PENERAPAN METODE ADAPTIVE BOOSTING (ADABOOST) PADA DECISION TREE UNTUK ANALISIS SENTIMEN PELANGGAN MAXIM - Erni Triana; Mustafid Mustafid; Rukun Santoso (Jurnal Gaussian, 2025)
Information technology is currently growing rapidly, one form of technology beneficiary is using the internet, namely online transportation services based on mobile applications. Maxim is one of the online transportation services in Indonesia that offers relatively cheaper prices compared to other online transportation services. This study aims to apply the Adaptive Boosting (Adaboost) method with Decision Tree to classify Maxim's customer review data so that it can establish customer satisfaction factors. Review data was obtained from June – December 2022 with a total of 1500 reviews. Classification was carried out using the Adaptive Boosting method with a Decision Tree and Tuning Hyperparameter Grid Search. Adaptive Boosting is used to improve the performance of the Decision Tree so it can work better. The Grid Search algorithm is used to determine the best hyperparameter combination in Adaptive Boosting so that the classification process can be more optimal. Classification using the Adaptive Boosting model with Decision Tree yields accuracy, precision and recall values of 83,69%, 86,75% and 85,71% with the best parameter combination based on Grid Search is n_estimator (number of trees) 300 and learning rate 0,001. Based on this accuracy value, it can be concluded that the Adaptive Boosting model is quite good at classifying Maxim's customer review data.