Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost

• Published in: Informatics (Basel) Vol. 10; no. 4; p. 84
• Main Authors: Madyatmadja, Evaristus D., Sianipar, Corinthias P. M., Wijaya, Cristofer, Sembiring, David J. M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2023
• Subjects: Accuracy; Algorithms; Analysis; Artificial intelligence; Big Data; citizen science; Classification; Clustering; Complaints; Consumer complaints; Crowdsourcing; Data analysis; Data mining; Datasets; Decision trees; Digitization; Electronic democracy; Electronic government; Government information technology services; Machine learning; Methods; public complaint; Public services; smart city; Support vector machines; sustainable city; Text categorization; Indonesia
• ISSN: 2227-9709; 2227-9709
• DOI: 10.3390/informatics10040084

Crowdsourcing has gradually become an effective e-government process to gather citizen complaints over the implementation of various public services. In practice, the collected complaints form a massive dataset, making it difficult for government officers to analyze the big data effectively. It is consequently vital to use data mining algorithms to classify the citizen complaint data for efficient follow-up actions. However, different classification algorithms produce varied classification accuracies. Thus, this study aimed to compare the accuracy of several classification algorithms on crowdsourced citizen complaint data. Taking the case of the LAKSA app in Tangerang City, Indonesia, this study included k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost for the accuracy assessment. The data were taken from crowdsourced citizen complaints submitted to the LAKSA app, including those aggregated from official social media channels, from May 2021 to April 2022. The results showed SVM with a linear kernel as the most accurate among the assessed algorithms (89.2%). In contrast, AdaBoost (base learner: Decision Trees) produced the lowest accuracy. Still, the accuracy levels of all algorithms varied in parallel to the amount of training data available for the actual classification categories. Overall, the assessments on all algorithms indicated that their accuracies were insignificantly different, with an overall variation of 4.3%. The AdaBoost-based classification, in particular, showed its large dependence on the choice of base learners. Looking at the method and results, this study contributes to e-government, data mining, and big data discourses. This research recommends that governments continuously conduct supervised training of classification algorithms over their crowdsourced citizen complaints to seek the highest accuracy possible, paving the way for smart and sustainable governance.