Decision Support System for Departemen Selection for Prospective Students using the Naïve Bayes Method and Analytical Hierarchy Process Model at Faculty of Engineering Universitas Musamus

Zubaedah, Reza; Lintang, M; Putra, Nasra Pratama

doi:10.1088/1757-899x/1125/1/012030

Cited by 3 publications

(1 citation statement)

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“…The artificial neural network (ANN) method was adopted by Latifah et al [57] to predict suitable student specialization in a dataset of 314 students based on student records from the iGracias Integrated Academic Information System at Telkom University in 2016, and they achieved an accuracy of 94.81%. The NB method and analytic hierarchy process (AHP) techniques were adopted by Zubaedah et al [58] to build a decision support system to predict suitable specialization in technical faculty in Indonesia. A rule-based classification algorithm (PART) was adopted by Tamiza et al [59] to propose an intelligent model for selecting and predicting suitable university specialization.…”

Section: Literature Reviewmentioning

confidence: 99%

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

et al. 2021

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University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.

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Section: Literature Reviewmentioning

confidence: 99%