KLASIFIKASI TINGKAT STRES MAHASISWA DALAM PENYELESAIAN TUGAS AKHIR MENGGUNAKAN NAÏVE BAYES DAN K-NEAREST NEIGHBOR

LENNI PEFRIANTI, NPM 2209100068 (2026) KLASIFIKASI TINGKAT STRES MAHASISWA DALAM PENYELESAIAN TUGAS AKHIR MENGGUNAKAN NAÏVE BAYES DAN K-NEAREST NEIGHBOR. Tugas_Akhir(Artikel) Journal of Computer Science and Information Systems (JCoInS) Program Studi Sistem Informasi, Fakultas Sains & Teknologi, Universitas Labuhanbatu (Sinta 6), 7 (1). pp. 129-137. ISSN 2747-2221 (e-Issn)

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Abstract

This study aims to analyze the stress levels of final-year students and compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in stress classification. Data were collected from 82 respondents through a questionnaire consisting of seven variables (S1–S7) measuring factors contributing to stress, which were classified into low, moderate, and high stress levels. The results show that both algorithms can classify student stress effectively, with Naïve Bayes achieving the highest accuracy (90.15%) compared to KNN (87.72%). Distribution analysis by study program indicates that Agrotechnology has the highest proportion of students with high stress (42.86%), followed by Information Systems (40.63%) and Information Technology (13.64%). This study provides insights for the university to offer targeted support through counseling or stress management workshops. Keyword : Student Stress Levels, Naive Bayes Algorithm, K-Nearest Neighbor Algorithm. Student Stress Levels, Naive Bayes Algorithm, K-Nearest Neighbor Algorithm.

Item Type: Article
Uncontrolled Keywords: Student Stress Levels, Naive Bayes Algorithm, K-Nearest Neighbor Algorithm.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Divisions: Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User: Unnamed user with email repository@ulb.ac.id
Date Deposited: 24 Apr 2026 09:22
Last Modified: 24 Apr 2026 09:22
URI: http://repository.ulb.ac.id/id/eprint/2157

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