Prediction of Kamtibmas Trends in the Jurisdiction of Bireuen Police Resort Using Naïve Bayes and Random Forest

Predicting trends in Public Security and Order (Kamtibmas) is crucial for supporting strategic decision-making by law enforcement agencies, particularly in regions with dynamic social and political environments such as Bireuen Regency. One of the key challenges is the absence of a data-driven predictive system capable of accurately identifying patterns in Kamtibmas incidents. This study aims to develop a predictive model for Kamtibmas trends within the jurisdiction of the Bireuen Police using Naïve Bayes and Random Forest machine learning algorithms. A quantitative approach is employed, following the Knowledge Discovery in Databases (KDD) methodology, which encompasses data selection, preprocessing, transformation, data mining, evaluation, and interpretation. The dataset, sourced from the daily reports of the Bireuen Police Intelligence Unit from 2021 to 2024, was encoded and normalized across variables such as time, day, month, sub-district, incident category, and reporting unit. The results indicate that the Random Forest algorithm significantly outperforms Naïve Bayes. Using a 90:10 split for training and testing data, Random Forest achieved an accuracy, precision, recall, and F1-score of 98%. In contrast, Naïve Bayes demonstrated lower performance, with accuracy ranging between 42% and 44%. These findings suggest that Random Forest is more effective in capturing complex patterns within Kamtibmas data and has strong potential for implementation as a strategic tool to support crime princidention and public order maintenance efforts in Bireuen Regency.