Analisis Hubungan Preferensi Genre Musik dan Kesehatan Mental pada Dataset MXMH

jafar Jaya Priambudhi; Imam Suharjo

doi:10.47065/josh.v7i4.10236

jafar Jaya Priambudhi * Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia
Imam Suharjo Universitas Mercu Buana Yogyakarta, Yogyakarta, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josh.v7i4.10236

Keywords: Mental Health; Music; Random Forest; Clustering; Music Genre

Abstract

Mental health is a critical issue that is influenced by various factors, including music-listening habits. In the field of music psychology, music genre preferences are known to be associated with emotional regulation and an individual’s psychological state. This study aims to analyze the relationship between music genre preferences and mental health using a data-driven approach. The dataset used is the Music & Mental Health Survey (MXMH), which consists of 737 respondents with variables including music genre preferences, duration of music listening, and mental health indicators such as anxiety, depression, insomnia, and obsessive-compulsive disorder (OCD). The research stages included data preprocessing, exploratory data analysis (EDA), determining the number of clusters using the Elbow Method and Silhouette Score, clustering using the K-Means algorithm, analyzing the relationship between music genre and mental health, and classifying the clustering results using Random Forest. The results showed that respondents could be grouped into three clusters with distinct mental health characteristics. A Silhouette Score of 0.2246 indicates that the quality of cluster separation is still relatively low, making the segmentation results more exploratory in nature. Correlation analysis revealed a positive relationship between the anxiety and depression variables, as well as differences in music genre preference patterns among groups with different mental health conditions. The feature importance results show that the music genre preference variable contributes to distinguishing the characteristics of each cluster. The contribution of this study is to provide an empirical overview of the relationship between music genre preferences and mental health conditions based on the MXMH dataset through a machine learning-based segmentation and classification approach. The findings of this study suggest that music preferences have the potential to be used as an indicator for understanding patterns of an individual’s psychological state, although further validation and methodological development are needed to achieve a more robust segmentation.

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