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Muhammad Iqbal
University Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering

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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
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

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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.
Analysis Of The Decision Tree (C4.5) And Random Forest Algorithms To Determine Student Eligibility For Final Project Assignments Based On Academic Requirements Eisyaniah Desvazulinda; Muhammad Iqbal; Muhammad Syahputra Novelan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9947

Abstract

Determining student eligibility for undertaking a final project is an important process in higher education, which is often still conducted manually and subjectively. This study aims to develop a classification model based on machine learning to determine the eligibility of students at Batam University using Decision Tree (C4.5) and Random Forest algorithms. The data used includes Grade Point Average (GPA), total completed credits (SKS), prerequisite course grades, and academic records. This research employs a quantitative approach with stages including data collection, data preprocessing, model development, and performance evaluation using accuracy, precision, and recall metrics. The results show that both algorithms are capable of classifying student eligibility effectively. The Decision Tree (C4.5) algorithm produces an interpretable model in the form of decision rules, while Random Forest demonstrates superior performance in terms of accuracy and prediction stability. The comparison indicates that Random Forest is more effective in handling complex data, whereas C4.5 provides better model transparency. In conclusion, the implementation of Decision Tree (C4.5) and Random Forest algorithms can serve as an effective solution to support objective and data-driven academic decision-making. The resulting model has the potential to be developed into a decision support system to improve the efficiency and quality of determining student eligibility for final project enrollment.
Co-Authors Eisyaniah Desvazulinda Eko Budianto Muhammad Syahputra Novelan Zulham Sitorus