Hermanto, Hermanto (2020) Prediksi Kelulusan dan Putus Studi Mahasiswa dengan Pendekatan Bertingkat pada Perguruan Tinggi. Jurnal Sistem Informasi & Manajemen Basis Data (SIMADA), 03 (02).
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Prediksi Kelulusan dan Putus Studi Mahasiswa.pdf Download (4MB) |
Abstract
Currently, the problem of college failure, its on-time graduation, and the factors that cause it is still an interesting research topic (C. Marquez-Vera, C. Romero and S. Ventura, 2011). This study compares three data mining classification algorithms namely Naive Bayes, Decision Tree and K-Nearest Neighbor to predict graduation and dropout risk for students to improve the quality of higher education and the most accurate algorithms to use Prepare graduation and dropout prediction Student studies. The best algorithm for predicting graduation and dropout is the decision tree with the best accuracy value of 99.15% with a training data ratio of 30%
| Item Type: | Article |
|---|---|
| Subjects: | Ilmu Komputer |
| Divisions: | Jurnal > Jurnal Ilmu Komputer |
| Depositing User: | Shela Safitri |
| Date Deposited: | 09 Aug 2022 04:20 |
| Last Modified: | 07 Feb 2023 01:47 |
| URI: | http://repo.darmajaya.ac.id/id/eprint/7932 |
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