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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) 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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Journal : Media Jurnal Informatika

 1 
Development of a Scientific Article Recommendation Web Application Using a Hybrid Recommender System: A Case Study in Computer Science Hodijah, ade
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. The resulting application provided multiple recommendation features by combining content analysis and user preferences. This application is expected to help researchers, students, and practitioners find relevant references more effectively.
Application of Named Entity Recognition (NER) in Job Vacancy Matching Using an Ontology-Based Approach (Case Study: Information Technology Sector) Hodijah, ade
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.
Co-Authors Ade Chandra Nugraha Arsyad, Zulkifli Asri Maspupah Carolina M.L. Dzakira Fabillah Ediana Sutjiredjeki Ediana Sutjiredjeki Farid N., Moh. Firdaus, Lukmannul Hakim Gelar, Trisna Ghifari Munawar Habinuddin, Endang Hashri Hayati Hayati, Hashri Junaedi, NS Juniwati, Endang Hatma Lasambouw, Carolina M Moh. Farid Najib Muhammad Riza Alifi Neneng Nuryati Nuryati, Neneng S, Waway Qodratulloh Santi Sundari Sari, Aprianti Nanda Setijohatmo, Urip Teguh Sri N. Yuliyawati Suhartini, Ida Sumeru Sumeru, Sumeru Urip Teguh Setijohatmo Wulan, Sri Ratna Yuliawati, Sri N