<![CDATA[The number of complaints received through the MyPelindo app has increased in line with its high usage for operational and administrative activities by employees. The large volume of complaint data received makes manual identification and grouping of complaints less effective and time-consuming. Therefore, the aim of this study is to extract and standardise complaint topics using the Latent Dirichlet Allocation (LDA) method, build a classification model based on the obtained topics, and implement this model in a website-based system to automatically predict the category of user complaints. The study was conducted using a text mining approach with a Knowledge Discovery in Databases (KDD) methodology, which includes the preprocessing stage, topic modelling using LDA and topic standardisation based on cosine similarity. The results of the standardisation were then used as category labels in the classification process. The best model was then implemented in a web-based system using the Flask framework. The results of the study showed that the optimal number of topics obtained was eight, with the highest coherence score of 0.353. The standardisation process successfully simplified the eight topics into three main categories: Ganti Perangkat & Error Umum, Masalah Login & Akun, and Absensi & Koreksi Data. Based on the classification evaluation results, the best model was Logistic Regression, achieving an accuracy rate of 96.05% and an F1-score of 0.96. The implementation of the web-based system also successfully predicted the category of complaints based on user input.]]>
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