KLASIFIKASI PENERIMA BANTUAN SKTM MENGGUNAKAN ALGORITMA NAIVE BAYES: STUDI KASUS DESA PESANGGRAHAN
DOI:
https://doi.org/10.69714/6w34wq73Keywords:
Classification, Data Mining, SKTM, Naive BayesAbstract
Implementation of the Naive Bayes algorithm for the classification of recipients of the Certificate of Inability to Pay (SKTM) assistance in Pesanggrahan Village. The classification process is carried out manually and using the RapidMiner application to validate the results. Manual calculations are carried out by calculating the probability of each attribute, such as occupation, age, income, marital status, vehicle, and asset ownership. The calculation results show that the probability for the "eligible" category is 0.097254, while the "uneligible" category has a probability of zero, so that the resident is classified as eligible to receive assistance. And, the calculation results using RapidMiner show results that are consistent with manual calculations. The Naive Bayes algorithm successfully classifies data with high accuracy, ensuring that assistance is more targeted to residents who meet the criteria. The implementation of this method provides an effective solution to overcome the problem of inaccurate distribution of assistance, increasing efficiency and transparency in decision-making by village officials. Thus, the Naive Bayes algorithm can be used as a tool in the process of determining recipients of assistance that is more objective and data-based.
References
R. K. (Ed.), Pelayanan Publik & Pemerintahan Digital Indonesia. Yayasan Pustaka Obor Indonesia, 2021.
A. Bunga Sari, H. Nurcahyanto, H. Lestari, and J. H. Soedarto, “KUALITAS PELAYANAN DALAM PEMBUATAN SURAT KETERANGAN TIDAK MAMPU (SKTM) PADA PROGRAM BANTUAN SOSIAL (BANSOS) Oleh,” J. Public Policy Manag. Rev., pp. 1–17, 2023, [Online]. Available: https://ejournal3.undip.ac.id/index.php/jppmr/article/view/43599
A. W. Zunan Setiawan, Muhammad Fajar, Arif Mudi Priyatno, Anggi Yhurinda Perdana Putri, Mediana Aryuni, Siti Yuliyanti, Harya Widiputra, Budanis Dwi Meilani, Rohmat Nur Ibrahim, Rezania Agramanisti Azdy, Satrio Junaidi, BUKU AJAR DATA MINING. 2023.
S. E. P. Rahayu Mayang Sari, Virdyra Tasril, Sri Wahyuni, Klasifikasi Forecasting Mengunakan Algoritma Naive Bayes. Serasi Media Teknologi, 2024.
M. Y. H. S. Dinda Ayu Pratiwi, Rolly Maulana Awangga, SELEKSI CALON KELULUSAN TEPAT WAKTU MAHASISWA TEKNIK INFORMATIKA MENGGUNAKAN METODE NAÏVE BAYES. Kreatif, 2020.
F. O. Dayera, Musa Bundaris Palungan, “G-Tech : Jurnal Teknologi Terapan,” G-Tech J. Teknol. Terap., vol. 8, no. 1, pp. 186–195, 2024, [Online]. Available: https://ejournal.uniramalang.ac.id/index.php/g-tech/article/view/1823/1229
R. Adolph, “KUALITAS PELAYANAN DALAM PEMBUATAN SURAT KETERANGAN TIDAK MAMPU (SKTM) PADA PROGRAM BANTUAN SOSIAL (BANSOS),” pp. 1–23, 2016.
A. Z. Macfud, A. P. Kusuma, and W. D. Puspitasari, “Analisis Algoritma Naive Bayes Classifier ( Nbc ),” vol. 7, no. 1, pp. 87–94, 2023.
M. Μ. Dr. Muhammad Ramdhan, S.Pd., Metode Penelitian. 2021.
F. M. Saputra Adiwijaya, Anugerah Tatema Harefa, Santi Isnaini, Syarifa Raehana, Budi Mardikawati, dr. Rudy Dwi Laksono, Saktisyahputra Saktisyahputra, Ramdani Purnamasari, Windi Susetyo Ningrum, Mayasari Mayasari, Nopita Sari, Buku Ajar Metode Penelitian Kualitatif. 2024.
D. Ig. Dodiet Aditya Setyawan, SKM., MPH.,, Ade Devriany, SKM., M.Kes., Nuril Huda, BUKU AJAR STATISTIKA. 2021.
M. C. I Made Agus Wirawan, S.Kom., Metode Penalaran dalam Kecerdasan Buatan. 2023.
