Exploratory Data Analysis Towards Terrorist Activity in Indonesia Using Machine Learning Techniques

  • Green Arther Sandag Computer Science, Universitas Klabat
Keywords: Prediction, K-Nearest Neighbor, K-Fold Cross-Validation

Abstract

Terrorism Activity is the subject of the talks in various countries, especially in Indonesia. Theactivities of terrorism are carried out in various ways using suicide bombs, violent action thataimed 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 thepast 6 years and took many casualties in every event. With this, it certainly gives a threat tothe people in Indonesia in terms of physical, moral and even in terms of national security. Toovercome this problem, it is necessary to increase the national security so that terrorism canbe prevented, and it will not happen again. This study is aimed to conduct an exploratory dataanalysis and predict terrorist activity in Indonesia using K-Nearest Neighbor (KNN), and ¬kfold cross-validation. In this research, data selection, data cleaning, data reduction wascarried out and feature selection process which aimed to find out the most influential dataattributes. Based on the result of the analysis to predict the terrorist activity, the result of theaccuracy was obtained with a value of k = 8 at 88.86%.
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Published
2019-12-18
How to Cite
Sandag, G. (2019). Exploratory Data Analysis Towards Terrorist Activity in Indonesia Using Machine Learning Techniques. Abstract Proceedings International Scholars Conference, 7(1), 1774-1785. https://doi.org/10.35974/isc.v7i1.1628