<![CDATA[This study aims to predict the location of theft crimes in the jurisdiction of the Badung Police by applying the K-Nearest Neighbor (KNN) method. The main focus of the study is to identify crime patterns based on time and location variables in order to improve the effectiveness of police prevention strategies. The problem raised is how machine learning-based models can help detect theft-prone areas and improve accuracy in crime prevention efforts. This study uses a quantitative approach with the CRISP-DM (Cross-Industry Standard Process for Data Mining) method. The data used includes information on time, location of the incident, and theft categories based on police reports. The research process includes business understanding, data exploration and preparation, modeling using KNN, model performance evaluation, and implementation in the form of interactive map visualization. Model performance is analyzed using evaluation metrics such as precision, recall, and F1-score to measure the level of prediction accuracy. The results of the study show that the KNN model is able to identify locations with a high risk of theft with fairly good accuracy. Areas with high activity, such as transportation facilities and commercial areas, are more vulnerable to this crime. In addition, thefts occur more often in the morning, evening, and early morning when people are off guard. In conclusion, the KNN method is effective in predicting theft-prone areas. Implementation of this model can help the police improve the effectiveness of patrols and security strategies. It is recommended that this model be combined with a geographic information system (GIS) to facilitate the analysis of crime patterns in order to improve public security more proactively.]]>
