Identifikasi Gangguan Kesehatan Mental Pada Remaja Generasi Z Menggunakan Artificial Neural Network
DOI:
https://doi.org/10.26418/justin.v12i4.86650Keywords:
Artificial Neural Network(ANN), Kesehatan Mental, Generasi Z, Gangguan, RemajaAbstract
Penelitian ini bertujuan untuk mengidentifikasi gangguan kesehatan mental pada remaja generasi Z menggunakan Artificial Neural Network (ANN). Generasi Z, yang terdiri dari orang-orang yang lahir dari tahun 1997 hingga 2012, menghadapi tekanan yang berbeda selama era digital, yang dapat memengaruhi kesehatan mental mereka. ANN dipilih karena kemampuan untuk memprediksi pola kompleks dan menemukan komponen yang berkontribusi pada gangguan kesehatan mental. Penelitian ini mengumpulkan data dari generasi Z remaja melalui kuesioner yang mengukur gejala gangguan kesehatan mental seperti depresi, kecemasan, borderline, dan anti sosial. Data kemudian diolah dan diproses menggunakan algoritma ANN untuk melatih model prediksi. Dengan data baru, model tersebut divalidasi untuk mengidentifikasi gangguan kesehatan mental. Hasil penelitian adalah berdasarkan data dari 23 pertanyaan kuesioner tentang gangguan kesehatan mental seperti depresi, kecemasan, borderline, dan antisosial. Di sini, model ANN dapat memprediksi kemungkinan gangguan kesehatan mental dengan tingkat akurasi 0.97. Jadi, ada kesempatan baru untuk deteksi dini dan intervensi cepat, yang dapat membantu mengurangi dampak negatif gangguan kesehatan mental pada remaja Gen Z.
References
L. Jacob et al., “The relationship between physical activity and mental health in a sample of the UK public: A cross-sectional study during the implementation of COVID-19 social distancing measures,†Ment. Health Phys. Act., vol. 19, p. 100345, Oct. 2020, doi: 10.1016/j.mhpa.2020.100345.
Alodokter, “Gangguan Mental,†Alodokter. Accessed: Oct. 03, 2024. [Online]. Available: https://www.alodokter.com/kesehatan-mental
S. Caliccchio, Kesehatan mental dan gangguan psikologis: Apa itu dan bagaimana cara kerjanya. Stefano Calicchio, 2022.
Hello Sehat, “Mental Illness Bisa Bahaya, Apakah Anda termasuk Salah Satunya?,†Hello Sehat. Accessed: Oct. 03, 2024. [Online]. Available: https://hellosehat.com/mental/penyakit-mental/
L. Laka et al., Pendidikan Karakter Gen Z di Era Digital. PT. Sonpedia Publishing Indonesia, 2024.
T. detikHealth, “Gangguan Kesehatan Mental Gen Z Meningkat 200%, Pakar Ungkap 2 Penyebabnya,†detikedu. Accessed: Oct. 03, 2024. [Online]. Available: https://www.detik.com/edu/detikpedia/d-7096836/gangguan-kesehatan-mental-gen-z-meningkat-200-pakar-ungkap-2-penyebabnya
W. Zeng, R. Chen, X. Wang, Q. Zhang, and W. Deng, “Prevalence of mental health problems among medical students in China: A meta-analysis,†Medicine (Baltimore), vol. 98, no. 18, p. e15337, May 2019, doi: 10.1097/MD.0000000000015337.
S. B. Harahap and Y. Yamasari, “Klasifikasi Tingkat Stres Mahasiswa Menggunakan RMSProp untuk Arsitektur Artificial Neural Network,†J. Inform. Comput. Sci. JINACS, vol. 5, no. 04, pp. 560–567, May 2024, doi: 10.26740/jinacs.v5n04.p560-567.
Y. Devianto, E. Sediyono, S. Y. J. Prasetyo, and D. Manongga, “Membaca Sinyal Electroencephalogram (EEG) Dalam Menangkap Tingkat Emosi (Berdasarkan Ontologi),†Fakt. Exacta, vol. 17, no. 2, Art. no. 2, Jul. 2024, doi: 10.30998/faktorexacta.v17i2.20878.
N. Veldasari, A. Fadli, A. W. Wardhana, and M. S. Aliim, “Analisis Perbandingan Metode Certainty Factor, Dempster Shafer dan Teorema Bayes dalam Deteksi Dini Gangguan Kesehatan Mental,†J. Pendidik. Dan Teknol. Indones., vol. 2, no. 7, Art. no. 7, Jul. 2022, doi: 10.52436/1.jpti.191.
D. Wahyuni and D. Winarso, “PENERAPAN METODE RULE BASED REASONING DALAM SISTEM PAKAR DETEKSI DINI GANGGUAN KESEHATAN MENTAL PADA MAHASISWA,†J. Softw. Eng. Inf. Syst. SEIS, pp. 1–10, Aug. 2022, doi: 10.37859/seis.v2i2.3991.
