NAINGGOLAN, RIO DAUD FERNANDO and Kurniati, Rizki and Saputra, Danny Matthew (2024) PERBANDINGAN METODE DECISION TREE (C4.5) DAN K-NEAREST NEIGHBOR UNTUK KLASIFIKASI KELULUSAN MAHASISWA. Undergraduate thesis, Sriwijaya University.
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Abstract
Timely graduation is one of the indicators of student success in obtaining a bachelor's degree. However, in reality, students do not always complete their studies within four years due to many factors influencing the duration of their studies. Timely graduation is also one of the components used to measure the quality of higher education institutions. To address this issue, a system is needed to classify student graduation. In this study, the authors use the K-Nearest Neighbor (KNN) and Decision Tree (C4.5) methods. This study utilizes several parameters, including GPA (Grade Point Average), Student Status, Marital Status, and CGPA (Cumulative Grade Point Average). K-Nearest Neighbor works by calculating the distance between the data to be classified and other data in the dataset, then determining the classification based on the majority class of the nearest data. Decision Tree (C4.5) builds a decision tree based on existing attributes, selecting the most influential attribute at each node to separate the data into more homogeneous subgroups. The results of this study show that the K-Nearest Neighbor method has an accuracy rate of 88.6%, while Decision Tree (C4.5) achieves an accuracy rate of 86.84%. This research provides insights into the performance of classification models for on-time and delayed student graduation.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Rio Daud Fernando Nainggolan |
| Date Deposited: | 05 Jul 2024 04:01 |
| Last Modified: | 05 Jul 2024 04:01 |
| URI: | http://repository.unsri.ac.id/id/eprint/149445 |
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