MACHINE LEARNING MODEL TO PREDICT THE RISK OF STUDENTS NOT GRADUATING ON TIME

PUTRA, DIONYSIUS ABIRAMA (2023) MACHINE LEARNING MODEL TO PREDICT THE RISK OF STUDENTS NOT GRADUATING ON TIME. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.

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Abstract

The purpose of this study is creating a program to predict the risk of college student not graduating on time, which is graduating after more than four years of study. The approach to solve this problem is by developing machine learning with Random Forest algorithm. The model is trained and tested using dummy dataset of US student academic records within the first semester to fourth semester. The model is also trained with different number of generated tree. To analyze the result, Confusion Matrix is used to compare the accuracy, precision, misclassification, and recall rates of each trained model. The final result shown that Random Forest model with 91 generated tree has the highest accuracy of 67%, to predict the risk of student not graduating on time.

Item Type: Thesis (Other)
Subjects: 000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: Mr Yosua Norman Rumondor
Date Deposited: 08 Dec 2023 01:50
Last Modified: 08 Dec 2023 01:50
URI: http://repository.unika.ac.id/id/eprint/33806

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