Analysis of the classification of terrorist attacks in Indonesia


Mufid Junaedi(1*), Ahmad Fachrurozi(2), Mochammad Rizky Kusumayudha(3), Windu Gata(4),


(1) STMIK NUSA MANDIRI, JAKARTA
(2) STMIK NUSA MANDIRI, JAKARTA
(3) STMIK NUSA MANDIRI, JAKARTA
(4) STMIK NUSA MANDIRI, JAKARTA
(*) Corresponding Author

Abstract


Terrorist attacks are now being global issue both in developing and developed countries. There are more than 180,000 terrorist attacks in 1970-2017. Indonesia is one of the countries attacked by terorist. Bombings and firearms cause fatalities. Classification of terorist attacks can be performed based on either the attack succed or not. Succed attack is defined as an unavoided action that caused fatalities. There are seven attributes studied in this paper: year, month, attack type, terorist name, target attack, city, and weapon type uses to attack. Evaluations shows that k-NN classiefier exerts the highest accuracy of 90.79%, followed by naïve bayes 80.45%, and C4.5 of 88.825%.
Keywords: Indonesia Terrorist, Classification Algorithm, Terorist Algorithm Classification.


Keywords


Teroris Indonesia, Algoritma Klasifikasi, Algoritma Klasifikasi Teroris.

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References


Alexandra, F. (2017). Analisis kajian terorisme dan radikalisme dalam 3 perspektif teoritis. Jurnal Paradigma, 6(3), 137–146.

Alkhalifi, Y., Zumarniansyah, A., Ardianto, R., Hardi, N., Augustia, A. E., & Komputer, I. (2020). COMPARISON OF NAIVE BAYES ALGORITHM AND C . 45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY. 17(1), 37–42.

Apandi, T. H., Maulana, R. B., Piarna, R., & Vernanda, D. (2019). ANALYZING THE POSSIBILITY OF DELAYS OF SPP PAYMENTS WITH C4.5 ALGORITHM (CASE STUDY OF POLITEKNIK TEDC BANDUNG) The work is distributed under the Creative Commons Attribution-Non-Commercial International 4.0 License. Jurnal TECHNO Nusa Mandiri, 16(2), 93–98. www.poltektedc.ac.id

Asriningtias, Y., Mardhiyah, R., Studi, P., Informatika, T., Bisnis, F., Informasi, T., & Yogyakarta, U. T. (2014). Aplikasi Data Mining Untuk Menampilkan Informasi Tingkat Kelulusan Mahasiswa. 8(1), 837–848. https://doi.org/10.12928/jifo.v8i1.a2082

Ermawati, E. (2019). Algoritma Klasifikasi C4.5 Berbasis Particle Swarm Optimization Untuk Prediksi Penerima Bantuan Pangan Non Tunai. Jurnal Sistem Informasi, 8(September), 513–528.

Fatmawati, F. (2016). Perbandingan Algoritma Klasifikasi Data Mining Model C4.5 Dan Naive Bayes Untuk Prediksi Penyakit Diabetes. None, 13(1), 50–59.

Fithri, D. L. (2016). MODEL DATA MINING DALAM PENENTUAN KELAYAKAN PEMILIHAN TEMPAT TINGGAL MENGGUNAKAN METODE NAIVE BAYES. Simetris : Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer. https://doi.org/10.24176/simet.v7i2.787

Fithri, D. L., & Darmanto, E. (2014). Sistem Pendukung Keputusan Untuk Memprediksi Kelulusan Mahasiswa Menggunakan Metode Naïve Bayes. Prosiding SNATIF, 1(1), 319–324. http://download.portalgaruda.org/article.php?article=198324&val=6548&title=SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMPREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN METODE NAÃ VE BAYES

Hendrian, S. (2018). Algoritma Klasifikasi Data Mining Untuk Memprediksi Siswa Dalam Memperoleh Bantuan Dana Pendidikan. Faktor Exacta, 11(3), 266–274. https://doi.org/10.30998/faktorexacta.v11i3.2777

Iin Parlina, Agus Perdana Windarto, Anjar Wanto, M. R. L. (2018). Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center. Memanfaatkan Algoritma K-Means Dalam Menentukan Pegawai Yang Layak Mengikuti Asessment Center Untuk Clustering Program Sdp.

Kalaiarasi, S., Mehta, A., & Bordia, D. (2019). Using Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat. International Journal of Engineering and Advanced Technology, 9(1), 5995–6000. https://doi.org/10.35940/ijeat.a1768.109119

Kumar, V., Mazzara, M., Messina, A., & Lee, J. (2020). A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks. Advances in Intelligent Systems and Computing, 925, 146–158. https://doi.org/10.1007/978-3-030-14687-0_13

MĂRGĂRIT, N., & PAVEL, C.-R. (2017). SELECTIVE ASPECTS RELATED TO THE RESEARCH AT THE SCENE IN CASE OF TERRORISM ACTS. ASPECTE SELECTIVE PRIVIND CERCETAREA LA FAŢA LOCULUI ÎN CAZUL ACTELOR DE TERORISM.

Mutiara, I. dan A. (2015). Penerapan K-Optimal Pada Algoritma Knn Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Ilmu Komputer Fmipa Unlam Berdasarkan Ip Sampai Dengan Semester 4. Klik - Kumpulan Jurnal Ilmu Komputer, 2(2), 159–173. https://doi.org/10.20527/KLIK.V2I2.26

Nuruzzaman, M. (2018). Terorisme dan Media Sosial Sisi Gelap Berkembangnya Teknologi Informasi Komunikasi. Syntax Literate ; Jurnal Ilmiah Indonesia.

Pratama, W. A. (2013). Analisa Perbandingan Algoritma Decision Tree , Naive Bayes , dan k-NN dalam Penentuan Target Tindakan Terorisme di Indonesia.

Sadewo, M. G., Windarto, A. P., & Hartama, D. (2017). PENERAPAN DATAMINING PADA POPULASI DAGING AYAM RAS PEDAGING DI INDONESIA BERDASARKAN PROVINSI MENGGUNAKAN K-MEANS CLUSTERING. InfoTekJar (Jurnal Nasional Informatika Dan Teknologi Jaringan). https://doi.org/10.30743/infotekjar.v2i1.164

Sandag, G. A. (2019). Exploratory Data Analysis Towards Terrorist Activity In Indonesia Using Machine Learning Techniques. Abstract Proceedings International Scholars Conference, 7(1), 1749–1760. https://doi.org/10.35974/isc.v7i1.1628

Singh, S., Verma, S., Tiwari, A., & Tiwari, A. (2017). A novel way to classify passenger data using Naïve Bayes algorithm (A real time anti-Terrorism approach). Proceedings on 2016 2nd International Conference on Next Generation Computing Technologies, NGCT 2016. https://doi.org/10.1109/NGCT.2016.7877433

Weeks, D. P. C. C. L. E. Y. N. to K. in 20. (2015). Global Terorist Dataset. Dk. https://doi.org/10.1017/CBO9781107415324.004

Wijayatun, R., & Sulistyo, Y. (2016). Prediksi Rating Film Menggunakan Metode Naive Bayes. Jurnal Teknik Elektro, 8(2), 60–63.




DOI: https://doi.org/10.31289/jite.v4i1.3788

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