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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 451 Documents
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Analisis Efektivitas Fine-Tuning dan Prompt Engineering Berbasis Llama 3.1 pada Deteksi Depresi di Media Sosial Muhammad Ikhsan Asagaf; Junta Zeniarja
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v%vi%i.10170

Abstract

Detecting depression has become an important concern in addressing mental health issues. According to WHO, more than 300 million people suffer from depression. Large Language Models offer great potential to address this issue, however the full fine-tuning process is often hampered by heavy computational requirements, and LLMs that are not specifically configured for a particular context can result in biased and inaccurate outcomes. This study aims to analyze the effectiveness of Prompt Engineering and Fine-Tuning using QLoRA in improving the accuracy of depression detection. Utilizing the Llama-3.1-8B-Instruct model on social media datasets, this research compares model performance in two scenarios consist of the application of direct prompting strategies on the base model and the application of QLoRA fine-tuning. Evaluation results demonstrate that the Chain-of-Thought strategy improved baseline accuracy from 81.4% to 84.4%, but still exhibited significant bias towards the 'Severe' class. In contrast, the QLoRA Fine-Tuning approach proved superior, achieving 92.4% accuracy with balanced F1-Scores across classes, effectively eliminating detection bias in the 'Minimum' class. These findings confirm that while prompting techniques can enhance baseline performance, QLoRA provides a more accurate, stable, and objective solution for depression detection tasks.

Page 46 of 46 | Total Record : 451

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