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All Journal Jurnal Teknologi AGROBUN: Jurnal Ilmu Pertanian
Nuraini, Fadilah
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering

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Mental Health Chatbot for Detecting Depression Symptoms Using Natural Language Processing and DASS-21 Nuraini, Fadilah; Najamuddin Dwi Miharja, Muhammad; Anshor, Abdul Halim
Jurnal Teknologi Vol. 17 No. 2 (2025): Jurnal Teknologi
Publisher : Faculty of Engineering Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jurtek.17.2.133-142

Abstract

The prevalence of depressive symptoms among university students continues to rise, driven by academic pressure, social isolation, and limited access to psychological support. Early detection and intervention remain critical challenges in mental health services. This study presents the design and implementation of an intelligent chatbot that integrates the Depression Anxiety Stress Scale-21 (DASS-21) with Natural Language Processing (NLP) techniques to enable non-clinical mental health screening. The chatbot processes user input through intent classification and text preprocessing pipelines to dynamically assess indicators of depression, anxiety, and stress. Utilizing a hybrid rule-based and machine learning architecture, the system provides a self-assessment interface that delivers personalized feedback based on the DASS-21 scoring rubric. Two models were evaluated: a TF-IDF-based Neural Network and a fine-tuned BERT model. The TF-IDF model achieved an accuracy of ninety-one percent with a weighted F1-score of 0.91, while the BERT model outperformed it with an accuracy of ninety-four percent and a weighted F1-score of 0.94. Notably, the BERT model demonstrated a recall of ninety-eight percent in identifying moderate depression cases. However, both models showed limitations in detecting mild depression due to data imbalance. The approach prioritizes usability, anonymity, and accessibility, key factors in promoting help-seeking behavior among young adults. The results demonstrate the potential of NLP-powered conversational agents as scalable, low-cost tools for early detection of mental health risks in academic environments.
ANALISIS DAYA KECAMBAH DAN VIABILITAS KACANG TANAH (Arachis hypogaea L.) Nuraini, Fadilah; Barokah, Umi
Agrobun Vol 2 No 2 (2025): AGROBUN: Juli - Desember
Publisher : LPPM Universitas Tjut Nyak Dhien

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36490/agrobun.v2i2.1938

Abstract

Viabilitas dan vigor merupakan komponen penting dalam penilaian kualitas benih. Sebagai langkah awal dalam usaha pembudidayaan kacang tanah, diperlukan persiapan benih dengan kualitas unggul. Penelitian ini bertujuan untuk menganalisis viabilitas benih kacang tanah melalui pengamatan daya kecambah pada dua perlakuan. Pengamatan dilakukan selama 10 hari dengan parameter yang diamati meliputi daya kecambah, jumlah benih normal, abnormal, dan tingkat kematian benih. Hasil penelitian menunjukkan perbedaan antara kedua perlakuan, dimana perlakuan 1 menghasilkan daya kecambah sebesar 80% sedangkan perlakuan 2 mencapai 72%.
Co-Authors Abdul Halim Anshor Najamuddin Dwi Miharja, Muhammad umi barokah