M. Iqbal and L. Rizqulloh, “Deteksi Dini Kesehatan Mental Akibat Pandemi Covid-19 Pada Unnes Sex Care Community Melalui Metode Self Reporting Questionnaire,†Prax. J. Sains Teknol. Masy. Dan Jejaring, vol. 3, no. 1, Art. no. 1, Sep. 2020, doi: 10.24167/praxis.v3i1.2730.
S. Khalizah, I. Zurfia, and Armansyah, “Penerapan Metode Forward Chaining Dan Dempster-Shafer Pada Sistem Pakar Deteksi Dini Gangguan Kesehatan Mental,†J. FASILKOM, vol. 14, no. 1, Art. no. 1, Apr. 2024, doi: 10.37859/jf.v14i1.6942.
A. I. Maulana and R. Rismayani, “Mental Health Analysis at the University of Dipa Makassar using Naïve Bayes Classifier,†IT J. Res. Dev., vol. 8, no. 1, pp. 72–80, Dec. 2023, doi: 10.25299/itjrd.2023.11444.
R. Rismayani, M. Pineng, and H. Herlinda, “Using Artificial Neural Network for System Education Eye Disease Recognition Web-Based,†J. Biomim. Biomater. Biomed. Eng., vol. 55, pp. 262–274, 2022, doi: 10.4028/p-7z9xpt.
Rismayani, S. R. D. Rachman, S. Wahyuni, Asmanurhidayani, J. Y. Mambu, and M. Pineng, “Implementation Artificial Neural Network on Identification System of Neurological Disorder,†in Intelligent Communication Technologies and Virtual Mobile Networks, G. Rajakumar, K.-L. Du, and Ã. Rocha, Eds., Singapore: Springer Nature, 2023, pp. 619–629. doi: 10.1007/978-981-99-1767-9_45.
Rismayani, A. A. Ilham, A. Achmad, and M. R. Y. Rachman, “Facial Skin Disorder Prediction Based on Non-Visual Information Using ANN Model,†in 2024 7th International Conference on Informatics and Computational Sciences (ICICoS), Jul. 2024, pp. 450–455. doi: 10.1109/ICICoS62600.2024.10636923.
R. Istya and I. Astutik, Expert System for Mental Health Condition Detection in Generation Z Using Backward Chaining Method: Sistem Pakar Deteksi Kondisi Kesehatan Mental pada Generasi Z Menggunakan Metode Backward Chaining. 2023. doi: 10.21070/ups.1664.
R. Rismayani et al., Deep Learning. Yayasan Kita Menulis, 2023. Accessed: Oct. 07, 2024. [Online]. Available: https://scholar.google.com/scholar?cluster=13343239218410604203&hl=en&oi=scholarr
N. J. C. Stapelberg, M. Randall, J. Sveticic, P. Fugelli, H. Dave, and K. Turner, “Data mining of hospital suicidal and self-harm presentation records using a tailored evolutionary algorithm,†Mach. Learn. Appl., vol. 3, p. 100012, Mar. 2021, doi: 10.1016/j.mlwa.2020.100012.
A. Belouali et al., “Acoustic and language analysis of speech for suicide ideation among US veterans,†Jul. 10, 2020, medRxiv. doi: 10.1101/2020.07.08.20147504.
N. Cummins, F. Matcham, J. Klapper, and B. Schuller, “Chapter 10 - Artificial intelligence to aid the detection of mood disorders,†in Artificial Intelligence in Precision Health, D. Barh, Ed., Academic Press, 2020, pp. 231–255. doi: 10.1016/B978-0-12-817133-2.00010-0.
J. Lu, B. Liu, Z. Lian, C. Cai, J. Tao, and Z. Zhao, “Prediction of Depression Severity Based on Transformer Encoder and CNN Model,†in 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP), Dec. 2022, pp. 339–343. doi: 10.1109/ISCSLP57327.2022.10038064.
K. Mao et al., “Prediction of Depression Severity Based on the Prosodic and Semantic Features With Bidirectional LSTM and Time Distributed CNN,†IEEE Trans. Affect. Comput., vol. 14, no. 3, pp. 2251–2265, Jul. 2023, doi: 10.1109/TAFFC.2022.3154332.
M. Yang, Z. Weng, Y. Zhang, Y. Tao, and B. Hu, “Three-Stream Convolutional Neural Network for Depression Detection With Ocular Imaging,†IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 4921–4930, 2023, doi: 10.1109/TNSRE.2023.3339518.
A. Thakkar, A. Gupta, and A. De Sousa, “Artificial intelligence in positive mental health: a narrative review,†Front. Digit. Health, vol. 6, Mar. 2024, doi: 10.3389/fdgth.2024.1280235.
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