Classification of Social Assistance Recipient Eligibility in Heram Urban Village Using the Naive Bayes Method

Authors

  • Helson Matuan Universitas Sepuluh Nopember Papua, Kota Jayapura, Papua, Indonesia
  • Heru Sutejo Universitas Sepuluh Nopember Papua, Kota Jayapura, Papua, Indonesia https://orcid.org/0009-0009-6836-5990

DOI:

https://doi.org/10.51903/h1g15q44

Keywords:

Naive Bayes, social assistance, classification, recipient eligibility, RapidMiner

Abstract

Determining the eligibility of social assistance recipients is an important stage in the government’s effort to distribute aid accurately to people who truly need it. In practice, however, the selection process often encounters problems, particularly inaccurate targeting caused by manual assessment. This study aims to implement the Naive Bayes algorithm to determine the eligibility of social assistance recipients in Heram Village. The dataset used in this study consisted of 358 records, divided into 251 training data and 107 testing data. Before the modelling process, the data were cleaned and selected so that only relevant variables were used in the classification process. The attributes applied in this study included gender, age, occupation, number of dependents, dependent category, priority category, monthly income, income category, housing condition, asset ownership, and employment status, with actual eligibility as the class label. The stages of data processing, modelling, and evaluation were carried out using RapidMiner software. The results showed that the Naive Bayes model achieved an accuracy of 85.05%, precision of 83.33%, recall of 86.54%, and AUC of 0.924, which is categorized as excellent classification. These findings indicate that the Naive Bayes algorithm is effective and feasible to be used as a decision-support method in determining social assistance recipient eligibility in a more objective, systematic, and measurable manner in Heram Village.

References

[1] I. Arfanda et al., “Naive Bayes dalam Menentukan Penerima Bantuan Langsung Tunai,” itscience-indexing.comI Arfanda, W Ramdhan, RA YusdaDigital Transformation Technology, 2021•itscience-indexing.com, Accessed: Apr. 07, 2026. [Online]. Available: https://itscience-indexing.com/jurnal/index.php/digitech/article/view/1091

[2] D. Setiawan et al., “Penerapan Metode Naive Bayes untuk Klasifikasi Penentuan Penerima Bantuan PKH,” proceeding.unpkediri.ac.idDA Setiawan, R Halilintar, LS WahyuniarProsiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 2021•proceeding.unpkediri.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/1137

[3] A. Musyafa, A. Januar, and E. Rilvani, “Penerapan Algoritma Naïve Bayes untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” Kohesi: Jurnal Sains dan Teknologi, vol. 10, no. 2, pp. 71–80, Aug. 2025, doi: 10.2238/EC3WFN06.

[4] D. W. Safitri et al., “Klasifikasi Kelayakan Penerima Bantuan Program Keluarga Harapan di Kecamatan Sinar Peninjauan Menggunakan Metode Naïve,” ejournal.catursakti.ac.idDW Safitri, E Fadihilah, S RahayuSimtek: jurnal sistem informasi dan teknik komputer, 2025•ejournal.catursakti.ac.id, vol. 10, no. 2, 2025, Accessed: Apr. 07, 2026. [Online]. Available: https://ejournal.catursakti.ac.id/index.php/simtek/article/view/1631

[5] Septia, Mega et al., “Penerapan Data Mining Klasifikasi Penerima Bantuan Pkh Di Desa Rambung Sialang Tengah Dengan Metode Naive Bayes,” journal.pelitanusantara.ac.idA SitohangJournal Data Science Penusa (JDSP), 2024•journal.pelitanusantara.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: http://journal.pelitanusantara.ac.id/index.php/jdsp/article/download/320/125

[6] T. Desi Nst, “Data Mining Kelayakan Penerima Bantuan Sembako di desa Bangun Purba Barat Menggunakan Metode Algoritma Naïve Bayes (Studi Kasus: Desa Bangun Purba Barat),” May 2024.

