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

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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