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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Scientific Journal of Informatics
Agus Subhan Akbar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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QnA Chatbot with Mistral 7B and RAG method: Traffic Law Case Study Muhammad Roiful Anam; Agus Subhan Akbar; Heru Saputro
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 15 No 03 (2024): Vol.15, No. 3 December 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p06

Abstract

Mistral 7B is a language model designed to achieve high efficiency and performance in handling Natural Language Processing (NLP). This research will evaluate the model's effectiveness in legal data processing using the Retrieval-Augmented Generation (RAG) method, focusing on road traffic and transportation law No 22/2009. The system was built using the LangChain framework, followed by fine-tuning the model and evaluated using BERTScore. Results showed that the fine-tuned Mistral 7B achieved an F1 score of 0.9151, higher than the version without fine-tuning (0.8804) and GPT-4 (0.8364). To improve accuracy, the model utilizes specific keywords that make it easier to find relevant data. Fine-tuning was shown to enhance precision, while the use of key elements in questions helped the model focus more on important information. The results are expected to support the development of artificial intelligence (AI) in Indonesia's legal system and provide practical guidance for applying AI technology in other areas of law.
Neural Style Transfer and Clothes Segmentation for Creating New Batik Patterns on Clothing Design Adali, Farhan; Agus Subhan Akbar; Danang Mahendra
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.19554

Abstract

Purpose: Applying the original batik image style to other object images and generating new batik patterns that applied to clothing. Methods: This research uses the Neural Style Transfer method to apply object images to batik to produce new batik patterns, and Clothes Segmentation is used to select areas of clothing in the image so the new batik patterns can be applied to clothing images. And Testing using SSIM, LPIPS and PSNR metrics. This research uses Google Colab, batik image data, and clothing mockup images taken from the internet. Result: This study shows high average results on SSIM, LPIPS and fair results on PSNR. The results show that the similarity is relatively high with high detected noise. Novelty: This research develops a new approach in the field of batik pattern innovation and its application to clothing design images. The novelty of this research lies in the implementation of Neural Style Transfer and Clothes Segmentation, which results in a method of exploring new batik patterns and applying them to clothing design images.
Co-Authors Adali, Farhan Danang Mahendra Heru Saputro Muhammad Roiful Anam