Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm

Authors

  • Amir Saleh Universitas Prima Indonesia, Indonesia
  • NP Dharshinni Universitas Prima Indonesia, Indonesia
  • Despaleri Perangin-Angin Universitas Prima Indonesia, Indonesia
  • Fadhillah Azmi Universitas Medan Area, Indonesia
  • Muhammad Irfan Sarif Universitas Pembangunan Panca Budi, Indonesia

DOI:

10.33395/sinkron.v8i1.11954

Keywords:

Recommendation systems; Learning strategy; Collaborative filtering; Naïve bayes, Training data, Testing data

Abstract

Recommendation systems are widely used in various fields of life to provide suggestions for a product, service, or piece of information to someone where there is an object to choose from. The recommendation system can also be applied in the field of education, especially in improving the quality of learning that occurs in schools. In this study, developing and implementing a recommendation system was used to determine the learning strategy applied in class. The system is very necessary in order to obtain effective and efficient learning in accordance with the desired learning style of students. In addition, learning that leads to students' desire to learn can make it easier for teachers to achieve predetermined learning goals. In this study, collaborative filtering techniques based on the Naive Bayes algorithm were used to determine the learning strategy. Before carrying out the recommendation process, datasets will be collected first, which are obtained from student responses through the questionnaires provided. This data will be used as training data to obtain recommendations on learning strategies that will be applied by the teacher in the classroom. After the training data is collected, the teacher will provide a response, and the results obtained will be used as testing data. From the results of implementing a recommendation system that has been built using the Naïve Bayes algorithm, the accuracy obtained is 90.91% in determining learning strategies that are appropriate to student learning styles.

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References

Adrian, R., Perdana, M. A. J. S., Asroni, A., & Riyadi, S. (2020). Applying the Naive Bayes Algorithm to Predict the Student Final Grade. Emerging Information Science and Technology, 1(2), 49–57. Retrieved from https://doi.org/10.18196/eist.127

Aji, W. N., & Budiyono, S. (2018). The Teaching Strategy of Bahasa Indonesia in Curriculum. International Journal of Active Learning, 58(2), 58–64. Retrieved from http://journal.unnes.ac.id/nju/index.php/ijal

Amelia, M., & Prystiananta, N. C. (2021). Using Inquiry Based Learning Strategy in Teaching Writing Descriptive Text. Linguistic, English Education and …, 5, 1–10. Retrieved from https://journal.ipm2kpe.or.id/index.php/LEEA/article/view/2648

Chavan, R., & Mukhopadhyay, D. (2017). A comparative study of recommendation algorithms in e-commerce. Proceedings of the International Conference on IoT in Social, Mobile, Analytics and Cloud, I-SMAC 2017, 163–168. Retrieved from https://doi.org/10.1109/I-SMAC.2017.8058331

Deschênes, M. (2020). Recommender systems to support learners’ Agency in a Learning Context: a systematic review. International Journal of Educational Technology in Higher Education, 17(1). Retrieved from https://doi.org/10.1186/s41239-020-00219-w

Dubey, A., & Ranjan, R. (2018). Movie Recommendation System using Naive Bayes Algorithm with Collaborative Filtering. International Journal of Science and Research, 9(7), 2018–2020. Retrieved from https://doi.org/10.21275/SR20603193402

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261–273. Retrieved from https://doi.org/10.1016/j.eij.2015.06.005

Kadafi, A. R. (2018). Perbandingan Algoritma Klasifikasi Untuk Penjurusan Siswa SMA. Jurnal ELTIKOM, 2(2), 67–77. Retrieved from https://doi.org/10.31961/eltikom.v2i2.86

Lisnawita, L., Guntoro, G., & Musfawati, M. (2022). Implementation of Naïve Bayes for Classification of Learning Types. Digital Zone: Jurnal Teknologi Informasi Dan Komunikasi, 13(1), 44–54. Retrieved from https://doi.org/10.31849/digitalzone.v13i1.9825

Mariskhana, K., Sintawati, I. D., & Widiarina. (2022). Implementation of Data Mining to predict sales of Bogo helmets using the Naïve Bayes algorithm, 7(4), 2303–2310.

