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Journal of Information Technology and Computer Engineering
Published by Universitas Andalas
ISSN : 25991663     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Journal of Information Technology and Computer Engineering (JITCE) is a scholarly periodical. JITCE will publish research papers, technical papers, conceptual papers, and case study reports. This journal is organized by Computer System Department at Universitas Andalas, Padang, West Sumatra, Indonesia.
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Articles 2 Documents
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Search results for , issue "Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering" : 2 Documents clear
Evaluating IndoGPT for Legal Queries: A Benchmark Against GPT-4 Models Palupi, Ade Cahyaning; irawan, ade
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

This study evaluates a chatbot developed with the Large Language Model (LLM) IndoGPT, focusing on its use of Retrieval-Augmented Generation (RAG) to answer questions about university regulations from legal PDF documents in the Indonesian Language. IndoGPT's performance is benchmarked against the more advanced models, GPT-4 and GPT-4o. The chatbot combines RAG techniques with the LangChain framework, and its effectiveness is assessed using the Retrieval-Augmented Generation Assessment (RAGAS) framework. The dataset includes publicly available legal documents from Universitas Pertamina, with test data created by the authors. IndoGPT consistently underperforms compared to GPT-4 and GPT-4o. GPT-4 achieves superior metrics with Context Precision at 0.9027, Context Recall at 0.8693, Faithfulness at 0.8486, and Answer Relevancy at 0.8142. Similarly, GPT-4o delivers strong results with Context Precision at 0.8896, Context Recall at 0.8594, Faithfulness at 0.8804, and Answer Relevancy at 0.8773. In contrast, IndoGPT shows significant deficiencies, with much lower scores: Context Precision at 0.6687, Context Recall at 0.5711, Faithfulness at 0.0738, and Answer Relevancy at 0.1628. These findings highlight IndoGPT's substantial limitations, especially when compared to GPT-4 and GPT-4o, which excel in providing accurate, contextually relevant answers. The GPT-4-based chatbot demonstrates strong capabilities in understanding user queries and delivering accurate responses while effectively reducing hallucinations through the RAG technique.
Development of a Multi-Task Learning CNN Model for Pneumonia Detection and Pathogen Classification Based on Medical Images Harahap, Aris Munandar; Samosir, Khairunnisa
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Pneumonia is one of the leading causes of death from respiratory tract infections worldwide. Early detection and identification of the causative pathogen are crucial for determining appropriate treatment. This study aims to develop a Convolutional Neural Network (CNN) model based on Multi-Task Learning (MTL) to simultaneously detect pneumonia and classify the type of pathogen through chest X-ray images. The model architecture uses a shared convolutional layer for feature extraction, which then branches into two classification paths. The model was trained using a dataset of X-ray images labeled with disease status and pathogen type, with two loss functions optimized simultaneously. Based on the training process and model architecture design, the estimated accuracy achieved is approximately 92% for pneumonia detection and 89% for pathogen type classification. These results indicate that the CNN-MTL approach is effective and efficient in simultaneously addressing two clinical tasks. The proposed model has the potential to be applied as a clinical decision support system, particularly in healthcare facilities with limited resources.

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