Public Complaint Management Information System Based on Complaint Topic Classification Using the Naive Bayes Model
DOI:
https://doi.org/10.55208/hasgnc24Keywords:
Public Complaint, Naive Bayes, CodeIgniter, Information System, Public ServiceAbstract
Superior public service is an essential component of community life. Pasirkaliki Village faces considerable hurdles in managing public grievances, including inefficiencies and complications in prioritizing complaints according to their urgency. This research proposes the creation of a Public Complaint Management Information System utilising the Naive Bayes method within the CodeIgniter Framework to address these challenges. The Naive Bayes method is selected for its efficacy in classifying data, enabling the identification of patterns and trends in incoming complaints. This system aims to enhance the efficiency and precision of complaint management, deliver swifter and more accurate responses, and ultimately improve public satisfaction with the services. The findings of this research are anticipated to enhance the provision of responsive and high-quality public services in Pasirkaliki Village.
References
Bahri, S., Utami, E., & Nasiri, A. (2022). Classification of Public Complaints Basedon Text Mining Using Modified K-Nearest Neighbor, Naïve Bayes and C4. 5 Algorithm. CCIT J, 15(2), 198–207.
Caldeira, F., Nunes, L., & Ribeiro, R. (2022). Classification of public administration complaints. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022).
Esperança, M., Freitas, D., Paixão, P. V., Marcos, T. A., Martins, R. A., & Ferreira, J. C. (2025). Proactive Complaint Management in Public Sector Informatics Using AI: A Semantic Pattern Recognition Framework. Applied Sciences, 15(2).
Hafidz, I. H., Sulistya, A., & Lidiawaty, B. R. (2025). Sentiment Analysis of Public Complaints: A Machine Learning Comparison of SVM, Naive Bayes, Random Forest, and XGBoost. In 2025 International Conference on Advancement in Data Science, E-Learning and Information System (ICADEIS), 1–6.
Hariguna, T., & Hananto, A. R. (2022). E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm. In 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 303–307.
Madyatmadja, E. D., Sianipar, C. P., Wijaya, C., & Sembiring, D. J. (2023). Classifying crowdsourced citizen complaints through data mining: Accuracy testing of k-nearest neighbors, random forest, support vector machine, and adaboost. In Informatics, 16(4), 84.
Nai, R., Meo, R., Morina, G., & Pasteris, P. (2023). Public tenders, complaints, machine learning and recommender systems: a case study in public administration. Computer Law & Security Review, 51.
Nanlohy, L. Y., Yuniarno, E. M., & Nugroho, S. M. S. (2020). Classification of public complaint data in SMS complaint using Naive Bayes multinomial method. In 2020 4th International Conference on Vocational Education and Training (ICOVET), 241–246.
Primya, T., & Kanagaraj, G. (2025). AI-Integrated Web Complaint Management System with Risk Analysis and Geolocation. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 1–6.
Wabang, K., Oky Dwi Nurhayati, & Farikhin. (2022). Application of The Naïve Bayes Classifier Algorithm to Classify Community Complaints. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(5), 872–876. https://doi.org/10.29207/resti.v6i5.4498
Downloads
Additional Files
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
