ANALISIS MODEL SISTEM REKOMENDASI KURSUS MOOC DENGAN METODE COLLABORATIVE FILTERING DAN INTEGRASI EXPLAINABLE AI

Authors

  • Nabila Muthia Putri Institut Teknologi Sumatera
  • Mugi Praseptiawan Institut Teknologi Sumatera
  • Meida Cahyo Untoro Institut Teknologi Sumatera

DOI:

https://doi.org/10.21927/ijubi.v7i1.4274

Keywords:

MOOC, Sistem rekomendasi, Collaborative Filtering, Explainable AI

Abstract

Sistem rekomendasi kursus Massive Open Online Course (MOOC) berperan penting dalam mendukung pembelajaran daring dengan memberikan saran kursus yang sesuai dengan preferensi pengguna. Dalam penelitian ini, kami mengembangkan model sistem rekomendasi kursus MOOC berbasis Collaborative Filtering dengan memanfaatkan dataset Coursera yang telah diproses. Preprocessing meliputi pembersihan data, penghapusan label yang tidak diperlukan, alokasi label, penghapusan data duplikat, dan analisis sentimen untuk memastikan konsistensi antara ulasan dan penilaian. Implementasi Collaborative Filtering melibatkan pembuatan tabel pivot, perhitungan Centered Cosine Similarity, dan prediksi penilaian kursus untuk pengguna yang belum pernah mengambil kursus tertentu. Evaluasi kinerja model dilakukan menggunakan metrik Root Mean Squared Error (RMSE) untuk mengukur tingkat kesalahan prediksi model. Hasil analisis dan evaluasi menunjukkan bahwa model yang dikembangkan berhasil memberikan rekomendasi kursus dengan tingkat kesalahan yang rendah, seperti yang tercermin dari nilai RMSE yang diperoleh yaitu 0.24 untuk sistem rekomendasi kursus MOOC. Integrasi Explainable AI dengan teknik LIME juga membantu dalam menjelaskan dan memahami rekomendasi yang diberikan oleh sistem, meningkatkan penjelasan tambahan terhadap model yang dibuat. Saran untuk pengembangan lebih lanjut termasuk fokus pada peningkatan interpretabilitas model dengan memperdalam integrasi Explainable AI, menggunakan dataset yang lebih besar, serta diversifikasi teknik pemodelan untuk meningkatkan kualitas dan akurasi rekomendasi yang diberikan oleh sistem.

References

S. Al Shaqsi and R. T. Syed, “Massive open online courses and entrepreneurship education in higher education institutions,†in Technology and Entrepreneurship Education: Adopting Creative Digital Approaches to Learning and Teaching, 2022. doi: 10.1007/978-3-030-84292-5_8.

S. Rizvi, B. Rienties, J. Rogaten, and R. F. Kizilcec, “Beyond one-size-fits-all in MOOCs: Variation in learning design and persistence of learners in different cultural and socioeconomic contexts,†Comput Human Behav, vol. 126, 2022, doi: 10.1016/j.chb.2021.106973.

S. Bansal and N. Baliyan, “A study of recent recommender system techniques,†International Journal of Knowledge and Systems Science, vol. 10, no. 2, 2019, doi: 10.4018/IJKSS.2019040102.

R. Duan, C. Jiang, and H. K. Jain, “Combining review-based collaborative filtering and matrix factorization: A solution to rating’s sparsity problem,†Decis Support Syst, vol. 156, 2022, doi: 10.1016/j.dss.2022.113748.

A. Beheshti, S. Yakhchi, S. Mousaeirad, S. M. Ghafari, S. R. Goluguri, and M. A. Edrisi, “Towards cognitive recommender systems,†Algorithms, vol. 13, no. 8, 2020, doi: 10.3390/A13080176.

J. Bharadiya and J. P. Bharadiya, “Machine Learning and AI in Business Intelligence: Trends and Opportunities,†International Journal of Computer (IJC) International Journal of Computer, vol. 48, no. 1, 2023.

Z. Li, H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen, “Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors,†2018, doi: 10.1145/3219819.3220014.

S. M. Al-Ghuribi and S. A. Mohd Noah, “Multi-Criteria Review-Based Recommender System-The State of the Art,†IEEE Access, vol. 7. 2019. doi: 10.1109/ACCESS.2019.2954861.

M. Ã. García-Cumbreras, A. Montejo-Ráez, and M. C. Díaz-Galiano, “Pessimists and optimists: Improving collaborative filtering through sentiment analysis,†Expert Syst Appl, vol. 40, no. 17, 2013, doi: 10.1016/j.eswa.2013.06.049.

