Implementation of the Naïve Bayes Algorithm in the Assessment of Competency Examinations for Training Program Participants at the Medan Industrial Training Center

Abstract

The development of information technology has driven digital transformation across various sectors, including education and industrial training. One important aspect is the assessment process of competency examinations for training program participants, which has traditionally been conducted manually, resulting in lengthy processing times, potential subjectivity, and low efficiency. This study aims to design and implement a competency examination assessment system based on the Naïve Bayes algorithm at the Medan Industrial Training Center (Balai Diklat Industri/BDI Medan). The research methodology includes problem identification, literature review, data collection in the form of examination questions and participants’ answer results, data preprocessing, system design, implementation using the Python programming language with a MySQL database, and system performance evaluation. The developed system involves three user roles: administrator, assessor, and participant. Participants complete theoretical examinations in the form of multiple-choice questions, while assessors provide evaluations for interviews and practical examinations. The administrator is responsible for managing data and examination questions, as well as processing assessment results using the Naïve Bayes algorithm. The implementation results indicate that the Naïve Bayes–based assessment system is capable of automatically classifying participants’ examination outcomes into Pass or Fail categories with a good level of accuracy. The system has proven to improve efficiency, accelerate the assessment process, and minimize subjectivity compared to manual methods. The conclusion of this study is that the application of the Naïve Bayes algorithm in a competency examination assessment system can serve as an effective and innovative solution for the digitalization of the evaluation process at BDI Medan. Future research is recommended to further develop the system to support essay-type questions using more advanced algorithmic approaches.

Keywords : Examination Assessment, Naïve Bayes, Machine Learning, Competency