Systematic Literature Review (SLR): Analisis Isu Mental Health Di Era Digital

Penulis

  • Gunawan Putra Wahdana Universitas Prof. Dr. H. M. Arifin Sallatang, Bantaeng, Indonesia
  • Dhimas Tribuana Akademi Sekretari Manajemen Indonesia Publik, Makassar, Indonesia
  • Dayanti Dayanti Universitas Partia Artha, Gowa, Indonesia

DOI:

https://doi.org/10.64476/jtbc.v2i2.68

Kata Kunci:

kesehatan mental, SLR, artificial intelligence, akses layanan, faktor sosial

Abstrak

Kesehatan mental merupakan isu global yang mengalami peningkatan signifikan dalam beberapa tahun terakhir, terutama akibat perubahan sosial dan perkembangan teknologi digital. Penelitian ini bertujuan untuk mengidentifikasi domain utama, menganalisis tren penelitian, serta menemukan kesenjangan dalam studi kesehatan mental melalui pendekatan Systematic Literature Review (SLR). Metode yang digunakan meliputi identifikasi, penyaringan, seleksi, dan analisis terhadap 20 jurnal ilmiah yang dipublikasikan pada periode 2023–2026. Hasil penelitian menunjukkan bahwa terdapat empat domain utama dalam penelitian kesehatan mental, yaitu akses layanan, teknologi dan kecerdasan buatan, faktor sosial, serta kolaborasi global. Temuan menunjukkan bahwa meskipun teknologi seperti Artificial Intelligence (AI) dan machine learning mampu meningkatkan akurasi prediksi dan deteksi dini, permasalahan utama masih terletak pada keterbatasan akses layanan dan pengaruh faktor sosial. Penelitian ini juga menemukan adanya kesenjangan antara pengembangan teknologi dan implementasi nyata di lapangan. Kontribusi utama penelitian ini adalah menghasilkan model integratif sistem kesehatan mental yang menggabungkan aspek akses, teknologi, faktor sosial, dan kolaborasi. Model ini diharapkan dapat menjadi dasar dalam pengembangan sistem kesehatan mental yang lebih efektif, terintegrasi, dan berkelanjutan di masa depan.

Unduhan

Data unduhan belum tersedia.

Referensi

Abd Al-Alim, M., Mubarak, R., Salem, N. M., & Sadek, I. (2024). A machine-learning approach for stress detection using wearable sensors in free-living environments. Computers in Biology and Medicine, 180, 108918. https://doi.org/10.1016/j.compbiomed.2024.108918

Adi Istya, R., Indra Astutik, I. R., & Hindarto, H. (2024). Sistem pakar deteksi kondisi kesehatan mental pada Generasi Z menggunakan metode backward chaining. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 9(1), 67–78. https://doi.org/10.29100/jipi.v9i1.4283

Alagarajah, J., Ceccolini, D., & Butler, S. (2024). Digital mental health interventions for treating mental disorders in young people based in low- and middle-income countries: A systematic review of the literature. Cambridge Prisms: Global Mental Health, 11, e74. https://doi.org/10.1017/gmh.2024.71

Bacellar, I. O. L., Morin, G., Daniels, S., Turecki, G., Palaniyappan, L., & Lepage, M. (2023). Opening up mental health research. Journal of Psychiatry and Neuroscience, 48(3), E209–E216. https://doi.org/10.1503/jpn.220199

Brocki, L., Dyer, G. C., Gładka, A., & Chung, N. C. (2023). Deep learning mental health dialogue system. arXiv. https://arxiv.org/abs/2301.09412

Carter, H., Araya, R., Anjur, K., Deng, D., & Naslund, J. A. (2021). The emergence of digital mental health in low-income and middle-income countries: A review of recent advances and implications for the treatment and prevention of mental disorders. Journal of Psychiatric Research, 133, 223–246. https://doi.org/10.1016/j.jpsychires.2020.12.016

Chakrabarti, S. (2024). Digital psychiatry in low-and-middle-income countries: New developments and the way forward. World Journal of Psychiatry, 14(3), 350–361. https://doi.org/10.5498/wjp.v14.i3.350

