Implementation of Random Forest for Predicting Complaint Handling Priorities at the Minahasa Education Office - Sinta Tumbo; Medi Hermanto Tinambunan; Kristofel Santa (Universitas Islam Indragiri, 2026)
Public service delivery in the education sector requires government institutions to handle public complaints promptly and accurately. However, the Minahasa Regency Education Office still faces challenges in determining complaint handling priorities, which are often subjective and manually assigned. This condition may lead to delays in addressing urgent complaints. This study aims to implement the Random Forest algorithm to objectively predict complaint handling priorities based on data. The research methodology includes collecting a dataset of 500 public complaints with six attributes: complaint type, violation level, work unit, follow-up action, outcome, and priority. The process involves data preprocessing, splitting the dataset into training and testing sets, building the Random Forest model, and evaluating its performance. Data processing and modeling were conducted using Jupyter Notebook within the Anaconda environment. Model performance was evaluated using accuracy, confusion matrix, precision, recall, and F1-score metrics. The results show that the Random Forest algorithm achieved an accuracy of 100%, with precision, recall, and F1-score values of 1.00 across all priority classes. These findings indicate that the model demonstrates excellent and stable classification performance. Therefore, it can serve as a foundation for developing a decision support system to improve the effectiveness and quality of public service delivery at the Minahasa Regency Education Office.