Pengelompokan Data Mining Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode Clustering (Studi Kasus : Kantor Desa Payabakung Hamparan Perak)
Abstract
Poverty is a problem that is often faced by various countries in the world, including Indonesia. In an effort to overcome poverty and increase people's access to food, in 2017 the Government gradually created a program that was formed to reduce the burden on the community in meeting basic needs, with the Non-Cash Food Assistance Program (BPNT). The problem is that the assistance provided has not been distributed on target / the distribution of assistance has not been objective, due to limited data and information obtained regarding families receiving BPNT assistance, so that families who should be entitled to receive assistance cannot receive assistance due to limited data available. Therefore, the village office is required to record again the families who are entitled to receive BPNT assistance with the existing criteria. The solution offered is to create a system of k-means that displays the clustering results of recipients of Non-Cash Food Assistance, by utilizing a number of data owned by the agency, it can be grouped using data mining technology. The benefit is that data mining can help agencies gain knowledge. by processing existing BPNT beneficiary data. The use of data mining techniques in grouping BPNT recipients is expected to be useful in facilitating the process of searching system data, which was previously still manual. The data group for recipients of Non-Cash Food Assistance (BPNT) in the work group (X) are private employees, for the income group (Y) are 1,400,001 – 1,700,000 and in the home status group (Z) are self-owned homes, and Centroid 2 ( 1,552,861.44), the data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Plantation, for the income group (Y) is 800,001 – 1,100,000 and in the house status group (Z) is Owned house, and Centroid3 (4,592,351.64) data group for recipients of Non-Cash Food Assistance (BPNT) in the occupation group (X) is Labor, for the income group (Y) is 500,001 – 800,000 and in the house status group (Z) is Rent house.
References
REFERENCES
K. Nisa, “Analisis Faktor-Faktor yang Mempengaruhi Tingkat Migrasi Tenaga Kerja Indonesia (TKI) (Studi Kasus Pada 6 Provinsi Tahun 2008-2017),” FEB UIN Syarif Hidayatullah, 2019.
L. E. Nainggolan, L. D. Sembiring, and N. T. Nainggolan, “Analisis Pengaruh Pertumbuhan Ekonomi terhadap Indeks Pembangunan Manusia yang Berdampak pada Kemiskinan di Provinsi Sumatera Utara,” Open J. Syst., vol. 15, no. 10, 2021.
T. J. Tan and D. Epriadi, “Evaluasi Pelaksanaan Bantuan Pangan Non Tunai di Kota Batam,” Progr. Stud. Adm. Negara, Univ. Puter. Batam, no. 1, 2021.
S. Laurentcia and R. Yusran, “Evaluasi Program Bantuan Pangan Non Tunai dalam Penanggulangan Kemiskinan di Kecamatan Nanggalo Kota Padang,” J. Civ. Educ., vol. 4, no. 1, 2021, doi: 10.24036/jce.v4i1.433.
M. Suryapuspita and J. Maâ€TMmuri, “IMPLEMENTASI KEBIJAKAN BANTUAN PANGAN NON TUNAI DI KECAMATAN DANUREJAN KOTA YOGYAKARTA,” Maj. Ilm. Din. Adm., vol. 17, no. 2, 2021, doi: 10.56681/da.v17i2.29.
D. Irvansyah and B. Setiawati, “Efektivitas Program Bantuan Pangan Non Tunai (BPNT) di Desa Simpung Layung Kecamatan Muara Uya Kabupaten Tabalong,” JAPB J. Mhs. Adm. Publik dan Adm. Bisnis, vol. 4, no. 2, 2021.
J. Karim, M. A. Puspa, and R. Kasim, “Sistem Pendukung Keputusan Penerima Bantuan Pangan Non Tunai Masyarakat Pada Kelurahan Dulalowo Timur Kota Gorontalo Menerapkan Metode Weight Aggregated Sum Product Assesment,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 4, 2021, doi: 10.30865/mib.v5i4.3269.
W. I. Akhyar, Gunawan, Haris Widiasmoro, and Layla Izza Rufaida, “Kebijakan Bantuan Pangan Non Tunai Dalam Perspektif Filsafat Hukum Murni,” Reformasi Huk., vol. 25, no. 1, 2021, doi: 10.46257/jrh.v25i1.189.
E. I. Syaripudin and M. T. Putri, “Kajian Kategori Penerima Bantuan Pangan Non Tunai (BPNT) dalam Perspektif Hukum Ekonomi Syari’ah,” J. JHESY, vol. 01, 2022.
