Implementation of the Naive Bayes Algorithm for Classification of Public Service Complaints in E-Government at Kunciran Indah Tangerang

Abstract

The implementation of e-government at the local government level is essential for improving the quality and efficiency of public services. However, the management of public service complaints at Kelurahan Kunciran Indah, Tangerang, is still conducted manually, leading to delays and inefficiencies in handling citizen reports. This study aims to implement the Naive Bayes algorithm to automatically classify public service complaints within an e-government system. A quantitative computational approach was employed using a dataset of 50 complaint records categorized into four classes: infrastructure, cleanliness, service, and administration. Data preprocessing techniques, including case folding, tokenization, and stopword removal, were applied prior to model training. The Naive Bayes classifier was used to build a classification model and evaluate its performance. The results show that the proposed model achieved an accuracy of 90%, demonstrating good performance in classifying text-based complaints across all categories. This indicates that the Naive Bayes algorithm is effective for supporting automated complaint classification in local government services. The implementation of this system can improve service efficiency, accelerate response time, and assist decision-making processes. Nevertheless, the study is limited by the relatively small dataset, and future research is recommended to utilize larger and more diverse data to enhance model performance.