Fa-Idzaa, Mea (2024) COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM FOREST METHODS IN ANALYZING THE EFFECT OF MUSIC ON REDUCING STRESS LEVELS AMONG 7TH-SEMESTER FEMALE STUDENTS DURING THESIS WRITING (Case Studi: Darussalam Gontor University). S1 Undergraduate thesis, Universitas Darussalam Gontor.

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Abstract

Universitas Darussalam Gontor is a boarding-based university designed to support learning efficiency. During their academic journey, students completing their theses often experience stress due to academic demands, making it a primary cause of stress. One effective way to reduce stress is by listening to music, which can enhance focus and productivity. This study aims to classify and analyze the influence of music on stress reduction among seventh-semester female students and compare the performance of the SVM and Random Forest algorithms in data analysis. The results show that out of a total of 340 collected data points, 22.4% of students found music to be highly effective in reducing stress, while 70.3% felt it was moderately effective. However, 7.4% reported no significant impact of music on stress reduction. The most effective and frequently listened-to music genre during thesis writing was pop music, particularly subgenres such as K-pop, J-pop, and Ipop, with more than 180 students out of the total 340 respondents preferring these genres. Regarding the effectiveness of the algorithms, SVM outperformed Random Forest with an accuracy of 97% compared to 79%. These findings indicate that music plays a role in managing and reducing stress levels among students during thesis writing and demonstrate that SVM is an effective algorithm for processing similar types of data. Keywords: Students, Stress, Music, SVM, Random Forest

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