[7] A. Arifin, W. Handoko et al., “Implementasi Metode Naive Bayes untuk Klasifikasi Penerima Program Keluarga Harapan,” academia.edu, Accessed: Apr. 07, 2026. [Online]. Available: https://www.academia.edu/download/86645177/812.pdf

[8] A. Damuri, U. Riyanto, “Implementasi Data Mining dengan Algoritma Naïve Bayes untuk Klasifikasi Kelayakan Penerima Bantuan Sembako,” academia.edu, Accessed: Apr. 07, 2026. [Online]. Available: https://www.academia.edu/download/122710411/2403.pdf

[9] I. Hadianti, R. Astuti, “Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial,” ejournal.itn.ac.id, vol. 7, no. 6, 2023, Accessed: Apr. 07, 2026. [Online]. Available: https://www.ejournal.itn.ac.id/jati/article/view/6302

[10] N. Huda, M. Hasbi, T. Susyanto, “Naive Bayes dalam Menentukan Penerima Bantuan Langsung Tunai,” itscience-indexing.com, vol. 19, no. 1, p. 39, 2021, doi: 10.30646/sinus.v19i1.525.

[11] Pohan, Tiara, “Implementasi Algoritma Naïve Bayes untuk Klasifikasi Penerima Bantuan BPJS di Rantauprapat Labuhanbatu,” 2025, Accessed: Apr. 07, 2026. [Online]. Available: http://repository.ulb.ac.id/id/eprint/1827

[12] L. N. Darussalam, R. Kurniawan, Y. A. Wijaya, and T. Suprapti, “Algoritma Naive Bayes untuk Meningkatkan Model Klasifikasi Penerima Program Indonesia Pintar Di SDN 2 Purwawinangun,” journal.eng.unila.ac.id, vol. 13, no. 1, pp. 2830–7062, doi: 10.23960/jitet.v13i1.5882.

[13] D. Bienfield, M. Siahaan, E. Carolina Bagre, J. I. Wanda, G. Silahooy, and H. Sutejo, “Implementation of Naïve Bayes Algorithm on the Eligibility of Kartu Indonesia Pintar Scholarship (Case Study: University of Sepuluh Nopember Papua),” Jurnal Ilmiah Sistem Informasi, vol. 4, no. 1, pp. 191–204, Dec. 2025, doi: 10.51903/RDZDM469.

[14] M. Hofmann and R. Klinkenberg, “RapidMiner: Data Mining Use Cases and Business Analytics Applications,” 2016, Accessed: Apr. 15, 2026. [Online].

[15] V. Kotu and B. Deshpande, “Predictive Analytics and Data Mining: Concepts and Practice with Rapidminer,” 2014, Accessed: Apr. 15, 2026. [Online].:

[16] M. F. Nugraha and S. B. Rahayu, “Penerapan Naïve Bayes dalam Mengklasifikasi Calon Penerima Bantuan Pangan Non Tunai di Desa Nanjung Mekar,” INTERNAL (Information System Journal), vol. 5, no. 2, pp. 137–146, Dec. 2022, doi: 10.32627/INTERNAL.V5I2.634.

[17] L. Adzy, A. Asriyanik, A. P.-J. Mnemonic, “Algoritma Naïve Bayes untuk Klasifikasi Kelayakan Penerima Bantuan Iuran Jaminan Kesehatan Pemerintah Daerah Kabupaten Sukabumi,” ejournal.itn.ac.idLB Adzy, A Asriyanik, A PambudiJurnal Mnemonic, 2023•ejournal.itn.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: https://www.ejournal.itn.ac.id/index.php/mnemonic/article/view/5714

[18] A. Septiarini et al., “Penentuan Kelayakan Masyarakat Miskin Penerima Bantuan menggunakan Metode Naïve Bayes (Studi Kasus: Kabupaten Penajam Paser Utara),” ejournal.uin-suka.ac.idN Madia, A Septiarini, HR Hatta, H Hamdani, M WatiJISKA (Jurnal Informatika Sunan Kalijaga), 2023•ejournal.uin-suka.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: https://ejournal.uin-suka.ac.id/saintek/JISKA/article/view/3752

[19] J. S. Azizah et al., “Penerapan Metode Naive Bayes Klasifikasi Kelayakan Penerima Bantuan Pangan Non Tunai (BPNT),” ejournal.itn.ac.id, vol. 8, no. 1, 2025, Accessed: Apr. 05, 2026. [Online]. Available: https://ejournal.itn.ac.id/index.php/mnemonic/article/download/12778/7153