Dar, M. A., Amin, R., Khan, R. R., Bashir, N., Zuhare, S. M., & Hyder, A. (2025). Unveiling the mental health services gap: Help-seeking and referral patterns in South Kashmir, India. Middle East Current Psychiatry, 32, Article 65. https://doi.org/10.1186/s43045-025-00555-5

Fatah, Z., & Hasanah, U. (2025). Prediksi tingkat stress dan kesehatan mental mahasiswa menggunakan algoritma SVM. Jurnal Mahasiswa Teknik Informatika, 4(2), 200–207. https://doi.org/10.35473/jamastika.v4i2.4542

Fitriani, L. N., Purba, V., Hasanah, Y. N., & Rozak, R. W. A. (2023). Analisis kesehatan mental di masyarakat. Nautical: Jurnal Ilmiah Multidisiplin Indonesia, 2(9), 1–7. https://doi.org/10.55904/nautical.v2i9.1051

Kim, J., Marcus, S. C., Mandell, D. S., & Hadley, T. R. (2023). Effectiveness of digital mental health tools to reduce depressive and anxiety symptoms in low- and middle-income countries: Systematic review and meta-analysis. JMIR Mental Health, 10, e43066. https://doi.org/10.2196/43066

Kola, L., Kohrt, B. A., Hanlon, C., Naslund, J. A., Sikander, S., Balaji, M., Benjet, C., Cheung, E. Y. L., Eaton, J., Gonsalves, P., Hailemariam, M., Luitel, N. P., Machado, D. B., Misganaw, E., Omigbodun, O., Roberts, T., Salisbury, T. T., Shidhaye, R., Sunkel, C., Ugo, V., van Rensburg, A. J., Gureje, O., Pathare, S., Saxena, S., Thornicroft, G., & Patel, V. (2021). COVID-19 mental health impact and responses in low-income and middle-income countries: Reimagining global mental health. The Lancet Psychiatry, 8(6), 535–550. https://doi.org/10.1016/S2215-0366(21)00025-0

Li, T. H., Kamin, L., George, J., Saiz, F. S., & Meyer, P. (2023). Impact of the COVID-19 pandemic on treatment for mental health needs: A perspective on service use patterns and expenditures from commercial medical claims data. BMC Health Services Research, 23, Article 86. https://doi.org/10.1186/s12913-023-09080-9

Mohamed, E. S., Naqishbandi, T. A., Bukhari, S. A. C., Rauf, I., Sawrikar, V., & Hussain, A. (2023). A hybrid mental health prediction model using support vector machine, multilayer perceptron, and random forest algorithms. Healthcare Analytics, 3, 100185. https://doi.org/10.1016/j.health.2023.100185

Narkhede, D. N. (2025). AI-powered mental health detection system using text analysis. International Journal of Progressive Research in Engineering, Management and Science. https://www.ijprems.com/research-paper/ai-powered-mental-health-detection-system-using-text-analysis

Ni, Y., & Jia, F. (2025). A scoping review of AI-driven digital interventions in mental health care: Mapping applications across screening, support, monitoring, prevention, and clinical education. Healthcare, 13(10), 1205. https://doi.org/10.3390/healthcare13101205

Nurhafiyah, I., & Marcos, H. (2023). Sistem pakar diagnosis kesehatan mental pada mahasiswa Universitas Amikom Purwokerto. KOMPUTA: Jurnal Ilmiah Komputer dan Informatika, 12(1), 49–56. https://doi.org/10.34010/komputa.v12i1.8978

Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F., & Eberhardt, J. (2024). Enhancing mental health with artificial intelligence: Current trends and future prospects. Global Medicine, 2, 100099. https://doi.org/10.1016/j.glmedi.2024.100099

Ramadhan, M. E. S., & Misbah, M. (2025). Sistem pemantauan stres dan kecemasan untuk deteksi dini kesehatan mental memakai sensor biomedik berbasis IoT dan deep neural networks. Elkom: Jurnal Elektronika dan Komputer, 18(1), 309–317. https://journal.stekom.ac.id/index.php/elkom/article/download/2843/1967

