Optimizing the Social Assistance Recipient Model in  CangkringVillage Using the Naïve Bayes Algorithm

Rotika; Nana Suarna; Irfan Ali; Dendy Indriya Efendi

doi:10.59934/jaiea.v4i2.849

Authors

Rotika STMIK IKMI Cirebon
Nana Suarna STMIK IKMI Cirebon
Irfan Ali STMIK IKMI Cirebon
Dendy Indriya Efendi STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.849

Keywords:

Naive Bayes algorithm, social assistance, data accuracy, data classification, decision making

Abstract

Social assistance is one of the methods used by the government to help the underprivileged. Cangkring Village is a village in Cirebon Regency that has inaccurate data on recipients of social assistance or underprivileged people. The Naive Bayes algorithm is one of the most effective techniques in machine learning for classifying data, in determining the eligibility of recipients of social assistance. The method works with a probabilistic approach to analyze data efficiently and accurately, can group data based on attributes and produce high accuracy. The problem in Cangkring Village, namely the accuracy of data on recipients of social assistance, is still a problem that requires special attention. This inaccuracy not only reduces the effectiveness of social assistance programs but also creates injustice for people in need. Invalid and inappropriate data causes the distribution of social assistance to be suboptimal. The purpose of this study is to optimize the accuracy model of social security recipients using the Naive Bayes algorithm, which can help improve the accuracy in determining eligible recipients.The method used in the study is secondary data processing taken from social assistance recipient data in Cangkring Village. This process includes data preprocessing stages, training and testing data distribution, and implementation or application of the Naive Bayes algorithm to perform classification. The results of the study show that the Naive Bayes algorithm is able to increase the accuracy of the classification of social assistance recipients with an accuracy rate of 90%, compared to the conventional method used previously. This study contributes to providing a more efficient and targeted method in selecting social assistance recipients, so that it can improve the social assistance distribution system in the future. Thus, the Naive Bayes algorithm can be an effective method for data-based decision making in the context of social policy.

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