Verification of Covid-19 Social Assistance Recipients using Naïve Bayes Classifier


  • Ramzi Kamali Universitas Mercu Buana
  • Yunita S. Sari Universitas Mercu Buana
  • Ismat Aldmour
  • Rahmat Budiarto Universitas Mercu Buana



Covid-19 Pandemic, Social assistance, Data Classification, Naïve Bayes, K-NN


The Indonesian government launches the Covid-19 social assistance program to reduce the impacts of the economic downturn during the pandemic. The recipients of social assistance in Sukabumi Selatan District of Jakarta Province is collected form Neighborhood Association (RT/RW). However, this mechanism has limitations in terms of feasibility assessment through direct verification which is not optimal due to social restriction activities. At the same time, data is also collected through the regular recipients of social aid program, so there is a data discrepancy that causing a bias in determining the recipients’ feasibility. Therefore, a mechanism is required to assess the eligibility of the recipients. This study aims to assist Social Service Agency of Sukabumi Selatan district, in assessing the eligibility of the recipients using Naïve Bayes classifier and K-Nearest Neighbors (K-NN) classifier as comparison. Experiments using Cross-Industry Standard Process for Data Mining (CRISP-DM) model were carried out on a collected dataset, and the results show that Naïve Bayes classifier shows the best result with 93% accuracy, 86% precision and 100% recall, while K-NN has 90% accuracy, 82% precision and 98% recall. This research may assist the Social Service Agency of the district to determining more accurately the target recipients.

Author Biographies

Ramzi Kamali, Universitas Mercu Buana

Ramzi Kamali is currently a student at Dept. of Informatics,  Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia. He is interested in data mining, algorithm analysis and web programming. Current reseach focuses are machine learning and text classification.

Yunita S. Sari, Universitas Mercu Buana

YUNITA SARTIKA SARI completed Master’s Degree of Computer Science from Budi Luhur University, Indonesia. Currently, she is a Secretary at Department of Computer Science, Mercu Buana University, Indonesia. Her research interests include E-Business, Business Intelligence, and Software Engineering.

Ismat Aldmour

 ISMAT ALDMOUR received B.Sc. and M.Sc. degrees in Electrical Engineering/ Communications from the University of Jordan, Jordan, in 1985 and 1994 respectively, PhD in mobile wireless networks from the University of Glamorgan, UK, in 2008. Currently, he is an Associate professor at the Dept. of Computer Engineering and Science in Al-Baha University in Saudi Arabia. His research interests include mobile and wireless networks, IOT, Wireless sensor networks, and Eng. Education.




How to Cite

Kamali, R., Sari, Y. S., Aldmour, I., & Budiarto, R. . (2022). Verification of Covid-19 Social Assistance Recipients using Naïve Bayes Classifier. International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence, 1(2), 1–12.



Research Article