ANALYSIS OF DEPRESSION IN COLLEGE STUDENT DURING COVID-19 PANDEMIC USING EXTREAM GRADIENT BOOST
ANALISIS DEPRESI PADA MAHASISWA SELAMA PANDEMI COVID-19 MENGGUNAKAN EXTREAM GRADIENT BOOST
Keywords:
Depression, PHQ-9, XGBoost
Abstract
The Covid-19 pandemic that spreads in Indonesia causes health, economic, and social problems in the community, including mental health. Of course, this mental health problem also hit students. Seeing these conditions, we conducted research on students of the Faculty of Computer Science, University of Singaperbangsa Karawang using the Patient Health Questionnaire-9 which measures a person's level of depression. In this study, we used Extreme Gradient Boost or XGBoost to classify students' depression tendencies. We break down the dataset into training data and testing data with 4 data sharing combinations, they are 80 : 20, 50 : 50, 90 : 10, 70 : 30. The combination of 90 : 10 data sharing has the best performance with accuracy, precision, recall, and F1-scores respectively 92.86%, 94.29%, 92.86% , and 92.06%. This method also has better performance than K-Nearest Neighbor, Random Forest, Multi Layer Perception, Support Vector Machine and Decision Tree .
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Budiastuti, Dyah & Bandur, A. (2018). Validitas Dan Reliabilitas Penelitian. Mitra Wacana Media. https://www.mitrawacanamedia.com/validitas-dan-reliabilitas-penelitian
Chen, T. et al. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/https://doi.org/10.1145/2939672.2939785
Cherif, I. L. et al. (2019). On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification. 2019 Wireless Days (WD), 1–6. https://doi.org/10.1109/WD.2019.8734193
Ghozali, I. (2013). Aplikasi Analisis Multivariate Dengan Program IBM SPSS 21 Update PLS Regresi. Badan Penerbit Universitas Diponegoro. https://mikroskil.ac.id/pustaka/index.php?p=show_detail&id=7026&keywords
Gullo, F. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62, 18–22. https://doi.org/10.1016/j.phpro.2015.02.005
Handayani, A., Jamal, A., & Septiandri, A. A. (2017). Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi (JNTETI), 6(4). https://doi.org/10.22146/jnteti.v6i4.350
Hasanah, U., Fitri, N. L., Supardi, S., & PH, L. (2020). Depression Among College Students Due to the COVID-19 Pandemic. Jurnal Keperawatan Jiwa, 8(4), 421. https://doi.org/10.26714/jkj.8.4.2020.421-424
Karo, I. M. K. et al. (2020). A Hybrid Classification Based on Machine Learning Classifiers to Predict Smart Indonesia Program. 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), 1–5. https://doi.org/10.1109/ICVEE50212.2020.9243195
Karo, I. M. K. et al. (2016). Spatial clustering for determining rescue shelter of flood disaster in South Bandung using CLARANS Algorithm with Polygon Dissimilarity Function. In 2016 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA), 70–75. https://doi.org/10.1109/ICMSA.2016.7954311
Kemdikbud, Pdd. (2021). Profil Perguruan Tinggi Universitas Singaperbangsa Karawang. https://pddikti.kemdikbud.go.id/data_pt/QzRENDE1QjMtOTgyRS00MDM0LUIyOUEtRUU5NDA4MDY4MUEw
Kusuma, M. D. S. et al. (2021). TINGKAT GEJALA DEPRESI PADA MAHASISWA PROGRAM STUDI SARJANA KEPERAWATAN DI INSTITUTE TEKNOLOGI DAN KESEHATAN (ITEKES) BALI. Jurnal Riset Kesehatan Nasional, 5(1), 29–34. https://doi.org/http://dx.doi.org/10.37294/jrkn.v5i1.310
Kusuma, P. D., Marchira, C. R., & Prawitasari, S. (2018). Patient health questionnaire-9 (PHQ-9) efektif Untuk mendeteksi risiko depresi postpartum. Jurnal Keperawatan Respati Yogyakarta, 5(3), 428–433.
Lempang, G. F., Walenta, W., Rahma, K. A., Retalista, N., Maluegha, F. J., & Utomo, F. I. P. (2021). Depresi Menghadapi Pandemi Covid-19 pada Masyarakat Perkotaan (Studi Literatur). Pamator Journal, 14(1), 66–71. https://doi.org/10.21107/pamator.v14i1.9854
Martin, M., & Nilawati, L. (2019). Recall dan Precision Pada Sistem Temu Kembali Informasi Online Public Access Catalogue (OPAC) di Perpustakaan. Paradigma - Jurnal Komputer Dan Informatika, 21(1), 77–84. https://doi.org/10.31294/p.v21i1.5064
Muslim, I., & Karo, K. (2020). Implementasi Metode XGBoost dan Feature Importance untuk Klasifikasi pada Kebakaran Hutan dan Lahan. Journal of Software Engineering, Information and Communication Technology, 1(1), 10–16.
Perhimpunan Dokter Spesialis Kedokteran Jiwa Indonesia. (2020). Masalah Psikologis Di Era Pandemi Covid-19. http://www.pdskji.org/home
Satuan Tugas Penanganan Covid-19. (2021). Peta Sebaran Kasus Covid-19 Di Indonesia. https://covid19.go.id/peta-sebaran
Sugiyono. (2014). Statistik untuk Penelitian (E. Mulyatiningsih (ed.)). Alfabeta. https://adoc.pub/statistik-untuk-penelitian.html
Published
2021-09-15
How to Cite
Prabowo, A., Nur Rois, D., Luthfi, A., & Enri, U. (2021). ANALYSIS OF DEPRESSION IN COLLEGE STUDENT DURING COVID-19 PANDEMIC USING EXTREAM GRADIENT BOOST. Jurnal Techno Nusa Mandiri, 18(2), 87-94. https://doi.org/10.33480/techno.v18i2.2399
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