Analysis And Prediction Of Job Training Suitability For Job Seekers’ Professions At The Department Of Employment, Industry, And Trade Of Batu Bara Regency Using The Naive Bayes Algorithm And Feature Selection

Eko Budianto; Muhammad Iqbal; Zulham Sitorus

Authors

Eko Budianto University Pembangunan Panca Budi
Muhammad Iqbal University Pembangunan Panca Budi
Zulham Sitorus University Pembangunan Panca Budi

Keywords:

Classification, Feature Selection, Job Training recommendation, Machine Learning, Naive Bayes Classifier.

Abstract

Public vocational training effectiveness depends on alignment between training programs and job seekers’ professional profiles. In practice, training placement is often determined manually and subjectively, causing competency mismatches that reduce program effectiveness. This study develops an optimized computational framework to predict the suitability of vocational training programs for job seekers at the Department of Employment, Industry, and Trade of Batu Bara Regency. Using the Knowledge Discovery in Databases (KDD) framework, 1,434 historical records containing demographic data, education, work experience, occupational interests, and competency indicators were analyzed. To address the conditional independence limitation of the Naive Bayes classifier, three filter-based feature selection methods Information Gain, Mutual Information, and Chi-Square were implemented and compared. Results show that feature selection improved model performance, increasing accuracy from 91.26% to 93.01% across all methods. The consistent performance indicates that all methods identified the same dominant predictor, primarily professional interest, while removing redundant attributes. The proposed hybrid model demonstrates strong stability and generalization capability, providing a reliable decision support system for reducing employment mismatches and improving workforce development resource allocation.

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