Crime Forecasting Model  Using Id3 Algorithm

Rosemarie Perreras; Jake Libed

doi:10.58870/berj.v10i1.83

Authors

Rosemarie Perreras San Beda University, Manila, Philippines https://orcid.org/0000-0003-0238-8777
Jake Libed San Beda University, Manila, Philippines

Keywords:

Data Analytics, Machine Learning, Crime Analysis, ID3 Algorithm

Abstract

Crime has been a significant concern in every community, as it brings a negative impact on the economy as well as damages to people’s lives and property. A total of 107,538 index crimes were recorded in 2019 (Philippine Statistics Authority, 2021), clearly showing that crime is a major area of concern for the government. Being able to identify significant patterns in crime data would result in a reduction in the crime rate. The study implements the iterative dichotomiser 3(ID3) algorithm to analyze crime patterns in the Philippines. This data analytic approach evaluates significant patterns in identifying key areas that need immediate concern based on data published by the crime research analysis center of the office of the Directorate for investigation and detective management of the Philippine National Police for the period of January to March 2018. Upon the application of the ID3 technique, four significant criteria are used, namely region, robbery incident killed, robbery incident wounded, and shooting incident. The computation for the entropy and gain is obtained from which attribute selection is derived. Among the four attributes from 23 records available, the highest information gain is treated as the most important criterion, and the results show that upon implementing the Shannon entropy calculation, the wounded in a shooting incident obtained the highest information gain and requires immediate attention in region 4A. An evaluation process has been conducted using relative absolute error, mean absolute error (MAE), and F1 score to determine the accuracy of the crime model, and it was able to obtain a value of 95.2827, 0.2964, and 0.905, respectively.

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