Sentiment Classification of The Capsule Hotel Guest Reviews using Cross-Industry Standard Process for Data Mining (CRISP-DM) - Singgalen, Yerik Afrianto (Universitas Budi Darma, 2024)
Technology advancements empower hotel accommodation service managers to undertake innovative initiatives to enhance guest appeal and ensure safety and comfort. One manifestation of such innovation is exemplified by The Capsule Hotel, which offers novel experiences to both domestic and international tourists. This research seeks to assess the sentiments of guests at The Capsule Malioboro, employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology and the Support Vector Machine (SVM) technique with Synthetic Minority Over-sampling Technique (SMOTE) operators. The findings demonstrate that when operated without SMOTE, the SVM algorithm yields a confusion matrix displaying an accuracy of 99.01%, precision of 99.00%, recall of 100%, AUC of 0.944, and an f-measure of 99.49%. With the integration of SMOTE, there is a notable enhancement across all metrics, with accuracy, precision, recall, AUC, and f-measure, all achieving perfect scores of 100%. In addition, an analysis of the top 10 frequently used words in guest reviews, such as