Model Rekomendasi Jurnal dengan Algoritma Jaccard Similarity dan Protokol OAI-PMH
DOI:
https://doi.org/10.52436/1.jpti.637Kata Kunci:
Journal Finder, OAI-PMH, Open Journal SystemAbstrak
Kesulitan peneliti dalam menemukan jurnal yang sesuai untuk publikasi artikel ilmiah menjadi salah satu hambatan dalam proses diseminasi ilmu pengetahuan. Masalah ini diperburuk oleh kurangnya integrasi antara sistem manajemen jurnal seperti Open Journal System (OJS) dengan layanan pencarian yang efektif. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah aplikasi journal finder yang mampu merekomendasikan jurnal berdasarkan kecocokan antara judul artikel dan abstrak. Metode yang digunakan adalah dengan mengimplementasikan algoritma jaccard similarity untuk menghitung kesamaan antara judul dan abstrak artikel dari input pengguna dengan metadata jurnal yang tersimpan dalam basis data, serta protokol Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) untuk mengumpulkan metadata artikel dari repositori jurnal yang berbasis OJS. Penelitian ini telah berhasil menghimpun sebanyak 59 repository platform OJS, dan 3.321 metadata artikel ilmiah yang disimpan dalam sebuah basis data jurnal. Hasil penelitian menunjukkan bahwa aplikasi journal finder berhasil merekomendasikan jurnal dengan tingkat kemiripan tertentu berdasarkan validasi data uji. Penelitian ini diharapkan dapat membantu para peneliti menemukan jurnal yang relevan secara lebih cepat dan meningkatkan efisiensi proses publikasi artikel ilmiah.
Unduhan
Referensi
M. Miftakul Amin, D. Stiawan, Ermatita, and R. Budiarto, “Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database,” Bull. Electr. Eng. Informatics, vol. 13, no. 3, pp. 2036–2047, 2024, doi: 10.11591/eei.v13i3.7665.
M. Demirkan, A. Özgür, and H. Erdem, “Journal Finder for TRDIZIN: Baseline Study,” in 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021. doi: https://doi.org/10.1109/SIU53274.2021.9477700.
N. Vara, F. Rahimi, and F. Danesh, “Do LIS experts select more appropriate journals than journal finders? A study about LIS journals?,” J. Librariansh. Inf. Sci., no. December 2024, pp. 1–13, 2023, doi: 10.1177/09610006231214562.
X. Li, S. Feng, and X. Zhang, “A Review of Methods Using Large Language Models in News Recommendation Systems,” in 2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2024. doi: https://doi.org/10.1109/SNPD61259.2024.10673956.
G. Huang, “E-Commerce Intelligent Recommendation System Based on Deep Learning,” in 2022 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), 2022. doi: https://doi.org/10.1109/IPEC54454.2022.9777500.
Y. Zhang, Y. Li, R. Wang, M. S. Hossain, and H. Lu, “Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4696–4705, 2021, doi: https://doi.org/10.1109/TITS.2020.2990214.
Y. H. Alfaifi, “Towards an Ontology-Based E-Learning Recommendation System,” in 2023 3rd International Conference on Computing and Information Technology (ICCIT), 2023. doi: https://doi.org/10.1109/ICCIT58132.2023.10273903.
V. N. Rathod, R. H. Goudar, A. Kulkarni, D. G. M, and G. S. Hukkeri, “A Survey on E-Learning Recommendation Systems for Autistic People,” IEEE Access, vol. 12, pp. 11723–11732, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3355589.
P. Ahlawat and C. Rana, “A Comprehensive Insight on Machine Learning Enabled Internet of Things Recommender Systems (IoTRS),” in 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021. doi: https://doi.org/10.1109/ICAC3N53548.2021.9725718.
S. Muskan, R. S, A. B, J. G, and S. Challa, “An Early Recommendation Tool to Enhance Medicinal Plant Growth based on GIS and Soil Data,” in 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3), 2023. doi: https://doi.org/10.1109/IC2E357697.2023.10262541.
K. Al Fararni, B. Aghoutane, L. Maada, F. Nafis, A. Yahyaouy, and J. Riffi, “Item Based Collaborative Filtering Tourism Recommender System Using Apache Mahout,” in 2024 International Conference on Intelligent Systems and Computer Vision (ISCV), 2024. doi: https://doi.org/10.1109/ISCV60512.2024.10620090.
