PENERAPAN DATA MINING DALAM PREDIKSI TINGKAT INDEKS PRESTASI MAHASISWA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) (STUDI KASUS : UNIVERSITAS SRIWIJAYA)

SUMARDI, M. FRIDO and Tania, Ken Ditha (2022) PENERAPAN DATA MINING DALAM PREDIKSI TINGKAT INDEKS PRESTASI MAHASISWA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) (STUDI KASUS : UNIVERSITAS SRIWIJAYA). Undergraduate thesis, Sriwijaya University.

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Abstract

The cumulative grade point or GPA is a student's academic achievement score which is calculated from all courses a student has passed. This study aims to design a web-based system that can predict the level of grade point average achieved by students using the SVM method which serves as a supporting material for making decisions about a program related to the level of student GPA. Sriwijaya University is the place chosen by the researcher for this case study. Researchers use the support vector machine or SVM method, while to calculate the accuracy of the data using 10 fold cross validation. The training data used is Sriwijaya University alumni data in 2019 and the testing data used is active student data. The results of this study are that the SVM method is able to predict student GPA levels with an accuracy rate of 73.27% and a web-based system for predicting student GPA levels.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	M. Frido Sumardi
Date Deposited:	24 Jan 2023 08:51
Last Modified:	24 Jan 2023 08:51
URI:	http://repository.unsri.ac.id/id/eprint/87278

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