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Improving the Major Recommendation Systems: Analysis of Hybrid Naïve Bayes-based Collaborative Filtering and Fuzzy Logic
Corresponding Author(s) : Amir Saleh
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 8, No. 4, November 2023
Abstract
Major recommendation systems have been widely used to assist prospective students in choosing major that matches their interests and potential. In an effort to improve the performance of the recommendation system, this study proposed to use collaborative filtering techniques with naïve Bayes approach. In addition, this study improved the input parameters using fuzzy logic in determining the recommended majors. The methodology used started from collecting user data, including gender, academic history, interests, and other relevant attributes. The data were used to train the naïve Bayes technique by estimating the probability of feature conformity between users and students in the recommended majors. However, there were problems such as uncertainty and ambiguity in user preferences for input data. The fuzzy logic method aimed to improve the input parameters to more accurately reflect the user preferences. The results of improving the input parameters by using fuzzy logic were then used in the naïve Bayes technique to obtain recommendations for the direction that best suits the user’s preferences. The final stage of this study used evaluation metrics such as precision, recall, and f1-score to measure the performance of the recommendation system in providing accurate recommendations. The use of a hybrid of naïve Bayes and fuzzy logic algorithms obtains an accuracy value of 87.27%, a precision value of 87.33%, a recall value of 87.24%, and an f1-score value of 87.26%. These results are higher than the usual naïve Bayes model applied in major recommendation systems.
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References
R. Yu, Z. A. Pardos, H. Chau, and P. Brusilovsky, “Orienting Students to Course Recommendations Using Three Types of Explanation,” Adjun. Proc. 29th ACM Conf. User Model. Adapt. Pers., no. June 2021, pp. 238–245, 2021. https://doi.org/10.1145/3450614.3464483
D. P. Kusumaningrum, N. A. Setiyanto, E. Y. Hidayat, and K. Hastuti, “Recommendation System for Major University Determination Based on Student’s Profile and Interest,” J. Appl. Intell. Syst., vol. 2, no. 1, pp. 21–28, 2017. https://doi.org/10.33633/jais.v2i1.1389
N. Rachburee, P. Sunantapot, D. Ounjit, P. Panklom, P. Porking, and W. Punlumjeak, “A Major Recommendation System in Educational Mining,” 2021 1st Int. Conf. Cyber Manag. Eng., pp. 1–5, 2021. https://doi.org/10.1109/CyMaEn50288.2021.9497279
D. Roy and M. Dutta, “A systematic review and research perspective on recommender systems,” J. Big Data, vol. 9, no. 1, 2022. https://doi.org/10.1186/s40537-022-00592-5
N. Mishra, S. Chaturvedi, A. Vij, and S. Tripathi, “Research problems in recommender systems,” J. Phys. Conf. Ser., vol. 1717, no. 1, 2021. https://doi.org/10.1088/1742-6596/1717/1/012002
J. Feigl and M. Bogdan, “Collaborative filtering with neural networks,” in The European Symposium on Artificial Neural Networks, 2017.
P. Valdiviezo-Diaz, F. Ortega, E. Cobos, and R. Lara-Cabrera, “A Collaborative Filtering Approach Based on Naïve Bayes Classifier,” IEEE Access, vol. 7, pp. 108581–108592, 2019. https://doi.org/10.1109/ACCESS.2019.2933048
A. Saleh, N. Dharshinni, D. Perangin-Angin, F. Azmi, and M. I. Sarif, “Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm,” Sinkron, vol. 8, no. 1, pp. 256–267, 2023. https://doi.org/10.33395/sinkron.v8i1.11954
L. Shu-xian and F. Sen, “Design and Implementation of Movie Recommendation System Based on Naive Bayes,” J. Phys. Conf. Ser., vol. 1345, 2019. https://doi.org/10.1088/1742-6596/1345/4/042042
M. S. Ozcan and T. Temel, “NEW RECOMMENDER SYSTEM USING NAIVE BAYES FOR E-LEARNING,” 2016.
K. Rrmoku, B. Selimi, and L. Ahmedi, “Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier,” Computation, vol. 10, no. 1, 2022. https://doi.org/10.3390/computation10010006
N. Pahuja, S. Chaudhari, and P. Raundale, “A Review On Recent Approaches to Recommendation System Model Using Distinct Product Features,” 2019 2nd Int. Conf. Intell. Comput. Instrum. Control Technol., vol. 1, pp. 764–767, 2019, doi: 10.1109/ICICICT46008.2019.8993399.
T. T. S. Nguyen, “Model-Based Book Recommender Systems using Naïve Bayes enhanced with Optimal Feature Selection,” Proc. 2019 8th Int. Conf. Softw. Comput. Appl., 2019, doi: https://dl.acm.org/doi/10.1145/3316615.3316727.
