Integrating Educational Data Mining through Decision Tree and Random Forest Comparison for Assessing Employment Suitability

Riski Hidayatullah Maas; Sumijan; Rini Sovia

doi:10.35134/komtekinfo.v13i3.703

Authors

Riski Hidayatullah Maas Universitas Negeri Padang, Padang, 25132, Indonesia
Sumijan Universitas Putra Indonesia "YPTK" Padang
Rini Sovia Universitas Putra Indonesia "YPTK" Padang

DOI:

https://doi.org/10.35134/komtekinfo.v13i3.703

Keywords:

DT, RF, Alumni Employment Suitability

Abstract

Educational Data Mining can be utilized to identify patterns in educational data and support the evaluation of the alignment between graduates’ competencies and labor market requirements. Assessing alumni employment suitability is essential for providing an overview of their readiness and job alignment after completing their education. This study aims to compare the performance of the Decision Tree (DT) and Random Forest (RF) algorithms in classifying alumni employment suitability levels. The research stages included data collection, selection of employed alumni data, data cleaning, transformation of categorical variables into numerical scores, target label construction, division of the dataset into training and testing sets, and class balancing using Random Over-Sampling. The classification models were constructed using the DT and RF algorithms and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. The dataset was obtained from the 2025 Alumni Tracer Study conducted by Universitas Negeri Padang and comprised 2,831 records with eleven variables related to alumni employment and competencies. The results indicate that the Decision Tree achieved an accuracy of 83.42%, a macro-averaged precision of 76.74%, a macro-averaged recall of 76.82%, and a macro-averaged F1-score of 76.59%. Random Forest demonstrated better performance, achieving an accuracy of 90.83%, a macro-averaged precision of 83.85%, a macro-averaged recall of 82.22%, and a macro-averaged F1-score of 82.94%. This study contributes an Educational Data Mining-based approach that can assist higher education institutions in evaluating alumni employment suitability and provide a basis for formulating policies aimed at improving graduates’ competencies.

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