Implementasi Data Mining Kesehatan Mental Menggunakan Asosiasi Dan Klasterisasi
Abstract
Penelitian ini menganalisis dataset hasil data mining dari kaggle.com, dengan tujuan untuk mengidentifikasi pola klaster dan hubungan asosiasi yang berkaitan dengan gangguan kesehatan mental. Dalam proses klasterisasi, nilai optimal untuk jumlah klaster ditemukan pada k = 3 dengan nilai Davies-Bouldin Index (DBI) sebesar 0,470, yang menunjukkan pembagian klaster yang jelas dan terpisah. Sementara itu, dalam analisis asosiasi menggunakan model FP-Growth, ditemukan 20 premis yang menggambarkan hubungan yang berbeda-beda, dengan usia (Age) sebagai faktor yang sering muncul dan berperan signifikan dalam mempengaruhi gangguan kesehatan mental. Berdasarkan metrik evaluasi seperti support, confidence, lift, dan conviction, beberapa nilai yang sering muncul adalah support tertinggi pada 0.742, confidence pada 0.821, dan lift pada 1.064. Selain itu, nilai Laplace dan p-s juga menunjukkan kontribusi penting pada temuan ini. Secara keseluruhan, hasil analisis asosiasi mengindikasikan bahwa usia memiliki pengaruh yang cukup besar terhadap gangguan kesehatan mental, dengan berbagai metrik menunjukkan konsistensi dalam hubungan tersebut. Temuan ini dapat menjadi dasar untuk penelitian lebih lanjut dalam memahami faktor-faktor yang mempengaruhi kesehatan mental berdasarkan karakteristik individu.
Kata Kunci: Data Mining, Kesehatan Mental, Asosiasi, Klasterisasi
References
Geasela, Y. M., Bernanda, D. Y., Andry, J. F., . Jusuf, C. K., Winata, S., Lydia, & Everlin, S. (2024). Analysis of Student Mental Health Dataset Using Mining Techniques," Journal of Computer Science, vol. 20, no. 1, pp. 121-128, 2024, https://doi: 10.3844/jcssp.2024.121.128.
Golden, T. L., Tetreault, L., Ray, C. E., Kuge, M. N., Tiedemann, A., & Magsamen, S. (2022). The State of Music-Based Interventions for Mental Illness: Thought Leaders on Barriers, Opportunities, and the Value of Interdisciplinarity. Community Mental Health Journal, 58(3), 487–498. https://doi.org/10.1007/s10597-021-00843-4
Gupta, A., Sharma, H., & Akhtar, A. (2021). A comparative analysis of k-means and hierarchical clustering. EPRA International Journal of Multidisciplinary Research (IJMR), 7(8). https://doi.org/10.36713/epra8308
Gupta, G., Wayal, A., Jain, R., Dr. Waghmare, K., Kadale, P. (2023). Mood Based Music Recommendation System. International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets39298
Hou, J. (2022). Effective Ways for College Students’ Mental Health Education Based on Music Therapy. Journal of Healthcare Engineering. Hindawi Limited. https://doi.org/10.1155/2022/3031064
Hou, T., Zhang, T., Cai, W., Song, X., Chen, A., Deng, G., & Ni, C. (2020). Social support and mental health among health care workers during Coronavirus Disease 2019 outbreak: A moderated mediation model. Plos one, 15(5). https://doi.org/10.1371/journal.pone.0233831
Keleş, A. E., Önen, E., & Górecki, J. (2022). Determination of green building awareness: a study in Turkey. Sustainability, 14(19), 11943. https://doi.org/10.3390/su141911943
Lu, D. (2023). Evaluation Model of Music Therapy’s Auxiliary Effect on Mental Health Based on Artificial Intelligence Technology. Journal of Environmental and Public Health, 2022. https://doi.org/10.1155/2022/9960589
Madyatmaja, E. D., Jordan, S. I., & Andry, J. F. (2021a). Big Data Analysis Using RapidMiner Studio To Predict Suicide Rate In Several Countries. ICIC Express Letters Part B: Applications, 12(8), 757-764. https://doi.org/10.24507/icicelb.12.08.757
Madyatmadja, E. D., Sembiring, D. J. M.,. Angin, S. M. B. P., Ferdy, D. and Andry, J. F. (2021b). Big data in educational institutions using RapidMiner to predict learning effectiveness," Journal of Computer Science, vol. 17, no. 4, pp. 403-413. https://doi: 10.3844/jcssp.2021.403.413
Mutalib, S., Shafiee, N. S. M., & Abdul-Rahman, S. (2021). Mental health prediction models using machine learning in higher education institution. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(5), 1782-1792. http://dx.doi.org/10.17762/turcomat.v12i5.2181
Qiaoyi, Chen. (2023). Mental Health as a Mediator of the Transformational Leadership to Better Performance. Journal of Education, Humanities and Social Sciences. https://doi.org/10.54097/ehss.v8i.4660
Pratama, Y., Pramudya, Y., Albert, E., Ferdinand, R., Juniansyah, V., & Rasywir, E. (2022). Klasterisasi Data Pertanian di Tingkat Provinsi Jambi Tahun 2021 Menggunakan Algoritma K-Means. Bulletin of Computer Science Research, 3(1), 57-63. https://doi.org/10.47065/bulletincsr.v3i1.205
Rahman, R. A., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020). Application of machine learning methods in mental health detection: a systematic review. Ieee Access, 8, 183952-183964. https://doi.org/10.1109/ACCESS.2020.3029154
Rebecchini, L. (2021). Music, mental health, and immunity. Brain, Behavior, and Immunity - Health, 18. https://doi.org/10.1016/j.bbih.2021.100374
Schwartz, K. D., Exner-Cortens, D., McMorris, C. A., Makarenko, E., Arnold, P., Van Bavel, M., ... & Canfield, R. (2021). COVID-19 and student well-being: Stress and mental health during return-to-school. Canadian journal of school psychology, 36(2), 166-185. https://doi.org/10.1177/08295735211001653
Wang, K., Gao, S., & Huang, J. (2022). Learning About Your Mental Health From Your Playlist? Investigating the Correlation Between Music Preference and Mental Health of College Students. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.824789
Wang, Q., Zhang, W., & An, S. (2023). A systematic review and meta-analysis of Internet-based self-help interventions for mental health among adolescents and college students. Internet Interventions, 100690. https://doi.org/10.1016/j.invent.2023.100690
Wesseldijk, L. W., Ullén, F., & Mosing, M. A. (2019). The effects of playing music on mental health outcomes. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-49099-9
Wesseldijk, L. W., Lu, Y., Karlsson, R., Ullén, F., & Mosing, M. A. (2023). A comprehensive investigation into the genetic relationship between music engagement and mental health. Translational Psychiatry, 13(1). https://doi.org/10.1038/s41398-023-02308-6
WHO (World Health Organization), 2019. What is the evidence on the role of the arts in improving health and well-being? A scoping review. World Health Organization. Regional Office for Europe;
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