[20] F. Pratama et al., “Implementasi Metode Naïve Bayes dalam Mengklasifikasi Penerima Program Keluarga Harapan (PKH) Desa Minggiran Kediri,” proceeding.unpkediri.ac.id, Accessed: Apr. 05, 2026. [Online]. Available: https://proceeding.unpkediri.ac.id/index.php/inotek/article/view/1072

[21] G. Wibawa., “Penerapan Metode Naive Bayes untuk Identifikasi Penerima Bantuan Program Keluarga Harapan (PKH) di Kabupaten Buton Tengah,” jmks.uho.ac.idGNA WibawaJurnal Matematika, Komputasi, dan Statistika, 2025•jmks.uho.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: https://jmks.uho.ac.id/index.php/journal/article/view/147

[22] D. L.Simorangkir and R. N. Sari, “Penerapan Data Mining dengan Algoritma SMOTE dan Naive Bayes untuk Klasifikasi Kelayakan Penerimaan Bantuan Sosial pada Kantor Camat Medan Timur,” Prosiding Seminar Nasional Multi Disiplin Ilmu (SENADIMU), vol. 2, no. 1, pp. 801–813, Dec. 2025, Accessed: Apr. 07, 2026. [Online]. Available: https://senadimu.potensi-utama.org/index.php/home/article/view/624

[23] B. Khotimah, W. Kustina, and Y. Kustiyahningsih, “Implementation of the Naive Bayes and Simple Additive Weighting Methods in the Feasibility Selection of Village Cash Assistance Recipients,” 2023, Accessed: Apr. 07, 2026. [Online]. Available: https://www.academia.edu/download/103512674/3985.pdf

[24] N. Riyanah et al., “Penerapan Algoritma Naive Bayes untuk Klasifikasi Penerima Bantuan Surat Keterangan Tidak Mampu,” scholar.archive.orgN Riyanah, F FatmawatiJurnal Teknologi Informasi dan Multimedia, 2021•scholar.archive.org, vol. 2, no. 4, pp. 206–213, 2021, Accessed: Apr. 07, 2026. [Online]. Available: https://scholar.archive.org/work/zicdp2vfjzfctk6vdjz5ohxuwm/access/wayback/https://journal.sekawan-org.id/index.php/jtim/article/download/117/82

[25] V. Savitri, H. Setiawan, Z., “Classification Eligibility Recipient BPJS in Ward Sendang Sari Using the Naive Bayes Method,” ejournal.isha.or.id, Accessed: Apr. 07, 2026. [Online]. Available: https://www.ejournal.isha.or.id/index.php/Mandiri/article/view/405

[26] A. Siregar, D. Irmayani, M., “Application of Data Mining to Classify Receiving Social Assistance Using the Naïve Bayes Method,” jurnal.itsm.ac.id, Accessed: Apr. 07, 2026. [Online]. Available: https://jurnal.itsm.ac.id/index.php/inside/article/view/881

[27] H. Nurrifqi Fakhri Fikrillah, S. Hudawiguna, C. Juliane, “Klasifikasi Penerima Bansos Menggunakan Algoritma Naive Bayes,” JATISI, vol. 10, no. 1, pp. 683–695, Mar. 2023, doi: 10.35957/JATISI.V10I1.3624.

[28] A. Surahman and U. Hayati, “Implementasi Algoritma Naïve Bayes untuk Prediksi Penerima Bantuan Sosial,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 1, pp. 347–352, Mar. 2023, doi: 10.36040/JATI.V7I1.6302.

[29] L. Adzy, A. Asriyanik, “Algoritma Naïve Bayes untuk Klasifikasi Kelayakan Penerima Bantuan Iuran Jaminan Kesehatan Pemerintah Daerah Kabupaten Sukabumi,” ejournal.itn.ac.id, vol. 6, no. 1, 2023, Accessed: Apr. 05, 2026. [Online]. Available: https://www.ejournal.itn.ac.id/index.php/mnemonic/article/view/5714

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Published

2026-10-28

Issue

Vol. 5 No. 3 (2026): November

Section

Data Mining, Data Science, Database Systems, and Data Warehousing

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Copyright (c) 2026 Helson Matuan; Heru Sutejo

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