W. Zhang, G. Ding, L. Chen, C. Li, and C. Zhang, “Generating virtual ratings from chinese reviews to augment online recommendations,†ACM Trans Intell Syst Technol, vol. 4, no. 1, 2013, doi: 10.1145/2414425.2414434.

S. Li et al., “Quantification and prediction of engagement: Applied to personalized course recommendation to reduce dropout in MOOCs,†Inf Process Manag, vol. 61, no. 1, Jan. 2024, doi: 10.1016/j.ipm.2023.103536.

J. Chen, X. Wang, S. Zhao, F. Qian, and Y. Zhang, “Deep attention user-based collaborative filtering for recommendation,†Neurocomputing, vol. 383, 2020, doi: 10.1016/j.neucom.2019.09.050.

Z. Cui et al., “Personalized Recommendation System Based on Collaborative Filtering for IoT Scenarios,†IEEE Trans Serv Comput, vol. 13, no. 4, 2020, doi: 10.1109/TSC.2020.2964552.

L. Longo, R. Goebel, F. Lecue, P. Kieseberg, and A. Holzinger, “Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020. doi: 10.1007/978-3-030-57321-8_1.

J. P. Gonzalo, A. Muñoz, S. Roque, J. Portela, and G. Madrid, “EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) TECHNIQUES BASED ON PARTIAL DERIVATIVES WITH APPLICATIONS TO NEURAL NETWORKS,†2023.

G. Srivastava et al., “XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions,†ACM Comput. Surv, vol. 1, no. 1, 2022, doi: 10.1145/1122445.1122456.

E. J. Chia and M. K. Najafabadi, “Solving Cold Start Problem for Recommendation System Using Content-Based Filtering,†in Proceedings - 2022 International Conference on Computer Technologies, ICCTech 2022, 2022. doi: 10.1109/ICCTech55650.2022.00015.

A. M. Ridwan, D. R. Ramdania, D. S. A. Maylawati, A. Wahana, E. Mulyana, and M. I. Al-Amin, “Leather Product Recommendation System using Collaborative Filtering Algorithm,†in Proceeding of 2022 8th International Conference on Wireless and Telematics, ICWT 2022, 2022. doi: 10.1109/ICWT55831.2022.9935395.

R. Bharti and D. Gupta, “Recommending Top N Movies Using Content-Based Filtering and Collaborative Filtering with Hadoop and Hive Framework,†in Advances in Intelligent Systems and Computing, vol. 740, 2019. doi: 10.1007/978-981-13-1280-9_10.

D. Shin, “How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance,†Comput Human Behav, vol. 109, 2020, doi: 10.1016/j.chb.2020.106344.

I. Karabila, N. Darraz, A. El-Ansari, N. Alami, and M. El Mallahi, “Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis,†Future Internet, vol. 15, no. 7, 2023, doi: 10.3390/fi15070235.

A. Da’u and N. Salim, “Recommendation system based on deep learning methods: a systematic review and new directions,†Artif Intell Rev, vol. 53, no. 4, 2020, doi: 10.1007/s10462-019-09744-1.

G. Shani and A. Gunawardana, “Evaluating Recommendation Systems,†in Recommender Systems Handbook, 2011. doi: 10.1007/978-0-387-85820-3_8.

J. Ni, Y. Cai, G. Tang, and Y. Xie, “Collaborative filtering recommendation algorithm based on TF-IDF and user characteristics,†Applied Sciences (Switzerland), vol. 11, no. 20, 2021, doi: 10.3390/app11209554.

Frontiers in massive data analysis. 2013. doi: 10.17226/18374.

Downloads

Published

2024-07-20

Issue

Vol. 7 No. 1 (2024): Indonesian Journal of Business Intelligence (IJUBI)

Section

Articles

License

COPYRIGHT TRANSFER FORM

The copyright to this article is transferred to Alma Ata University Press if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to AAU Press. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.

We declare that:

1. This paper has not been published in the same form elsewhere.

2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.

3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.

Furthermore, I/We hereby transfer the unlimited rights of publication of the above mentioned paper in whole to AAU Press. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.

The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.

Retained Rights/Terms and Conditions

 

1. Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.

2. Authors may reproduce or authorize others to reproduce the Work or derivative works for the authors personal use or for company use, provided that the source and the AAU Press copyright notice are indicated, the copies are not used in any way that implies AAU Press endorsement of a product or service of any employer, and the copies themselves are not offered for sale.

3. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.