Z. Putri, Sugiyarto, and Salafudin, “Sentiment Analysis using Fuzzy Naïve Bayes Classifier on Covid-19,” Desimal J. Mat., vol. 4, no. 1, pp. 13–20, 2021. http://dx.doi.org/10.24042/djm.v4i2.7390
L. P. Wanti and O. Somantri, “Comparing Fuzzy Logic Mamdani and Naïve Bayes for Dental Disease Detection,” J. Inf. Syst. Eng. Bus. Intell., vol. 8, no. 2, pp. 182–195, 2022. http://dx.doi.org/10.20473/jisebi.8.2.182-195
A. Papa, Y. Shemet, and A. Yarovyi, “Analysis of fuzzy logic methods for forecasting customer churn,” Technol. Audit Prod. Reserv., vol. 1, no. 2(57), pp. 12–14, 2021. https://dx.doi.org/10.15587/2706-5448.2021.225285
H. Li, K. Yu, C. Lien, C. Lin, and C. Yu, “Improving Aquaculture Water Quality Using Dual-Input Fuzzy Logic Control for Ammonia Nitrogen Management,” J. Mar. Sci. Eng. Artic., 2023. https://doi.org/10.3390/jmse11061109
Y. Xianrui, Y. Xiaobing, L. Chenliang, and C. Hong, “An improved parameter control based on a fuzzy system for gravitational search algorithm,” Int. J. Comput. Intell. Syst., vol. 13, no. 1, pp. 893–903, 2020. https://doi.org/10.2991/ijcis.d.200615.001
W. E. Pangesti, R. Suryadithia, M. Faisal, and ..., “Collaborative Filtering Based Recommender Systems For Marketplace Applications,” Int. J. …, vol. 2(5), no. 1201–1209, pp. 1201–1209, 2021. https://doi.org/10.51601/ijersc.v2i5.184
R. Nugroho, A. Polina, and Y. Mahendra, “Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach,” Int. J. Appl. Sci. Smart Technol., vol. 2, no. 2, pp. 119–126, 2020. https://doi.org/10.24071/ijasst.v2i2.2987
A. Wahana, D. S. Maylawati, B. A. Wiwaha, M. A. Ramdhani, and A. S. Amin, “News recommendation system using collaborative filtering method,” J. Phys. Conf. Ser., vol. 1402, no. 7, 2019, doi: 10.1088/1742-6596/1402/7/077010.
I. Ryngksai and L. Chameikho, “Recommender Systems : Types of Filtering,” Int. J. Eng. Res. Technol., vol. 3, no. 11, pp. 251–254, 2014.
B. R. Gajanansapre, “On Fuzzy Logic to handle Vague and Imprecise Data,” IJournals Int. J. Softw. Hardw. Res. Eng., vol. 3, no. 9, pp. 75–80, 2015.
F. Azmi, M. K. Gibran, A. Ridwan, and A. Saleh, “Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes,” Indones. J. Comput. Sci., vol. 12, no. 1, pp. 1032–1043, 2023. https://doi.org/10.33022/ijcs.v12i3.3232
I. Agus, S. W. Ningsih, and A. M. Abadi, “Fuzzy Decision Making with Mamdani Method and Its Aplication for Selection of Used Car in Sleman Yogyakarta Definition of Fuzzy Logic,” Seminar.Uny.Ac.Id, pp. 35–44, 2017.
W. Sałabun et al., “A fuzzy inference system for players evaluation in multi-player sports: The football study case,” Symmetry (Basel)., vol. 12, no. 12, pp. 1–49, 2020. https://doi.org/10.3390/sym12122029
P. C. V, V. P. Pandian V, V. K. Kumar V, and S. M. Bharathi V, “Recommendation System Using Naive Bayes Classifier,” Int. Res. J. Eng. Technol., pp. 5507–5510, 2020.
B. Song, Y. Gao, and X. M. Li, “Research on Collaborative Filtering Recommendation Algorithm Based on Mahout and User Model,” J. Phys. Conf. Ser., vol. 1437, no. 1, 2020. https://doi.org/10.1088/1742-6596/1437/1/012095
W. Wei, Z. Wang, C. Fu, R. Damaševičius, R. Scherer, and M. Wožniak, “Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model,” J. Phys. Conf. Ser., vol. 1682, no. 1, 2020. https://doi.org/10.1088/1742-6596/1682/1/012043
K. Mariskhana, I. D. Sintawati, and Widiarina, “Implementation of Data Mining to predict sales of Bogo helmets using the Naïve Bayes algorithm,” Sink. J. dan Penelit. Tek. Inform., vol. 7, no. 4, pp. 2303–2310, 2022. https://doi.org/10.33395/sinkron.v7i4.11768
N. E. Ramli, Z. R. Yahya, and N. A. Said, “Confusion Matrix as Performance Measure for Corner Detectors,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 29, no. 1, pp. 256–265, 2022. https://doi.org/10.37934/araset.29.1.256265
A. Tasnim, M. Saiduzzaman, M. A. Rahman, J. Akhter, and A. S. M. M. Rahaman, “Performance Evaluation of Multiple Classifiers for Predicting Fake News,” J. Comput. Commun., vol. 10, no. 09, pp. 1–21, 2022. https://doi.org/10.4236/jcc.2022.109001