Analisa Tweet Mahasiswa untuk Deteksi Gejala Depresi dengan Penerapan Natural Language Processing
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Mental health issues are increasingly gaining attention, with depression being a primary factor linked to high suicide rates caused by psychological disorders. College students are identified as a vulnerable group to depression and anxiety, which can be triggered by various factors. Meanwhile, individuals self-expression on social media, especially on platform X (Twitter), which offers freedom of expression, is considered reflective of one’s mental well-being. This study aims to explore the analysis of college students tweets using a Natural Language Processing (NLP) approach to detect depressive symptoms through linguistic patterns. Data was collected via crawling techniques using keywords such as “depression”, “stress”, and “burnout” resulting in 24,167 tweets from January to March 2025. After data cleaning, 8,308 tweets remained. Sentiment labeling using the Inset Lexicon shows that 68.1% (5,663 tweets) were labeled negative, reflecting college students tendency to use platform X as a medium to express negative emotions. The Random Forest model integrated with TF-IDF feature extraction achieved 87.51% accuracy, demonstrating its capability to address majority class bias (negative) and capture the morphological complexity of informal language. The implications of the research encourage the development of a digital monitoring system for the proactive detection of college college students depression symptoms. The lexicon’s limitations in incorporating informal vocabulary (slang) becomes a recommendation for further research to enhance analysis accuracy.
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Apriliani, F., & Maharani, W. (2023). Depression Detection on Social Media Twitter using XLNet Method. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 8(1), 172-180. https://doi.org/10.29100/jipi.v8i1.3345.
Cahyono, A. S. (2016). Pengaruh media sosial terhadap perubahan sosial masyarakat di Indonesia. Publiciana, 9(1), 140-157.
Distina, P. P., & Kumail, M. H. (2019). Pengembangan dimensi psychological well-being untuk pengurangan risiko gangguan depresi. Jurnal dakwah dan pengembangan sosial kemanusiaan, 10(1), 39-59.
Erma, H. N., Santika, D., Nurhasanah, I., & Lestari, I. (2022). Dampak Media Sosial Terhadap Konflik Di Masyarakat. EDUSOSHUM: Journal of Islamic Education and Social Humanities, 2(1), 12-20. https://doi.org/10.52366/edusoshum.v2i1.33.
Fachriza, M., & Munawar, M. (2023). Analisis Sentimen Kalimat Depresi Pada Pengguna Twitter Dengan Naive Bayes, Support Vector Machine, Random Forest. KOMPUTEK, 7(2), 49-58.
Fahma, F., & Safitri, D. (2024). Dinamika Identitas Budaya dalam Era Globalisasi: Tantangan dan Kesempatan Media Sosial terhadap Budaya Masyarakat Lokal: Dynamics of Cultural Identity in the Era of Globalization: Challenges and Opportunities for Social Media on Local Community Culture. Jurnal Intelek Dan Cendikiawan Nusantara, 1(3), 3675-3682.
Gifari, O. I., Adha, M., Freddy, F., & Durrand, F. F. S. (2022). Film Review Sentiment Analysis Using TF-IDF and Support Vector Machine. Journal of Information Technology, 2(1), 36-40. https://doi.org/10.46229/jifotech.v2i1.330.
Musfiroh, D., Khaira, U., Utomo, P. E. P., & Suratno, T. (2021). Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon: Sentiment Analysis of Online Lectures in Indonesia from Twitter Dataset Using InSet Lexicon. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 1(1), 24-33. https://doi.org/10.57152/malcom.v1i1.20.
Mutmainah, S. (2022). Kemungkinan Depresi Dari Postingan Pada Sosial Media. Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, 1(2), 82-88. https://doi.org/10.20885/snati.v1i2.11.
Putra, D. E., Nelwati, N., & Fernandes, F. (2023). Hubungan depresi, stres akademik dan regulasi emosi dengan ide bunuh diri pada mahasiswa. Jurnal Keperawatan Jiwa, 11(3), 689-706.
Rahayu, K., Fitria, V., Septhya, D., Rahmaddeni, R., & Efrizoni, L. (2023). Klasifikasi Teks untuk Mendeteksi Depresi dan Kecemasan pada Pengguna Twitter Berbasis Machine Learning: Text Classification for Detecting Depression and Anxiety among Twitter Users based on Machine Learning. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 3(2), 108-114. https://doi.org/10.57152/malcom.v3i2.780.
Rizqi, B. R. T., & Heriyanto, H. (2023). Penyebaran Informasi melalui Thread Berita di Twitter oleh Mahasiswa S-1 Program Studi Ilmu Perpustakaan Universitas Diponegoro. Anuva: Jurnal Kajian Budaya, Perpustakaan, dan Informasi, 7(3), 515-528. https://doi.org/10.14710/anuva.7.3.515-528.
Rusly, N. F., Qoni’ah Nur Wijayanti, S. I., & Ikom, M. (2023). PERILAKU SELF DISCLOSURE PADA KALANGAN REMAJA MELALUI MEDIA SOSIAL TWITTER. Jurnal Media Akademik (JMA), 1(1).
Santoso, N. N., Maulita, Y., & Khair, H. (2022). Sistem Pakar Diagnosa Tingkat Depresi Mahasiswa Tingkat Akhir Dengan Menggunakan Metode Certainty Factor. Bull. Multi, 1, 732-740.
Tamaraya, A., & Ubaedullah, D. (2021). Dampak Penggunaan Twitter Terhadap Pengungkapan Diri Mahasiswa. INTERAKSI PERADABAN: Jurnal Komunikasi dan Penyiaran Islam, 1(1).
Zaskya, M., Boham, A., & Lotulung, L. J. H. (2021). Twitter sebagai media mengungkapkan diri pada kalangan milenial. Acta Diurna Komunikasi, 3(1).
Zhu, E., Zhang, J., Yan, J., Chen, K., & Gao, C. (2022). N-gram MalGAN: Evading machine learning detection via feature n-gram. Digital communications and networks, 8(4), 485-491.