<![CDATA[PT Dirgantara Indonesia (PTDI) faces challenges in managing employee complaints. This research aims to improve PTDI employee complaint management through sentiment analysis using the Naive Bayes algorithm with the CRISP-DM method. The stages applied include business understanding, data understanding, data preparation, modeling, evaluation and implementation. Employee complaint data is collected and processed using stop words removal and tokenization techniques. The Naive Bayes model is trained and evaluated using accuracy, precision, recall and F1-score metrics. The research results show that the Naive Bayes model is effective in grouping employee complaints into mild and severe categories. The model has an accuracy of 88.5%. The implementation of this sentiment analysis system is expected to help PTDI management handle employee complaints more quickly and precisely, increasing satisfaction and productivity. This research also contributes to the development of the science of sentiment analysis and machine learning, as well as its application in complaint management in companies. With this system, PTDI management can identify and prioritize complaints that require immediate handling, increasing operational efficiency and service quality to employees. This research provides practical solutions for PTDI and adds insight into the application of machine learning in managing employee complaints.]]>
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