Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees

Keywords: Accuracy, confusion matrix, decision trees, naïve bayes, test size


The employee performance assessment at the Palembang City Fire and Disaster Management Service (DPKPB) is applied to other than the employee performance assessment implementation team based on the Decree of the Head of the Palembang City DPKPB Number 146 of 2021 concerning the employee performance assessment implementation team and awards for exemplary employees. Subjective assessments are avoided to obtain assessment results that are by the achievements of each employee. The application of data mining can be an alternative to avoid subjectivity in performance assessment. In this research, a comparison of the Naive Bayes and Decision Tree algorithms was carried out to assess the performance of Palembang City DPMPB employees. The results of further research will be used as an alternative solution in conducting performance assessments that are more objective than previous assessments. Both algorithms were evaluated for model performance using the Confusion Matrix. Based on the results of the evaluation carried out, it was stated that the Decision Tree algorithm had better accuracy, namely 91.74% compared to Naïve Bayes which had an accuracy of 88.99% with a test size of 0.4

Author Biographies

Dewi Sartika, Univesitas Indo Global Mandiri, Indonesia

currently a leacturer for Informatics Engineering, Faculty of Computer Science at Universitasy of Indo Global Mandiri, Palembang, Indonesia. She completed her degree for Bachelor of Informatics Engineering in Sriwijaya University and Master of Computer Science in Universitas Indonesia. She has research interests in software enginerring and computational intellegent.

Rendra Gustriansyah, Universitas Indo Global Mandiri

Dr. Rendra Gustriansyah, S.T., M.Kom. currently a lecturer at the Faculty of Computer Science, Indo Global Mandiri University, Palembang, Indonesia. He completed his Bachelor of Engineering degree in Electrical Engineering from Sriwijaya University. Furthermore, he completed his Masters and Doctoral degrees with BPPDN scholarships from the Ministry of Research, Technology and Higher Education for Masters in Computer Science - University of Indonesia and Doctoral in Engineering (Informatics Engineering) from Sriwijaya University.

He was entrusted with various structural positions, such as Head of Computer Laboratory, Head of Departement of Informatics Engineering, Computer Systems, Computer Engineering, Vice Dean of the Faculty of Computer Science, Head of Technical Support Bureau, Head of Business Institute, Head of Research and Development Institute.

In addition, he is also an active writer and reviewer of various international and national (accredited) journals, his current Scopus h-index is 6. His areas of expertise include Machine Learning, Data Mining, Decision Making, and Business Intelligence. Until now, he has been a lecturer for more than 24 years and holds a competency certificate with a Web Developer qualification from the Indonesian Professional Certification Authority.

How to Cite
Sartika, D., & Gustriansyah, R. (2024). Comparison of naive Bayes and decision tree algorithms to assess the performance of Palembang City fire and Disaster management employees. TEKNOSAINS : Jurnal Sains, Teknologi Dan Informatika, 11(1), 132-138.