Klasterisasi Pola Gejala Depresi Menggunakan Agglomerative Hierarchical Cluster Analysis Berdasarkan Skor Depresi PHQ-9
DOI:
https://doi.org/10.55606/juisik.v5i2.993Keywords:
AHC, Clustering, Depression, Mental Health, PHQ-9Abstract
Mental health issues such as stress, anxiety, and depression are increasingly affecting individuals across various levels, from mild to severe. Despite this, public perception often underestimates these conditions, particularly depression, which is frequently misunderstood as a temporary emotional phase. In fact, depression is a serious mental disorder that requires proper attention and treatment. This study aims to classify early patterns of depressive symptoms using the Agglomerative Hierarchical Clustering (AHC) algorithm. Data were collected from 1,030 respondents using the PHQ-9 questionnaire, a widely recognized tool for assessing depression levels. The analysis was conducted using the AHC method, and cluster evaluation was performed using the Davies-Bouldin Index (DBI) to determine optimal clustering. Results indicate that the Complete Linkage method provided the most optimal performance, as reflected in the highest Cophenetic Correlation Coefficient compared to other linkage methods. The DBI evaluation suggests that the optimal number of clusters is four. Among the respondents, 17.2% fall into cluster 1, 32.5% into cluster 2, 21.9% into cluster 3, and 28.3% into cluster 4. These clusters represent distinct patterns of depressive symptoms, which can be used for early detection and improved mental health interventions.
References
Aan, A., Permana, J., Putu, N., & Puspa, N. (2024). Pengumpulan data tweet berdasarkan kata kunci depresi dan kisah hidup di kalangan mahasiswa berbasis PHQ-9. [Nama Jurnal Tidak Dicantumkan], 21(1), 24–33.
Dirgayunita, A. (2016). Depresi: Ciri, penyebab dan penangannya. Jurnal An-Nafs: Kajian dan Penelitian Psikologi, 1(1), 1–14. https://doi.org/10.33367/psi.v1i1.235
Fatimah, N. (2014). Desain studi kasus. Fakultas Kedokteran UIN Jakarta.
Firdaus, R. D., Laksana, T. G., & Ramadhani, R. D. (2019). Pengelompokan data persediaan obat menggunakan perbandingan metode K-means dengan hierarchical clustering single linkage. Jurnal Informatics, Information System, Software Engineering and Applications, 2(1), 33–48.
Hendra Perdana, N. A. N. S. (2019). Pencarian cluster optimum pada single linkage, complete linkage dan average linkage. Bimaster: Buletin Ilmiah Matematika, Statistik dan Terapannya, 8(3), 393–398. https://doi.org/10.26418/bbimst.v8i3.33173
Indriyaningrum, N. B., Brahmantyo, H. P., & Mustika, S. (2017). Derajat depresi pasien hepatitis C kronis yang mendapat terapi PegIFN-α. Jurnal Kedokteran Brawijaya, 29(3), 234–237. https://doi.org/10.21776/ub.jkb.2017.029.03.9
Kartikasari, M. D. (2021). Self-organizing map menggunakan Davies-Bouldin Index dalam pengelompokan wilayah Indonesia berdasarkan konsumsi pangan. Jambura Journal of Mathematics, 3(2), 187–196. https://doi.org/10.34312/jjom.v3i2.10942
Maulana, M. R., & Al Idrus, S. I. (2023). Sistem pakar untuk mengukur tingkat depresi mahasiswa menggunakan metode Fuzzy Sugeno. Ocean Engineering: Jurnal Ilmu Teknik dan Teknologi Maritim, 2(1), 37–50.
Miharja, M., & Adhkar, S. (2022). Implementasi chatbot deteksi depresi dini pada mahasiswa dengan PHQ-9 (Patient Health Questionnaire) menggunakan NLP (Natural Language Processing). Prosiding Saintek, 1(1), 103–108.
Prayitno, E., Tarigan, N., Sukmawaty, W., & Mauidzoh, U. (2022). Gangguan mental emosional dan depresi pada remaja. Kebangkitan UMKM Pascapandemi Covid-19, 2(4), 4787–4794. https://www.bajangjournal.com/index.php/J-ABDI/article/view/3641/2684
Seino, Y., et al. (2018). A cluster analysis of bronchial asthma patients with depressive symptoms. Internal Medicine, 57(14), 1967–1975. https://doi.org/10.2169/internalmedicine.9073-17
Senjaya, A. A., et al. (2023). Increasing awareness of depression in adolescents and children through online campaign activities. Jurnal Layanan Masyarakat (Journal of Public Service), 7(3), 326–331. https://doi.org/10.20473/jlm.v7i3.2023.326-331
Suhirman, S., & Wintolo, H. (2019). System for determining public health level using the agglomerative hierarchical clustering method. Compiler, 8(1), 95. https://doi.org/10.28989/compiler.v8i1.425
Tamara, R. (2023). Data mining penentuan jurusan siswa menggunakan metode agglomerative hierarchical clustering (AHC). Jurnal Media Informatika Budidarma, 7(2), 873–880. https://doi.org/10.30865/mib.v7i2.6092
Wijaya, A. E., Asmin, E., & Saptenno, L. B. E. (2023). Tingkat depresi dan ansietas pada usia produktif. Jurnal Ilmiah Kesehatan Sandi Husada, 12(1), 150–156.
Wulandari, S. (2023). Clustering Indonesian provinces on prevalence of stunting toddlers using agglomerative hierarchical clustering. Faktorexacta, 16(2). https://doi.org/10.30998/faktorexacta.v16i2.17186
Yoduke, F., Daulima, N. H., & Mustikasari, M. (2023). Peran guru PAI dalam bimbingan dan konseling terhadap pembentukan akhlak siswa di sekolah dasar. Alauddin Scientific Journal of Nursing, 4(1), 16–24.
Yusuf, M., & Wahyu, A. (2022). Analysis of the grouping of provinces in Indonesia according to the democracy index with the agglomerative hierarchical clustering algorithm. [Nama Jurnal Tidak Dicantumkan], 15, 27–35.
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