| Autor: |
Sholeh, Muhammad, Nurnawati, Erna Kumalasari |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 3077 Issue 1, p1-8, 8p |
| Abstrakt: |
One indicator of the success of the higher education system is the timely graduation of students. Students who take undergraduate programs are declared to graduate on time if students can study for less than or equal to eight semesters. Graduation time must be monitored from the beginning of the semester. Their success is indicated by the number of courses that pass each semester. Normalization is done so that the resulting model has maximum accuracy. This study aims to build the best Classification model using the Support Vector Machine (SVM) algorithm. That can predict students' timely graduation by comparing the data normalization process with the Z-Score, Min-Max, and without normalization methods. The datasheet is taken from data on student study results in each semester in ten study programs at Institut Sains & Teknologi AKPRIND class 2017, with as many as 267 data with 19 attributes. The model was developed using the first to sixth-semester achievement index data. The recommended model is selected from the maximum accuracy results. The results showed that the classification model with the SVM algorithm using Z-score normalization produced the highest accuracy, with an accuracy value of 83%. That is, the recommended model is a model using Z-Score normalization. The model generated from this algorithm can be used to predict student graduation. The hope is that this model can prevent student study failure by treating students who are predicted to experience study failure. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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