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All Journal JSAI (Journal Scientific and Applied Informatics) Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma Journal of Elektronik Sistem InformasI
Budy Santoso, Cahyono
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Evaluasi Metode Retrieval pada Chatbot Domain Khusus Berbasis Retrieval-Augmented Generation Asmaidin, Asmaidin; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9897

Abstract

This study evaluated retrieval methods in the implementation of a domain-specific chatbot based on Retrieval-Augmented Generation to improve information accuracy and relevance while reducing hallucination risks. The primary problem addressed was the incorrect selection and prioritization of contextual documents in chatbot systems built on large language models, particularly in technical domains. An experimental approach was applied by comparing three retrieval strategies: lexical retrieval based on term frequency–inverse document frequency, semantic retrieval using vector representations, and a hybrid retrieval method combining lexical and semantic signals. System performance was measured using Recall at different ranking thresholds and Mean Reciprocal Rank to assess both document discovery and ranking quality. The results demonstrated that lexical retrieval achieved the highest precision at the top-ranked position, while semantic retrieval showed reduced effectiveness due to semantic drift in technical documents. The hybrid approach improved mid-range recall performance but still exhibited ranking ambiguity for top-ranked results. These findings indicated that retrieval quality in Retrieval-Augmented Generation systems depended more on effective ranking and context prioritization than on document availability alone. The study concluded that systematic evaluation of retrieval methods was essential for developing reliable domain-specific chatbots.
Deep Learning-Based Detection of Potato Leaf Diseases Using ResNet-50 with Mobile Application Deployment Budy Santoso, Cahyono; Effendi, Rufman Iman Akbar; Siregar, Johannes Hamonangan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5186

Abstract

Plant diseases significantly reduce agricultural productivity, especially in developing regions with limited access to early detection tools. This research presents a deep learning-based approach for detecting potato leaf diseases, focusing on Early blight, Late blight, and healthy conditions. A modified ResNet-50 architecture was employed and trained using a publicly available potato leaf image dataset. Preprocessing steps included data augmentation and normalization to enhance model generalization. The model achieved a high accuracy of 99.31%, with precision, recall, and F1-score all exceeding 99%, indicating excellent classification performance. This study introduces a novel approach that improves classification performance through an optimized deep learning architecture, achieving higher accuracy compared to existing models. In addition to enhancing predictive capability, the study also addresses the practical need for accessibility by integrating the trained model into an Android-based mobile application. The application allows users to upload or capture leaf images and receive real-time predictions. The interface was designed for simplicity and usability in field conditions, making it accessible to farmers and agricultural workers. The findings demonstrate that combining deep learning with mobile technology can offer an effective and scalable solution for early disease detection in agriculture. Future work may explore cross-crop adaptability and lightweight model optimization for real-time performance on low-resource devices.
Co-Authors Ahmad, Alifio Fikra Ahmad Asmaidin, Asmaidin Bhagawanta, Bajra Effendi, Rufman Iman Akbar Faizul Anwar Ramdhani Ghaniy, Rajib Hafid Roihan Harry Vadilan Sianturi Johannes Hamonangan Siregar Muhammad Mujiburochman Rachma, Mutia Raihan Cikal Herlambang Reyner Shaquille Rachim Vistiyawati, Vanessa