANI KECAMATAN HUTABAYU RAJA ),” vol. 1, no. 1, pp. 1–5, 2020.
E. Rouza, Basorudin, and Efrida, “Identifikasi dan Klasifikasi UMKM di Kabupaten Rokan Hulu Menggunakan Metode K-Means,” J. Ilm. Univ. Pengaraian, vol. 7, no. 01, pp. 32–40, 2021.
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