R. Kurniawan, S. Suhada, and R. Dewi, “Penerapan Algoritma K-Means Clustering Dalam Persentase Merokok Pada Penduduk Umur Di Atas 15 Tahun Menurut Provinsi,” J. Sist. Komput. dan Inform., vol. 2, no. 2, 2021.
P. M. Silitonga Irene Sri, “Klusterisasi Pola Penyebaran Penyakit Pasien Berdasarkan Usia Pasien Dengan Menggunakan K-Means Clustering,” J. TIMES, vol. VI, no. Vol 6, No 2 (2017), 2017.
L. Y. Hutabarat, I. Gunawan, I. Purnamasari, M. Safii, and W. Saputra, “Penerapan Algoritma K-Means Dalam Pengelompokan Jumlah Penduduk Berdasarkan Kelurahan Di Kota Pematangsiantar,” J. Ilmu Komput. dan Teknol., vol. 2, no. 2, 2022, doi: 10.35960/ikomti.v2i2.704.
Widodo, “Analisa Data Mining Dalam Mengelompokan Data Menggunakan MATLAB,” Merdeka Penelit., vol. 4, no. 2, 2018, doi: 10.21009/pinter.4.2.5.
C. A. Sirait, J. H. Kandami, G. B. Aji, and A. Fadli, “Analisis Data Populasi Ayam Kampung di Wilayah Papua Barat Menggunakan Metode K-Means,” G-Tech J. Teknol. Terap., vol. 7, no. 1, 2023, doi: 10.33379/gtech.v7i1.1917.
Y. Syahra, I. Mariami, R. I. Ginting, R. Mahyuni, and A. Azlan, “Pelatihan Penggunaan Rapid Miner Untuk Pengelompokan Data Nilai Siswa SMK Raksana 2 Medan,” ABDIMAS IPTEK, vol. 3, no. 1, 2023, doi: 10.53513/abdi.v3i1.7452.
W. Romadhona, B. Indarmawan Nugroho, and A. Alim Murtopo, “Implementasi Data Mining Pemilihan Pelanggan Potensial Menggunakan Algoritma K-Means,” J. Minfo Polgan, vol. 11, no. 2, 2022, doi: 10.33395/jmp.v11i2.11797.
S. Budi, Data Mining Teknik Pemanfaatan Data untuk Keperluan Bisnis. 2007.
E. Prasetyo, Data Mining : Konsep Dan Aplikasi Menggunakan Matlab. 2013.
S. Butsianto and N. T. Mayangwulan, “Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering,” J. Nas. Komputasi dan Teknol. Inf., vol. 3, no. 3, 2020, doi: 10.32672/jnkti.v3i3.2428.
B. S. Praja, P. D. Kusuma, and C. Setianingsih, “Penerapan Metode K-Means Clustering Dalam Pengelompokan Data Penumpang Dan Kapal Angkutan Laut Di Indonesia,” e-Proceeding Eng., vol. 06, no. 1, 2019.
A. F. Ayutrisula and A. Fanani, “Customer Profiling dengan Menggunakan Metode K-Means Euclidean Distance di BPJS Ketenagakerjaan Tanjung Perak,” J. Mhs. Mat. Algebr., vol. 1, no. 1, 2020.
P. W. Kesuma, A. Risalah, and B. P. Purba, “Penerapan Data Mining Dalam Proses Pengelompokkan Data Masyarakat Kurang Mampu di Kota Deli Serdang Menggunakan Metode Clustering,” J. Masy. Inform. Sumatera Utara, vol. 10, no. 5, 2020, doi: 10.14710/jmasif.v7i1.10794.
S. Ramadani, I. Ambarita, and A. M. H. P. Pardede, “Metode K-Means Untuk Pengelompokan Masyarakat Miskin Dengan Menggunakan Jarak Kedekatan Manhattan City Dan Euclidean (Studi Kasus Kota Binjai),” Inf. Syst. Dev., vol. 4, no. 2, Jul. 2019.
R. K. Dinata, H. Akbar, and N. Hasdyna, “Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus,” Ilk. J. Ilm., vol. 12, no. 2, 2020, doi: 10.33096/ilkom.v12i2.539.104-111.
J. Hutagalung, D. Nofriansyah, and M. A. Syahdian, “Penerimaan Bantuan Pangan Non Tunai (BPNT) Menggunakan Metode ARAS,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 1, pp. 198–207, 2022.
Copyright (c) 2023 Fany Juliawati, , Relita Buaton, Rusmin Saragih

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain copyright and grant the EXPLORER right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) the work for any purpose, even commercially with an acknowledgement of the work's authorship and initial publication in EXPLORER.
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 acknowledgement of its initial publication in EXPLORER.
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 (See The Effect of Open Access).





.png)















