A NAÏVE BAYES ANALYSIS OF GRADUATE USERS’ SATISFACTION
Keywords:
Bayesian network, e1071, caret, conditional probability
Abstract
The Bayesian network describes a conditional probability relationship between one random variable and another, which is dependent on each other. It provides a cohesive representation of the joint probability distribution, one of which is the formation of structures Bayesian Network using the naïve Bayes method. The purpose of this study was to develop the Bayesian network model using the naïve Bayes method on user satisfaction toward the competencies of graduates of the Faculty of Teacher Training and Education (FTTE) of Muhammadiyah University Pringsewu Lampung and to predict the level of satisfaction among graduates by identifying how often the model predicts correctly. The naïve Bayes resulted from the analysis that can be used to estimate the value of the proportion of each random variable in user satisfaction indicators. Then, the model successfully classified the user satisfaction measurement results of graduates, achieving 91.67%.
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Chakraborty, S., Mengersen, K., Fidge, C., Ma, L., Lassen, D. (2016). A Bayesian Network-based customer satisfaction model: a tool for management decisions in railway transport. Decision Analytics 3. https://doi.org/10.1186/s40165-016-0021-2
Cheng, Y., Lin, H. (2020). Robust Learning of Fixed-Structure Bayesian Networks in Nearly-Linear Time. Presented at the International Conference on Learning Representations.
Crawford, E., Kay, J. and Eric, M. (2002). The intelligent email sorter, Proceedings of the 19th International Conference on Machine Learning, Sydney, Australia, pp. 83–90
Fadlan, C., Ningsih, S. and Windarto, A.P. (2018). Penerapan Metode Naïve Bayes Dalam Klasifikasi Kelayakan Keluarga Penerima Beras Rastra. JUTIM (Jurnal Teknik Informatika Musirawas), 3(1), pp. 1–8. https://doi:10.32767/jutim.v3i1.286.
Fenton, N.E., McLachlan, S., Lucas, P., Dube, K., Hitman, G.A., Osman, M., Kyrimi, E., Neil, M. (2021). A Bayesian network model for personalised COVID19 risk assessment and contact tracing. https://doi.org/10.1101/2020.07.15.20154286
Indah, N.P. & Rizqia L.A. (2019). Pemodelan Bayesian Network untuk Prediksi Penyaluran Saluran Pernapasan. PETIR: Jurnal Pengkajian dan Penerapan Teknik Informatika, Vol. 12, No. 2, 292-302.
Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective, Artificial Intelligence in Medicine 23: 89–109.
Lesniak, A., Janowiec, F. (2020). Application of the Bayesian Networks in Construction Engineering. Civil and Environmental Engineering Reports 30, 221–233. https://doi.org/10.2478/ceer-2020-0028
Miyahara, K. and Pazzani, M.J. (2000). Collaborative filtering with the simple Bayesian classifier, Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence, Melbourne, Australia, pp. 679–689.
Pearl, J. (1986). Fusion, propagation, and Structuring in belief networks. Available at: https://www.sciencedirect.com/science/article/abs/pii/000437028690072X . https://doi.org/10.1016/0004-37023(86)90072-X. (Accessed: 19 November 2021).
Rochmana, Y.C., Hakimah, M. and Farida, F. (2020). Perbandingan Metode Naïve Bayes Dan Equivalence Classes Dalam Menentukan Struktur Bayesian Network. POSITIF : Jurnal Sistem dan Teknologi Informasi, 6(2), pp. 132–136. https://doi:10.31961/positif.v6i2.995.
Salini, S., Kenett, R. (2007). Bayesian networks of customer satisfaction survey data. Department of Economics University of Milan Italy, Departemental Working Papers 36. https://doi.org/10.1080/02664760802587982
Scutari, M and Denis, J-B. (2015). Bayesian Networks with examples in R. Taylor & Francis.
Taheri, S. and Mammadov, M. (2013). Learning The Naïve Bayes Classifier with Optimization Models. Int. J. Appl. Math. Comput. Sci., Vol. 23.
Taheri, S., Mammadov, M. and Bagirov, A.M. (2011). Improving Naive Bayes Classifier Using Conditional Probabilities. 121, p. 6.
Wang, D., Amril Jaharadak, A., Xiao, Y. (2020). Dynamic Knowledge Inference Based on Bayesian Network Learning. Mathematical Problems in Engineering 2020, 1–9. https://doi.org/10.1155/2020/6613896
What is package e1071? – Diaridelsestudiants.com (no date). Available at: https://diaridelsestudiants.com/what-is-package-e1071/ (Accessed: 20 December 2021).
Wenhui, L., Fengtian, C., Chuna, W., Xingkai, M. (2021). Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks. Advances in Civil Engineering 2021, e6624579. https://doi.org/10.1155/2021/6624579
Xu and Du. (2018). Factors Influencing Users; Satisfaction and Loyalty to Digital Libraries in Chinese Universities. Computers in Human Behavior. Vol. 83. 64 – 72.
Zupan, B., Demsar, J., Kattan, M.W., Ohori, M., Graefen, M., Bohanec, M. and Beck, J.R. (2001). Orange and decisions-at-hand: Bridging predictive data mining and decision support, Proceedings of the ECML/PKDD Workshop on Integrating Aspects of Data
Mining, Decision Support and Meta-Learning, Freiburg, Germany, pp. 151–162.
Published
2025-01-25
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