Prediksi Mood Berdasarkan Pola Aktivitas Fisik pada Remaja Menggunakan Algoritma Machine Learning

Autor(s): Mayang Mega Kencana, Jajat Jajat, Mohammad Zaky, Kuston Sultoni, Yati Ruhayati
DOI: 10.25157/jkor.v11i1.17894

Abstract

Penelitian ini bertujuan untuk memprediksi mood remaja berdasarkan pola aktivitas fisik menggunakan algoritma machine learning. Aktivitas fisik sering kali berkorelasi erat dengan kondisi emosional, sehingga pendekatan ini dapat memberikan wawasan baru dalam mendukung kesehatan mental. Data dikumpulkan dari 50 remaja mahasiswa melalui perangkat wearable selama 90 hari, mencakup parameter seperti jumlah langkah, durasi aktivitas, intensitas, dan pola tidur. Sementara untuk data mood diperoleh dari skala BRUMs yang sudah diadaptasi dan divalidasi. Algoritma Decision Tree dan Support Vector Machine (SVM) digunakan untuk membangun model prediksi, dengan evaluasi kinerja berdasarkan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Decision Tree lebih unggul dibandingkan SVM, dengan akurasi sebesar 98,44% dibandingkan 97,45%. Decision Tree juga menunjukkan keunggulan dalam interpretasi model dan efisiensi komputasi, yang penting untuk implementasi aplikasi prediktif real-time. Penelitian ini menyimpulkan bahwa algoritma Decision Tree merupakan pendekatan yang lebih efektif untuk prediksi mood berbasis pola aktivitas fisik pada remaja. Temuan ini diharapkan dapat menjadi dasar pengembangan sistem pendukung kesehatan mental berbasis teknologi wearable.

Keywords

Aktivitas fisik, kesehatan mental, machine learning, prediksi mood, remaja

Full Text:

PDF

References

Alves, D. G. L., Rocha, S. G., Andrade, E. V., Mendes, A. Z., & Cunha, Â. G. J. (2019). The positive impact of physical activity on the reduction of anxiety scores: a pilot study. Revista da Associação Médica Brasileira, 65, 434-440.

Bland, H. W., Melton, B. F., Bigham, L. E., & Welle, P. D. (2014). Quantifying the impact of physical activity on stress tolerance in college students. College student journal, 48(4), 559-568.

Cernian, A., Olteanu, A., Carstoiu, D., & Mares, C. (2017, May). Mood detector-on using machine learning to identify moods and emotions. In 2017 21st International Conference on Control Systems and Computer Science (CSCS) (pp. 213-216). IEEE.

Cho, C. H., Lee, T., Kim, M. G., In, H. P., Kim, L., & Lee, H. J. (2019). Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study. Journal of medical Internet research, 21(4), e11029.

Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019). Review of machine learning algorithms for diagnosing mental illness. Psychiatry investigation, 16(4), 262.

Chong, C. S., Tsunaka, M., & Chan, E. P. (2011). Effects of yoga on stress management in healthy adults: a systematic review. Alternative therapies in health and medicine, 17(1), 32.

Driver, S., & Ede, A. (2009). Impact of physical activity on mood after TBI. Brain injury, 23(3), 203-212.

Hardwis, S., & Jajat, J. (2024). Analisis Resiko Obesitas Berdasarkan Aktivitas Fisik: Implementasi Metode Artificial Intelligence Machine Learning. Jurnal Keolahragaan, 10(2), 29-36.

Hidaka, B. H. (2012). Depression as a disease of modernity: explanations for increasing prevalence. Journal of affective disorders, 140(3), 205-214.

Hoge, E., Bickham, D., & Cantor, J. (2017). Digital media, anxiety, and depression in children. Pediatrics, 140(Supplement_2), S76-S80.

Horne, D., Kehler, D. S., Kaoukis, G., Hiebert, B., Garcia, E., Chapman, S., ... & Arora, R. C. (2013). Impact of physical activity on depression after cardiac surgery. Canadian Journal of Cardiology, 29(12), 1649-1656.

Jajat, J., Sudrazat, A., Zaky, M., & Sultoni, K. (2024). Implementation of Artificial Intelligence (AI) Machine Learning for Analysis of Physical Activity Behavior, Sedentary Behavior, and Obesity Risk. Indonesian Journal of Sport Management, 4(4).

Jie, Jianda, Zhilin, Rui, & Fugao. (2024). Adolescent mental health interventions: A narrative review of the positive effects of physical activity and implementation strategies. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1433698/full

Johnson, R., & Wang, P. (2021). Comparative Analysis of Machine Learning Algorithms for Health Monitoring Systems. Data Analytics Review, 8(4), 345-360.

Kanning, M., & Schlicht, W. (2010). Be active and become happy: an ecological momentary assessment of physical activity and mood. Journal of sport and exercise psychology, 32(2), 253-261.

Koch, E. D., Tost, H., Braun, U., Gan, G., Giurgiu, M., Reinhard, I., ... & Reichert, M. (2020). Relationships between incidental physical activity, exercise, and sports with subsequent mood in adolescents. Scandinavian Journal of Medicine & Science in Sports, 30(11), 2234-2250.

Smith, A., Brown, R., & Davis, K. (2022). Kernel-Based Approaches in Small Dataset Analysis: Challenges and Limitations. International Journal of Data Science, 10(1), 102-115.

Zhang, X., Li, J., & Huang, Y. (2023). The Efficiency of Tree-Based Algorithms in Mood Prediction Applications. Journal of Machine Learning Applications, 15(3), 245-260.

DOI: http://dx.doi.org/10.25157/jkor.v11i1.17894

Refbacks

  • There are currently no refbacks.