Application of Naïve Bayes Algorithm for Non-Cash Food Assistance Recipients in Kampar Regency

Abstract

Non- Non-Cash Food Assistance (BPNT) is a non-cash food social assistance from the government given to beneficiary families (KPM) of Rp. 200,000 per month which is given in the form of basic necessities by using an electronic card. The large number of residents who will be selected makes it difficult for village officials to make decisions on who is eligible or ineligible as recipients of non-cash food assistance every month. This research was conducted to design a decision support system for the eligibility of non-cash food assistance recipients by using the naïve bayes algorithm in order to help the Parit Baru Village apparatus every month in determining the eligibility of the next non-cash food assistance recipients, by perform Confusion Matrix calculations. From the results of the discussion carried out, it can be concluded that Naïve Bayes and the resulting rules had an accuracy rate of 95%, the Precision value was 94% and Recall was 100%. Therefore, the Naïve Bayes algorithm can be applied to the decision support system in determining the eligibility of recipients of non-cash food assistance.

Prediction of Stunting in Toddlers Combining the Naive Bayes Method and the C4.5 Algorithm

“…This method is very effective and efficient for large datasets, making it popular in the fields of natural language processing and pattern recognition (Supendar, Rusdiansyah, Suharyanti, & Tuslaela, 2023) (Siregar, Irmayani, & Sari, 2023) (Tanjung, Tampubolon, Panggabean, & Nandrawan, 2023). Naive Bayes is easy to implement and requires a smaller amount of training data to estimate parameters, so it is effective for applications that require fast responses (Madjid, Ratnawati, & Rahayudi, 2023) (Anam, Rahmiati, Paradila, Mardainis, & Machdalena, 2023). Although simple, this method can be very effective if the assumption of independence between features is precise enough, and even if this assumption is violated, its performance is often still quite good (Lubis & Chandra, 2023) (Saleh, Dharshinni, Perangin-Angin, Azmi, & Sarif, 2023).…”