K. Al Fararni, F. Nafis, B. Aghoutane, A. Yahyaouy, J. Riffi, and A. Sabri, “Hybrid recommender system for tourism based on big data and AI: A conceptual framework,” Big Data Min. Anal., vol. 4, no. 1, pp. 47–55, 2021, doi: https://doi.org/10.26599/BDMA.2020.9020015.
D. Mogaveera, V. Mathur, and S. Waghela, “e-Health Monitoring System with Diet and Fitness Recommendation using Machine Learning,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021. doi: https://doi.org/10.1109/ICICT50816.2021.9358605.
S. M. Al-Ghuribi, S. A. M. Noah, M. A. Mohammed, N. Tiwary, and N. I. Y. Saat, “A Comparative Study of Sentiment-Aware Collaborative Filtering Algorithms for Arabic Recommendation Systems,” IEEE Access, vol. 12, pp. 174441–174454, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3489658.
F. U. D. Laseno and B. Hendradjaya, “Knowledge-Based Filtering Recommender System to Propose Design Elements of Serious Game,” Proc. Int. Conf. Electr. Eng. Informatics, vol. 2019-July, no. July, pp. 158–163, 2019, doi: 10.1109/ICEEI47359.2019.8988797.
R. M. Yusup and R. Wahdiniwaty, “Siamese Neural Networks Approach to Hybrid Recommender System Modeling for Fostering Economic Growth in Fashion Domain,” in 2023 9th International Conference on Signal Processing and Intelligent Systems (ICSPIS), 2023. doi: https://doi.org/10.1109/ICSPIS59665.2023.10402654.
S. Rani, “Machine Learning Algorithms for building Recommender Systems,” in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), IEEE, 2019, pp. 785–790.
M. Ge, G. Pilato, F. Persia, and D. D’Auria, “Recommender System for Social Media: Research Challenges and Future Applications,” in 2023 Fifth International Conference on Transdisciplinary AI (TransAI), 2023. doi: https://doi.org/10.1109/TransAI60598.2023.00033.
C. D. Casuat, A. S. M. Isira, E. D. Festijo, A. S. Alon, J. N. Mindoro, and J. A. B. Susa, “A Development of Fuzzy Logic Expert-Based Recommender System for Improving Students’Employability,” in 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), 2020. doi: https://doi.org/10.1109/ICSGRC49013.2020.9232543.
X. Nan and D. Wu, “Design and Implementation of Japanese Smart Tourism System Based on Improved Genetic Algorithm,” in 2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS), 2022. doi: https://doi.org/10.1109/AIARS57204.2022.00033.
R. Ahuja, A. Solanki, and A. Nayyar, “Movie recommender system using k-means clustering and k-nearest neighbor,” Proc. 9th Int. Conf. Cloud Comput. Data Sci. Eng. Conflu. 2019, pp. 263–268, 2019, doi: 10.1109/CONFLUENCE.2019.8776969.
S. P. Priya* and D. M. Karthikeyan, “A Novel Automatic Journal Recommender System,” Int. J. Recent Technol. Eng., vol. 8, no. 6, pp. 2608–2612, 2020, doi: 10.35940/ijrte.f8598.038620.
D. Wang, Y. Liang, D. Xu, X. Feng, and R. Guan, “A content-based recommender system for computer science publications,” Knowledge-Based Syst., vol. 157, pp. 1–9, 2018, doi: 10.1016/j.knosys.2018.05.001.
M. B. Magara, S. O. Ojo, and T. Zuva, “Towards a Serendipitous Research Paper Recommender System Using Bisociative Information Networks (BisoNets),” 2018 Int. Conf. Adv. Big Data, Comput. Data Commun. Syst. icABCD 2018, pp. 1–6, 2018, doi: 10.1109/ICABCD.2018.8465475.
R. Yuliant and N. Karna, “Knowledge Sharing Filtering on OAI-PMH,” 2016 Int. Conf. Inf. Technol. Syst. Innov., no. October 2016, 2016, doi: 10.1109/ICITSI.2016.7858213.
W. H. Gomma and A. A. Fahmy, “A Survey of Text Similarity Approaches,” Int. J. Comput. Appl., vol. 68, no. 13, pp. 13–18, 2013.
S.-C. Necula, “Exploring the Model-View-Controller (MVC) Architecture: A Broad Analysis of Market and Technological Applications,” Preprints, no. Mvc, 2024, doi: 10.20944/preprints202404.1860.v1.
