Model Interpretation for Student Major Selection Using Principal Component Analysis and Random Forest
DOI:
https://doi.org/10.46984/zyc1hh02Keywords:
Major Selection, PCA, Random Forest, Recommendation, SHAPAbstract
The development of information technology has had a significant impact on the education sector by providing data-driven tools to support the process of major selection. This process often causes confusion among students due to its crucial role in determining their academic and career futures. This study aims to develop an accurate and transparent recommendation system for major selection through the integration of Principal Component Analysis (PCA), Random Forest (RF), and SHAP. The research follows a systematic framework that includes data processing and model evaluation stages. PCA is applied to reduce the dimensionality of complex student data in order to improve computational efficiency and minimize information redundancy. Furthermore, the Random Forest algorithm is employed as a classification model to predict major recommendations such as Science, Social Sciences, and Religious Studies. The SHAP method is integrated to provide both mathematical and visual interpretations of the contribution of each academic feature to the model’s prediction results. The research data are obtained from the internal records of MAN 1 Payakumbuh covering the last three academic years (2022/2023–2024/2025). The dataset consists of 571 eleventh-grade students with tenth-grade academic scores and non-academic skill variables. The implementation of this model is able to provide more objective recommendations compared to conventional subjective assessments, achieving an accuracy of 88.70%. Visualization of feature contributions using SHAP enhances transparency and facilitates stakeholders’ understanding of the basis for each model decision. This study contributes to improving the efficiency of the major selection process and supports more accurate academic decision-making for students and educators.
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