Model Klasifikasi Penerima Bantuan Pangan Non-Tunai (BPNT) Menggunakan Algoritma Naïve Bayes Berbasis Data Sosial Ekonomi
Model Klasifikasi Penerima Bantuan Pangan Non-Tunai (BPNT) Menggunakan Algoritma Naïve Bayes Berbasis Data Sosial Ekonomi
DOI:
https://doi.org/10.22441/format.2026.v15.i2.001Abstrak
Program Bantuan Pangan Non-Tunai (BPNT) merupakan salah satu program pemerintah yang bertujuan meningkatkan kesejahteraan masyarakat melalui pemberian bantuan pangan kepada keluarga yang memenuhi kriteria tertentu. Permasalahan yang masih sering dihadapi dalam penyaluran BPNT adalah ketidaktepatan sasaran akibat proses seleksi penerima yang masih bergantung pada penilaian manual sehingga berpotensi menimbulkan kesalahan inklusi maupun eksklusi. Penelitian ini bertujuan menerapkan algoritma Naïve Bayes untuk menentukan kelayakan penerima BPNT berdasarkan data sosial ekonomi masyarakat. Data yang digunakan berasal dari basis data Percepatan Penghapusan Kemiskinan Ekstrem (P3KE) Kabupaten Rokan Hulu tahun 2024 sebanyak 2.055 data rumah tangga yang mencakup berbagai atribut demografi, sosial, dan ekonomi. Metode penelitian meliputi tahapan pengumpulan data, praproses data, pembentukan model klasifikasi menggunakan algoritma Naïve Bayes, serta evaluasi kinerja model menggunakan confusion matrix dan nilai akurasi. Hasil penelitian menunjukkan bahwa penerapan SMOTEN dan optimasi parameter pada algoritma Naïve Bayes mampu meningkatkan kemampuan model dalam mengenali kelas penerima BPNT yang memiliki jumlah data lebih sedikit. Peningkatan nilai recall dari 53,33% menjadi 60,00% menunjukkan bahwa model lebih sensitif dalam mengidentifikasi rumah tangga yang berpotensi layak menerima bantuan. Hasil penelitian menunjukkan bahwa model yang dihasilkan layak digunakan sebagai pendukung keputusan dalam proses identifikasi penerima BPNT berbasis data sosial ekonomi.
Unduhan
Referensi
[1] W. W. Arupandani, F. Taufik, and R. Mahyuni, “Implementasi Data Mining Menentukan Penerimaan Bantuan Sosial Pangan (BSP) Menggunakan Algoritma C4.5,” Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), vol. 2, no. 5, p. 705, 2023, doi: 10.53513/jursi.v2i5.5612.
[2] A. P. Siregar, D. Irmayani, and M. N. Sari, “Analysis of the Naïve Bayes Method for Determining Social Assistance Eligibility Public,” SinkrOn, vol. 8, no. 2, pp. 805–817, 2023, doi: 10.33395/sinkron.v8i2.12259.
[3] L. Dasar, P. Implementasi, and R. Daerah, “Risalah Kebijakan Optimalisasi Pemutakhiran Data Tunggal Sosial Ekonomi Nasional,” pp. 1–4, 2025.
[4] T. Srimurni and M. Sholihah, “Implementasi Program Bantuan Pangan Non Tunai (BPNT) Desa Duren Kecamatan Klakah Kabupaten Lumajang,” KNOWLEDGE: Jurnal Inovasi Hasil Penelitian dan Pengembangan, vol. 2, no. 4, pp. 309–316, 2022.
[5] E. Aiken, S. Bellue, D. Karlan, C. Udry, and J. E. Blumenstock, “Machine learning and phone data can improve targeting of humanitarian aid,” Nature, vol. 603, no. 7903, pp. 864–870, 2022, doi: 10.1038/s41586-022-04484-9.
[6] C. Pete, J. Julian, R. Randy, T. Thomas, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0: Step-by-step data mining guide,” CRISP-DM Consortium, p. 76, 2000.
[7] A. I. Perhan, I. Yustiana, and I. Sanjaya, “Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification,” Bit-Tech, vol. 8, no. 1, pp. 901–909, 2025, doi: 10.32877/bt.v8i1.2755.
[8] A. Hasibuan, “Penerapan Algoritma C4.5 Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” 2021.
[9] A. Putri, C. S. Hardiana, E. Novfuja, F. T. P. Siregar, Rahmaddeni, Y. Fatma, and R. Wahyuni, “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 1, pp. 20–26, 2023, doi: 10.57152/malcom.v3i1.610.
[10] M. Safrudin, Martanto, and U. Hayati, “Perbandingan Kinerja Naïve Bayes dan Support Vector Machine untuk Klasifikasi Sentimen Ulasan Game Genshin Impact,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, pp. 3182–3188, 2024, doi: 10.36040/jati.v8i3.8415.
[11] A. L. R. Entini and K. Handoko, “Implementasi Data Mining dengan Algoritma Naive Bayes,” Jurnal Comasie, vol. 3, pp. 343–351, 2023.
[12] X. Q. Liu, X. C. Wang, L. Tao, F. X. An, and G. R. Jiang, “Alleviating conditional independence assumption of Naive Bayes,” Statistical Papers, vol. 65, no. 5, pp. 2835–2863, 2024, doi: 10.1007/s00362-023-01474-5.
[13] A. Fatmawati, A. I. Purnamasari, and I. Ali, “Implementasi Naïve Bayes untuk Klasifikasi Kelayakan Penerima Bantuan Sosial,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 1, pp. 745–750, 2024, doi: 10.36040/jati.v8i1.8714.
[14] A. K. Saputra, W. Susanty, and A. S. Kurniawan, “Optimasi Naïve Bayes Classifier Menggunakan Particle Swarm Optimization pada Klasifikasi Data Penerima Bantuan Sosial,” EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi, vol. 14, no. 2, p. 80, 2024, doi: 10.36448/expert.v14i2.3951.
[15] Nilawati, “Perbandingan Tingkat Akurasi Metode Weighted Naïve Bayes dengan Random Forest dalam Mengklasifikasi Penerima Program Keluarga Harapan (PKH),” Program Studi Statistika Universitas Sulawesi Barat, 2024.
[16] F. Nugraha and A. Surahmat, “Sistem Otomatis Ringkasan Laporan Keuangan Berbasis PDF Menggunakan Metode NLP Transformer”, FORMAT, vol. 14, no. 2, pp. 238–244, Oct. 2025.
[17] Y. Yanuardi, F. F. Basri, F. F. Basri, M. L. Aksani, and M. L. Aksani, “Klasifikasi Kepribadian Berdasarkan Dimensi Ekstraversi Berbasis Data Mining Menggunakan Extremely Randomized Trees”, FORMAT, vol. 14, no. 2, pp. 229–237, Sep. 2025.
Unduhan
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Artikel ini berlisensi Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
