This paper discusses Educational Data Mining (EDM) to predict the punctuality and graduation predicate. Both are considered essential aspects that represent the students academic performance. The model was developed by using academic records of 100 students from the vocational school of Informatics Management at Universitas Teknologi Yogyakarta. The dataset consisting of three features and two different labels was obtained by creating Application Programming Interfaces (APIs) connected to an academic database. Two classification algorithms were used to obtain knowledge from the dataset, i.e., Support Vector Machine (SVM) and Naive Bayes (NB). From the observations, SVM achieved accuracy for the punctuality of graduation on 0.68 while NB on 0.60. On graduation predicate, both algorithms achieved the same accuracy level of 0.92. The proposed model can be applied to chatbot applications. It will help students to get online academic suggestions and recommendations.

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Rianto and M. Fachrie, “Development of educational data mining model for predicting student punctuality and graduation predicate”, International Journal of Technology and Engineering Studies, vol. 5, no. 5, pp.151–156, 2019