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All Journal SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan
Kusnawi
Amikom University Yogyakarta, Indonesia
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management

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Performance Analysis of SVM and Random Forest Algorithms in the Case of the Influence of Music on Mental Health Karisma Septa Kresna; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15130408

Abstract

Mental health disorders are conditions that impress a person's behavior, mindset, and emotions. According to WHO data, the rate of mental disorders in Asia has increased significantly in the past two decades, with about one-fifth of the world's adolescent population experiencing stress each year. Music has long been known to have a positive influence on mental health, and music therapy is used as one approach to assist individuals in improving social, mental, and physical conditions. In this study, the authors used data mining techniques to identify relevant patterns regarding the influence of music on mental health. Two classification algorithms, namely the Support Vector Machine (SVM) and Random Forest, is used to analyze and characterize the data. SVM is known to excel at managing high-dimensional data, while Random Forest is effective at handling data with missing outliers and features. This study purpose to oppose the performance of the two algorithms in classifying the influence of music on mental health to identify the superior algorithm in this context. The Random Forest algorithm gets 93% accuracy and SVM gets 95% accuracy, the hyperparameter tuning on the SVM algorithm has a better performance than Random Forest with an accuracy score of 97% for SVM, while for Random Forest it gets an accuracy score of 94%. The results of the study are expected to provide insight into the use of music as a mental health therapy tool.
Optimization of Stress Classification Among Students Using Random Forest Algorithm Raffa Nur Listiawan Dhito Eka Santoso; Kusnawi
SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Vol. 2 No. 2 (2025): April
Publisher : RAM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15130385

Abstract

Stress is a condition of physical and psychological discomfort experienced by students due to academic pressure, demands from parents and teachers, and schoolwork. This stress can lead to physical tension, behavioral changes, and mental health problems if not handled properly.  Random Forest is a promising approach to analyze and classify student stress. The aim of this study is to classify stress among students to enable the development of targeted interventions to support student well-being and academic success. The dataset used was sourced from Kaggle and included 1100 datasets with 21 columns. The research stages included data preprocessing, exploratory data analysis, modeling, Decision tree classification and evaluation of the confusion matrix model and Deployment as a measure of stress level. Classification results were evaluated by calculating accuracy, precision, recall and f1-score for stress classes (low, medium and high). The results of this study resulted in an accuracy value before tuning of 87.27% and after tuning of 88.64%. This research can provide insights for schools, parents, and government to develop more effective strategies in addressing the problem of stress among students. The use of Random Forest algorithm is proven to be effective in analyzing and classifying stress, so that it can help in decision making and appropriate welfare interventions to tackle before stress reaches critical levels.
Co-Authors Karisma Septa Kresna Raffa Nur Listiawan Dhito Eka Santoso