<![CDATA[Twitter is one of the most widely used social media platforms in Indonesia, and telecommunication companies such as PT Telekomunikasi Seluler (Telkomsel) utilize their official account, @Telkomsel, as a primary channel for customer interaction and complaint handling. Currently, incoming complaint tweets are still classified manually by the customer service division, a procedure that is time-consuming and prone to human inconsistency. This study implements a text mining approach integrating the Naïve Bayes algorithm, Adaptive Boosting (AdaBoost), and the Synthetic Minority Over-Sampling Technique (SMOTE) to automatically classify Indonesian-language tweets mentioning @Telkomsel into two classes: "Complaint" and "Non-Complaint." A dataset of 2,000 tweets was collected through crawling in RapidMiner, followed by a two-stage text preprocessing workflow utilizing Gataframework and RapidMiner. The classification model was validated using 10-fold cross-validation. The experimental results demonstrate that the combination of Naïve Bayes, AdaBoost, and SMOTE achieves an accuracy of 78.84%, precision of 79.90%, recall of 80.51%, and an AUC of 0.809. These findings confirm that the proposed framework is capable of effectively automating complaint detection to support more responsive customer service operations.]]>
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