Nasution, W. N. (2017). STRATEGI PEMBELAJARAN. Medan: Perdana Publishing.

Nasution, W. N. (2020). Expository Learning Strategy: Definition, Goal, Profit and Procedure. IOSR Journal Of Humanities And Social Science (IOSR-JHSS, 25(5), 7–10. Retrieved from https://doi.org/10.9790/0837-2505080710

Nugroho, R., Polina, A., & Mahendra, Y. (2020). Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach. International Journal of Applied Sciences and Smart Technologies, 2(2), 119–126. Retrieved from https://doi.org/10.24071/ijasst.v2i2.2987

Pangesti, W. E., Suryadithia, R., Faisal, M., & ... (2021). Collaborative Filtering Based Recommender Systems For Marketplace Applications. International Journal of …, 1201–1209. Retrieved from https://ijersc.org/index.php/go/article/view/184

Pramudita, R. (2020). Pengujian Black Box pada Aplikasi Ecampus Menggunakan Metode Equivalence Partitioning. INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics, 4(2), 193. Retrieved from https://doi.org/10.51211/itbi.v4i2.1347

Pratiwi, H. Y., Ain, N., & Igut, H. J. (2019). The Implementation of Problem Based Learning Model to Improve Student’s Motivation and Critical Thinking. Berkala Ilmiah Pendidikan Fisika, 7(3), 177. Retrieved from https://doi.org/10.20527/bipf.v7i3.6519

Puntheeranurak, S., & Pitakpaisarnsin, P. (2013). Time-aware Recommender System Using Naïve Bayes Classifier Weighting Technique. Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation, 68(3ca), 266–269. Retrieved from https://doi.org/10.2991/3ca-13.2013.66

Rrmoku, K., Selimi, B., & Ahmedi, L. (2022). Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier. Computation, 10(1). Retrieved from https://doi.org/10.3390/computation10010006

Surdin. (2018). The Effect of Contextual Teaching and Learning ( CTL ) Models on learning outcomes of Social Sciences of the material of forms the face of the earth on Class VII of Junior High School. International Journal of Education and Research, 6(3), 57–64. Retrieved from http://ijern.com/March-2018.php

Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811–2819. Retrieved from https://doi.org/10.1016/j.procs.2010.08.006

Urdaneta-Ponte, M. C., Mendez-Zorrilla, A., & Oleagordia-Ruiz, I. (2021). Recommendation systems for education: Systematic review. Electronics (Switzerland), 10(14). Retrieved from https://doi.org/10.3390/electronics10141611

V, P. C., Pandian V, V. P., Kumar V, V. K., & Bharathi V, S. M. (2020). Recommendation System Using Naive Bayes Classifier. International Research Journal of Engineering and Technology, 5507–5510. Retrieved from www.irjet.net

Yolasb, E. Y., Sitanayah, L., & Kumenap, V. D. (2022). Rekomendasi Pemilihan Media Tatap Muka Pembelajaran Daring Menggunakan Metode ELECTRE. JOINTER : Journal of Informatics Engineering, 3(01), 1–9. Retrieved from https://doi.org/10.53682/jointer.v3i01.51

Zain, M. (2017). Pengembangan Strategi Pembelajaran Dan Pemilihan Bahan Ajar. Inspiratif Pendidikan, 6(1), 172. Retrieved from https://doi.org/10.24252/ip.v6i1.4925

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How to Cite

Saleh, A. ., Dharshinni, N. ., Perangin-Angin, D. ., Azmi, F. ., & Sarif, M. I. (2023). Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(1), 256-267. https://doi.org/10.33395/sinkron.v8i1.11954