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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JADECS (Journal of Art, Design, Art Education and Culture Studies) Sinkron : Jurnal dan Penelitian Teknik Informatika Media Jurnal Informatika Jurnal Difusi Jurnal Pengabdian Masyarakat IPTEK Tedc Journal Of Artificial Intelligence And Software Engineering JGGAG (Journal of Games, Game Art, and Gamification)
Ade Hodijah
Politeknik Negeri Bandung
Religion Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Veterinary Other

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Development of a Scientific Article Recommendation Web Application Using a Hybrid Recommender System: A Case Study in Computer Science Az-zahra, Adinda Raisa; Prawira, Alfien Sukma; Nugraha, Mahesya Setia; Hodijah, Ade; Setijohatmo, Urip Teguh; Setiarini , Siti Dwi; Wisnuadhi , Bambang
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5672

Abstract

The rapid increase in scientific publications has created significant challenges for researchers in finding relevant literature. Conventional citation-based recommender applications often have drawbacks, such as bias toward popular articles and vulnerability to manipulation through citation cartels, which reduce objectivity. To address these limitations, this development aimed to design and develop a web-based scientific article recommendation application using a hybrid recommender system approach. The development followed the waterfall methodology, covering requirements analysis, design, implementation, and testing stages. The hybrid approach combines Content-based filtering by analyzing content similarity and Collaborative filtering based on user interaction history. Scientific articles and user preferences were modeled in a graph database to map relationships, with the implementation of Graph Data Science Library using algorithms named K-Nearest Neighbor, Degree centrality, and PageRank.  Based on 101 black-box unit test cases, the application successfully delivered three main recommendation features by integrating content analysis—based on access history and currently viewed articles—with user preference modeling through peer institutions. The testing results confirm that all recommendation functions operated as intended across various user scenarios.  Overall, the developed application provides multiple recommendation features that enhance objectivity and relevance, supporting researchers, students, and practitioners in discovering
Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector) Gunawan , Rizki; Hodijah, Ade; Taqwim , Muhammad Ikhsan Maulana; Siti Ababil , Afyar; Setijohatmo, Urip Teguh; Wulan, Sri Ratna; Alifi , Muhammad Riza; Sari , Aprianti Nanda; Hayati, Hashri
Media Jurnal Informatika Vol 17 No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5675

Abstract

The dissemination of job vacancies through online platforms still faces limitations in understanding the semantic relationships between the skills possessed by job seekers and the qualifications required by a job position. This mismatch results in an inefficient search process and longer search times. This study aims to develop a semantic-based job vacancy recommendation application (talent matching) using a skill ontology approach. One of the main challenges in developing the ontology is the lack of standardized data structures in job vacancy postings, particularly in the job description section. To address this issue, Named Entity Recognition (NER) techniques are applied to automatically extract skill entities from job description texts. The extracted results are then classified into a taxonomy structure using SkillsGPT, thereby forming a hierarchical skill concept model semantically represented within the ontology using Protégé. The matching process between user skills and job qualifications is conducted through semantic similarity calculations employing the Sánchez Similarity method. Job vacancy data are collected via web scraping, while system development follows the Rational Unified Process (RUP) methodology and is evaluated using Black Box testing. Evaluation results demonstrate that the developed system is capable of providing semantically relevant job vacancy recommendations according to the user's skill profile. Therefore, this study contributes both theoretically and practically to the development of ontology-based recommendation systems, particularly in the automated modeling of skill taxonomies from unstructured data.
Evaluating RAG Performance on Small Language Models for Low-Resource Devices through Chunking and Retrieval Methods Agustiani, Amelia Dewi; Putri, Salsabila Maharani; Hutahaean, Jonner; Sholahuddin, Muhammad Rizqi; Alifi, Muhammad Riza; Hodijah, Ade
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1733

Abstract

Retrieval-Augmented Generation (RAG) combines generative capabilities of language models with external document retrieval to answer questions grounded in reference texts. However, deploying RAG on low-resource devices like Android smartphones is challenging because SLMs have limited computational capacity and depend heavily on efficient chunking and retrieval. Although interest in on-device processing is growing, research on RAG configurations for SLMs under strict resource constraints especially for domain-specific tasks remains limited. This study therefore investigates which combinations of chunking technique, chunk size, overlap, and retrieval strategy best balance accuracy and speed on low-resource devices. The evaluation uses 148 Indonesian questions sourced from an official Hajj guidebook. The study consists of two phases retrieval and generation. Retrieval is evaluated using BLEU, ROUGE-L, MRR, MAP, and Hit@k, while answer quality is measured with BERTScore. The experiments compare different chunking methods (fixed-size or semantic), chunk sizes (128 or 256 tokens), overlaps (25, 50 and 100 tokens), and retrieval methods (dense, sparse, or hybrid). Results show that sparse retrieval with 256-token chunks and 100-token overlap yields the best answer quality (F1 = 0.726). However, 128-token chunks with the same overlap provide the fastest generation time (69.737 seconds). The main contribution of this study is a systematic evaluation of RAG configurations for fully on-device SLMs using a domain-specific Hajj and Umrah dataset not explored in prior research. The findings provide practical guidance for designing efficient and accurate RAG-based question-answering systems on low-resource devices.
Co-Authors Ade Chandra Nugraha Agustiani, Amelia Dewi Alifi , Muhammad Riza Arsyad, Zulkifli Asri Maspupah Az-zahra, Adinda Raisa Carolina M.L. Dzakira Fabillah Ediana Sutjiredjeki Ediana Sutjiredjeki Farid N., Moh. Firdaus, Lukmannul Hakim Gelar, Trisna Ghifari Munawar Gunawan , Rizki Habinuddin, Endang Hashri Hayati Hayati, Hashri Jonner Hutahaean Junaedi, NS Juniwati, Endang Hatma Lasambouw, Carolina M Moh. Farid Najib Muhammad Riza Alifi Muhammad Rizqi Sholahuddin Neneng Nuryati Nugraha, Mahesya Setia Nuryati, Neneng Prawira, Alfien Sukma Putri, Salsabila Maharani S, Waway Qodratulloh Santi Sundari Sari , Aprianti Nanda Sari, Aprianti Nanda Setiarini , Siti Dwi Setijohatmo, Urip Teguh Siti Ababil , Afyar Sri N. Yuliyawati Suhartini, Ida Sumeru Sumeru, Sumeru Taqwim , Muhammad Ikhsan Maulana Urip Teguh Setijohatmo Wisnuadhi , Bambang Wulan, Sri Ratna Yuliawati, Sri N