Individual Risk Classification of Crime Groups using Ensemble Classifier Method

• Published in: International journal of advanced computer science & applications Vol. 13; no. 5
• Main Authors: Anggana, Ardhito P., Zahra, Amalia
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2022
• Subjects: Accuracy; Bagging; Crime; Decision making; Decision trees; K-nearest neighbors algorithm; Model accuracy; Terrorism
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2022.0130512

The most significant challenge for humanity worldwide to crime, especially terrorist attacks, should be considered. Determining the priority scale for anticipating individual terrorist groups is not easy and will significantly affect work activities and subsequent decision-making measures. Priority scale determination decisions should be made carefully so team members cannot choose the desired priority target. Determining the exact priority scale for a target can be influenced by several factors, such as desire factors and ability factors, using Dataset Intelligence. This research aims to find out the ability of each target and pattern to be carried out. Based on this problem, the study used the K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT), and Ensemble Bagging methods. Each of these algorithms has its characteristics; This classification technique can group priority targets according to their similarities, abilities, and desires. The value of each method used can be used as a reference to determine the correct group information for officers to determine the next steps. The study obtained a maximum accuracy of 70.25% using the Ensemble Bagging-Backward Elimination-K-Nearest Neighbor (KNN) classification method using 20 features. The results showed tests conducted and final analysis and conclusions based on accuracy and recall performance. The precision performance revealed that the Ensemble Bagged KNN, more precisely than KNN, Naïve Bayes, Decision Tree, and Bagging Naïve Bayes and Bagging Decision Tree. The KNN Bagging ensemble model can add accuracy, map individuals, and detect who should be intensely monitored based on predictive results.