ENHANCING COMPLAINT MANAGEMENT THROUGH INFORMATION SYSTEMS: LSTM-BASED AUTOMATIC CLASSIFICATION OF BANK CUSTOMER COMPLAINTS IN INDONESIA
DOI:
https://doi.org/10.30829/jipi.v10i1.24493Keywords:
Text Classification, Information Systems, Customer Complaints, LSTM, Deep Learning, Natural Language Processing (NLP), Digital BankingAbstract
This study develops an automatic classification system for customer complaints in the banking sector using the Long Short-Term Memory (LSTM) deep learning method. A dataset comprising 4,714 customer complaint entries was collected from Bank Sumut's internal communication records, categorized into six major complaint types. The data underwent comprehensive preprocessing, including cleaning, tokenization, and vectorization. A supervised learning approach was applied using an LSTM-based neural network architecture, and the model's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrated a classification accuracy of 100% on the test set, with the model successfully categorizing free-text complaints into predefined categories. The findings highlight the strong potential of LSTM models in supporting automated text-based customer service operations within digital banking environments, particularly for Indonesian-language complaint datasets. Further research is recommended to validate the model on unseen real-world data and to address challenges related to data imbalance.References
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