IMPLEMENTATION OF MACHINE LEARNING IN IMPROVING WEBSITE USER EXPERIENCE AND SATISFACTION - Alyusi, Shiefti Dyah; Yuadi, Imam (JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 2025)
This research aims to analyze user satisfaction in accessing the Airlangga University library website through the application of machine learning algorithms. The benefit of this research is that it provides insight into improving the quality of digital library services based on data-based analysis. The methods used include user surveys, data preprocessing, and application of the Orange Data Mining with models Support Vector Machine (SVM) and K-Nearest Neighbor (kNN) algorithms to classify user satisfaction levels, as well as comparing the results of the two models. The results show that the SVM model is able to achieve a Recall accuracy of 0.587 in identifying user satisfaction, but the precision metric is greater in SVM and the AUC is greater in kNN so it still requires optimization. This research concludes that the application of machine learning, especially SVM, can be an effective tool for improving user experience and providing more precise recommendations for improving library services.