DAVINA RIZKY EFENDI, NPM 2207100004 (2025) KLASIFIKASI TINGKAT KELULUSAN MAHASISWA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR (K-NN) PADA DATA AKADEMIK PERGURUAN TINGGI. Tugas_Akhir(Artikel) Journal of Computer Science and Information Systems (JCoInS), 6 (3). pp. 409-421. ISSN 2747-2221(e-ISSN)
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Abstract
Higher education is an important factor in scoring quality human resources, where one indicator of success is the graduation rate of students on time. This study aims to classify the graduation rate of students using the algorithm K-Nearest Neighbor (K-NN) based on academic data which includes GPA, number of credits, frequency of repetition of courses, and attendance. The results of the classification showed that 30% of students successfully graduated on time, while the rest had delays. With the k-NN approach, it is expected that this model can help universities in predicting student graduation more accurately and optimizing academic interventions to improve graduation efficiency. Keywords : K-Nearest Neighbor (K-NN), Graduation Rate, Academic Data, Student Classification, Higher Education
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | K-Nearest Neighbor (K-NN), Graduation Rate, Academic Data, Student Classification, Higher Education |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science |
| Divisions: | Fakultas Sains Dan Teknologi > Informatika Komputer |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 08 Oct 2025 02:07 |
| Last Modified: | 08 Oct 2025 02:07 |
| URI: | http://repository.ulb.ac.id/id/eprint/1769 |
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