Rijayanti, S. N., Rohmanu, A., & Endang. (2026). Rancang bangun chatbot interaktif sebagai media edukasi kesehatan mental remaja menggunakan metode Rapid Application Development (RAD) di SMK Dewantara 2 Kabupaten Bekasi. Jurnal Informatika SIMANTIK, 11(1), 1–5. https://simantik-panca-sakti.ac.id/index.php/simantik/article/view/145

Rugulies, R., Aust, B., Greiner, B. A., Arensman, E., Kawakami, N., LaMontagne, A. D., & Madsen, I. E. H. (2023). Work-related causes of mental health conditions and interventions for their improvement in workplaces. The Lancet, 402(10410), 1368–1381. https://doi.org/10.1016/S0140-6736(23)00869-3

Santomauro, D. F., Herrera, A. M. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., Bang-Jensen, B. L., Bertolacci, G. J., Bloom, S. S., Castellano, R., Castro, E., Chakrabarti, S., Chattopadhyay, J., Cogen, R. M., Collins, J. K., & Ferrari, A. J. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. The Lancet, 398(10312), 1700–1712. https://doi.org/10.1016/S0140-6736(21)02143-7

Sharma, A., Rushton, K., Lin, I. W., Wadden, D., Lucas, K. G., Miner, A. S., Nguyen, T., & Althoff, T. (2023). Cognitive reframing of negative thoughts through human-language model interaction. arXiv. https://arxiv.org/abs/2305.02466

Sihombing, N., Rahmadi, M. A., Atikah, S., Lubis, N. L., Rizki, L. M., Nasution, H., Mawar, L., & Hasibuan, R. H. (2026). Effectiveness of digital mental health intervention in Middle Eastern conflict zones: A technology-based meta-analysis. Jurnal Medika Nusantara, 4(1), 23–41. https://doi.org/10.59680/medika.v4i1.2180

Skovira, C. M., Pfoh, E., Thompson, A., & Rish, J. (2023). Closing the mental health access gap through novel analytics. Cureus, 15(7), e42093. https://doi.org/10.7759/cureus.42093

Taban, M., Nooraeen, S., Tanha, K., Moradi-Lakeh, M., & Malakouti, S. K. (2024). Effectiveness and cost-effectiveness of community-based mental health services for individuals with severe mental illness in Iran: A systematic review and meta-analysis. BMC Psychiatry, 24, Article 314. https://doi.org/10.1186/s12888-024-05666-7

Tong, J., Zhang, J., Zhu, N., Pei, Y., & Song, P. (2023). Effects of COVID-19 pandemic on mental health among frontline healthcare workers: A systematic review and meta-analysis. Frontiers in Psychology, 13, 1096857. https://doi.org/10.3389/fpsyg.2022.1096857

Valencia-Pecho, D. I., Varela-Guevara, S., Basauri-Delgado, M., & Saintila, J. (2026). Impact of depression, anxiety, and stress on mental health among Peruvian healthcare professionals. Healthcare, 14(4), 490. https://doi.org/10.3390/healthcare14040490

Waechter, R., Gallant, C., De Wilde, K., Arens, G., Brady, T., Custodio, J., Wakita, Y., Landon, B., Boateng, Y., Parthab, N., & Bhagat, A. (2023). Prevention of mental illness within public health: An analysis of progress via systematic literature review and a pathway forward. Preventive Medicine Reports, 34, Article 102249. https://doi.org/10.1016/j.pmedr.2023.102249

Wen, X., Wang, C., Zhang, Y., & Li, J. (2024). How does digital integration influence the mental health of low-income populations? Evidence from China. Healthcare, 12(24), 2593. https://doi.org/10.3390/healthcare12242593

Diterbitkan

2026-06-02

Cara Mengutip

Putra Wahdana, G., Tribuana, D., & Dayanti, D. (2026). Systematic Literature Review (SLR): Analisis Isu Mental Health Di Era Digital. Jurnal Teknologi Dan Bisnis Cerdas, 2(2), 153–165. https://doi.org/10.64476/jtbc.v2i2.68