Classifying Public Complaints in Denpasar: a Comparative Study of CNN, RNN, LSTM, and Stacking Deep Learning Models

Abstract

The process of lodging complaints represents a complex behavioral construct, influenced by the interplay of emotional states, sociocultural factors, and situational contexts. It functions as a pivotal channel for citizens to express dissatisfaction regarding the quality of governmental services. This research aims to optimize public complaint management by leveraging deep learning-based text classification on citizen submissions collected from the Denpasar City Complaint Web Portal. The methodological approach integrates several neural network models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, further enhanced by a Stacking ensemble technique that amalgamates the strengths of each architecture. The dataset consists of 10,302 textual records, categorized into four semantic classes: Complaints, Suggestions, Inquiries, and Information. To improve the robustness and reliability of the classification, advanced preprocessing steps were implemented, including the application of the Synthetic Minority Over-sampling Technique (SMOTE) to alleviate class imbalance and the utilization of Term Frequency–Inverse Document Frequency (TF-IDF) for extracting the most informative textual features. Empirical results demonstrate that the Stacking ensemble model significantly outperforms individual baseline models, achieving an accuracy of 77.83%, with recall and F1-score values of 74.38%. These findings highlight the effectiveness of ensemble deep learning approaches in multiclass complaint classification, thereby supporting improvements in public service delivery and fostering greater governmental transparency. Ultimately, this study contributes to the field of automated text classification by illustrating the potential of advanced neural architectures to enhance citizen participation and institutional accountability.