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Ahmad, Ahmad Gunawan
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Computer Science & IT Education Other

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KLASIFIKASI PENERIMA BANTUAN SKTM MENGGUNAKAN ALGORITMA NAIVE BAYES: STUDI KASUS DESA PESANGGRAHAN Ahmad, Ahmad Gunawan; Zaehol Fatah, Zaehol Fatah
Jurnal Riset Sistem Informasi Vol. 2 No. 1 (2025): Januari : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/6w34wq73

Abstract

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.
Co-Authors Zaehol Fatah, Zaehol Fatah