Exploratory Data Analysis Towards Terrorist Activity in Indonesia Using Machine Learning Techniques
Keywords:Prediction, K-Nearest Neighbor, K-Fold Cross-Validation
Terrorism Activity is the subject of the talks in various countries, especially in Indonesia. The
activities of terrorism are carried out in various ways using suicide bombs, violent action that
aimed to demoralize by creating fear to the society and national security. In Indonesia,
according to Kompas news website recorded there were 10 suicide bombings occurred in the
past 6 years and took many casualties in every event. With this, it certainly gives a threat to
the people in Indonesia in terms of physical, moral and even in terms of national security. To
overcome this problem, it is necessary to increase the national security so that terrorism can
be prevented, and it will not happen again. This study is aimed to conduct an exploratory data
analysis and predict terrorist activity in Indonesia using K-Nearest Neighbor (KNN), and ¬kfold cross-validation. In this research, data selection, data cleaning, data reduction was
carried out and feature selection process which aimed to find out the most influential data
attributes. Based on the result of the analysis to predict the terrorist activity, the result of the
accuracy was obtained with a value of k = 8 at 88.86%.
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