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All Journal JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal Pendidikan Informatika dan Sains JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Mosharafa: Jurnal Pendidikan Matematika Bulletin of Information Technology (BIT) Jurnal Nasional Teknik Elektro dan Teknologi Informasi PELS (Procedia of Engineering and Life Science) Proceedings of The ICECRS Jurnal Algoritma SAGA: Journal of Technology and Information Systems Physical Sciences, Life Science and Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Electrical Engineering semanTIK IJHCS Academia Open
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Program Studi Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Muhammadiyah Sidoarjo
Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Journal : ijhcs

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Implementation of an Academic Information Chatbot Based on Retrieval-Augmented Generation (RAG) Using LLaMA 3.1 Mochamad Alfan Rosid; Muhammad Saddam Heykal Bustomy; Suprianto; Hamzah Setiawan
International Journal on Human-Computing Studies Vol. 8 No. 1 (2026): International Journal of Human Computing Studies (IJHCS)
Publisher : Research Parks Publishers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v8i1.5768

Abstract

Providing fast and accurate academic information is a fundamental requirement for universities. However, users often experience difficulties in efficiently searching for specific information on study program websites. This study aims to develop a Retrieval-Augmented Generation (RAG)-based academic information chatbot that is directly integrated with the Informatics Study Program website at Muhammadiyah University of Sidoarjo. The system was built using the LangGraph architecture, ChromaDB vector database, and LLaMA 3.1 model through the Groq API. Testing was conducted using the RAGAS framework to assess the quality of answers and load testing for system performance. The results showed that the system achieved a Context Precision score of 0.86 and a Faithfulness score of 0.77, indicating high relevance and accuracy of answers. In addition, the implementation of the Groq API resulted in an average response latency of 2.11 seconds with a 94% success rate in load testing. These findings indicate that the RAG-based chatbot is effective in overcoming website limitations in delivering academic information at the Informatics Study Program at Muhammadiyah University of Sidoarjo.
Classification of Sentiment in Reddit Forum Comments using Fine-Tuned IndoBERT (Case Study: Free Nutritious Meal Program) Bram Aji Saka Putra; Suprianto
International Journal on Human-Computing Studies Vol. 8 No. 2 (2026): International Journal of Human Computing Studies (IJHCS)
Publisher : Research Parks Publishers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v8i2.5772

Abstract

This study analyzes public opinion on the Free Nutritious Meals Program (MBG) policy on the Reddit platform using the IndoBERT model. The study uses a dataset of 6,295 Reddit comments collected from 2024 to 2025. The preprocessing stage implemented a comprehensive suite of textual refinement procedures, encompassing normalization of colloquial language, emoji conversion, and translation into Indonesian to establish linguistic consistency across the corpus. Manual annotation was performed with meticulous care on a balanced dataset of 3,000 comments, evenly allocated among positive, negative, and neutral classes. An 80:20 train–test split was applied to enhance the reliability of model training and evaluation. A fine‑tuned IndoBERT model exhibited outstanding performance, attaining 99.00% for accuracy, F1‑score, and precision. Applied to the full dataset, the model predicted neutral sentiment as predominant (59.44%), followed by positive (34.11%) and negative (6.45%) sentiments, a distribution that suggests a measured and reflective public discourse on the topic. Model reliability was further supported by a mean confidence score of 0.8502 and a processing throughput of 137.93 samples per second, indicating strong potential for deployment as an effective tool for near real‑time sentiment monitoring in public policy contexts.
Co-Authors Ade Eviyanti Adisya Ayustina andhika andhika Andre Aditya Pradana Annasdyto Virlib Aprillio Ardhian Ramadhan Arga Pradana Arif Senja Fitrani Azmuri Wahyu Azinar Bagus Permadi Putra Bram Aji Saka Putra Bramatya Mahendra V.N Candra Iswayudi Cindy Taurusta Ditta Adelia Enggi Sabrilla Assara fahrezi auliasyafa Fatahillah Aji Maulana Givari Eka Fajar Hamzah Setiawan Hanif Amrullah Heral Reynaldo Hindarto Illusyia Insyiroh Irwan Alnarus Kautsar Ivan Aries Rizaldy M. Purnomo Adji Saputro Mochamad Alfan Rosid Mochammad Naufal Nibros Muchamat David Arifudin Muchammad Luthfi Affandy Muhamad Maulana Musafir Muhammad Fahruddin Mahfudhoni Muhammad Hafizh Fattah Muhammad Saddam Heykal Bustomy Nofitasary, Dina Novia Ariyanti Nuril Lutvi Azizah Priyo Wahyu Setiyo Aji Pudjianto, Hendrawan Ricky Wahyu Setyawan Rieko Dita Hermawati Rizal Nur Ramadhan Rizky Bayu Wardana Rohman Dijaya Royhan Faradis Sasmito Bagus Sumadyo Shafa Hayu Apsari Shovi Firdaus Sukma Aji Sumarno Sumarno Syahfrizka Dyah Nazwa Umbara Uce Indahyani Uce Indahyanti Wahidiyah Kurniawati Yulian Findawati Yunianita Rahmawati Yunianita Rahmawati Yunita Setiyaningsih Zulham Efendi, Muhammad