Comparative Study of Chatbot Architectures Using LLMs and RAG: Evaluating FAISS-Based Knowledge Retrieval Schemes Herlawati Herlawati, Ben Rahman, Rahmadya Trias Handayanto, Prima Dina Atika, Dwipa Handayani, Maimunah Maimunah, Endang Retnoningsih, Syahbaniar Rofiah, Didik Setiyadi 2025 10th International Conference on Informatics and Computing Icic 2025, 2025 The use of Large Language Models (LLMs) for chatbot applications is currently widespread. The availability of various models with specific characteristics tailored to different needs has made these models increasingly popular. However, challenges remain, particularly the high computational resource requirements, such as the need for a Graphics Processing Unit (GPU). This has led to the need for optimization through emerging technologies, one of which is Retrieval-Augmented Generation (RAG). RAG allows users to avoid resource-intensive fine-tuning processes. Several RAG methods are already in use, such as Facebook AI Similarity Search (FAISS), Weaviate, Elasticsearch, Qdrant, etc., but the implementation techniques vary widely and still require experimentation to achieve optimal performance while minimizing computational demands. This is especially important considering that chatbots are often integrated into web servers, which are typically focused on web programming rather than artificial intelligence (AI). This study evaluates several chatbot schemes utilizing RAG with a FAISS index for a Question and Answer (Q/A)-based chatbot. The model used is a lightweight one with minimal GPU requirements, namely Gemma:2b-Instruct. The tested schemes include: (i) a single FAISS index, (ii) a single FAISS index with special chunking, (iii) two FAISS indices handling Q/A and non-Q/A in a serial manner, and (iv) two FAISS indices handling Q/A and non-Q/A in parallel. Experimental results show that using special chunking to handle Q/A improves accuracy, and the use of two FAISS indices in parallel minimizes the dominance of the Q/A index over the non-Q/A index.
Improving ShuffleNet and SqueezeNet Using Residual Network for Small Device Herlawati Herlawati, Ben Rahman, Rahmadya Trias Handayanto, Prima Dina Atika, Dani Yusuf, Maimunah Maimunah, Endang Retnoningsih, Syahbaniar Rofiah, Didik Setiyadi 2024 9th International Conference on Informatics and Computing Icic 2024, 2024 The development of technology, particularly hardware, has been very rapid lately. Small devices, which previously required limited memory and processor speed, now have capacities and performance comparable to mid-range devices. As a result, some deep learning research that was too focused on minimal size needs to be revised. Some deep learning models suitable for small capacities but needing performance enhancement have become the focus of this research. Two models, SqueezeNet and ShuffleNet, will enhance their performance by inserting residual networks (ResNet) to improve performance. To objectively determine the effects of adding the ResNet module, both the data and hyperparameters, e.g., learning rate, number of epochs, etc., are kept the same. Data is taken from benchmark data with 978 training images across 9 classes. The test results show that the combination of ShuffleNet with ResNet outperforms SqueezeNet with ResNet.
Mobile-based Augmented Reality for Historic- Building Lessons in West Java Syahbaniar Rofiah, Dadan Sukma, Rahmadya Trias Handayanto, Herlawati, Solikin, Didik Setiyadi Proceedings of 2019 4th International Conference on Informatics and Computing Icic 2019, 2019 Learning media is currently important in education at the elementary and higher education level. Teacher should choose the right learning media with a good curriculum for students. Current technology can be utilized to support learning media. New education material, such as video or audio can be added. One kind of learnings that can be delivered with the video and audio media is about historic building. Historic buildings are cultural and historic heritage that must be preserved for the need for students to find information about historic buildings, names, locations and their historic story. The study proposed the mobile-based learning media of historic building. The development method used in this study was Rapid Application Development (RAD). A blueprint for learning media of historic buildings in west Java in this study might help the local governments to share historic buildings.
Knowledge Management System for Supporting the Small Medium Enterprise (UMKM) in Bekasi City Endang Retnoningsih, Fata Nidaul Khasanah, Rahmadya Trias Handayanto, Herlawati, Syahbaniar Rofiah, Solikin Proceedings of 2019 4th International Conference on Informatics and Computing Icic 2019, 2019 “Usaha Mikro Kecil Menengah” (UMKM), a kind of Small Medium Enterprise in Indonesia, has become an important part of the national economic system. Developing UMKM is a strategic task to drive the national economy because its business activities cover almost all business fields. One of the problems faced by UMKM is the lack of knowledge and skills to manage the business. Such a human resource problem should be addressed for increasing the competitiveness in the market today. The article proposed a knowledge management system (KMS) development method using the Amrit Tiwana framework in which some stages should be conducted, i.e. infrastructure evaluation stages, knowledge system analysis, team knowledge management design, the development of knowledge management system, prototype deployment, and evaluation.
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