Mobile Forensics for Cyberbullying Detection using Term Frequency - Inverse Document Frequency (TF-IDF)

Authors

  • Imam Riadi Universitas Ahmad Dahlan
  • Sunardi Sunardi Universitas Ahmad Dahlan
  • Panggah Widiandana Universitas Ahmad Dahlan

DOI:

https://doi.org/10.26555/jiteki.v5i2.14510

Keywords:

Cyberbullying, TF, IDF, Forensics, Mobile

Abstract

The case of cyberbullying in Indonesia was ranked third in the world in 2015 and as much as 91% was experienced by children [1]. RSA Anti Fraud Command Center (AFCC) report reports that in 2015 45% of transactions were carried out through mobile channels, while 61% of fraud occurred through mobile devices [2]. WhatsApp in July 2019, 1.6 billion users access the WhatsApp messenger on a monthly basis [10]. The data opens a reference for investigators to better anticipate cybercrime actions that can occur in the whatsapp application because more users are using the application. In this study using the TF-IDF method in detecting cyberbullying that occurs in order to be able to add a reference for investigators. The conclusions that have been obtained from the simulation of conversations between four people in a whatsapp group get the results of the cyberbullying rate that the user "a" has a cyberbullying rate of 66.80%, the user "b" has a cyberbullying rate of 50%, the user "c" has a level cyberbullying is 33.19%, user "c" has a cyberbullying rate of 0% from the data proving that the TF-IDF method can help investigators detect someone who will commit cyberbullying actions but in its development a better way is needed when preprocessing so that the abbreviation or changing words can still be detected perfectly.

References

Riadi, I., Fadlil, A., & Fauzan, A. (2018b). Evidence Gathering and Identification of LINE Messenger on Android Device. International Journal of Computer Science and Information Security (IJCSIS), 16(June), 201–205.

F. B. Walean, Interviewee, [Interview]. 6 10 2015.

Riadi, I., Fadlil, A., & Fauzan, A. (2018a). A Study of Mobile Forensic Tools Evaluation on Android-Based LINE Messenger. International Journal of Advanced Computer Science and Applications, 9(10), 201–206. https://doi.org/10.14569/ijacsa.2018.091024

RSA, "2016: Current State of Cybercrime," 2013. [Online]. Available: https://www.rsa.com/content/dam/rsa/PDF/2016/05/2016-current-stateof-cybercrime.pdf. [Accessed 5 November 2016]

Umar, R., Riadi, I., & Muthohirin, B. F. (2019). Live forensics of tools on android devices for email forensics. 17(4), 1803–1809. https://doi.org/10.12928/TELKOMNIKA.v17i4.11748

Riadi, I., Sunardi, S., & Kadim, A. A. (2019). Monitoring Log Aplikasi Mobile Native Menggunakan Framework Grr Rapid Response. Jurnal Buana Informatika, 10(1), 1. https://doi.org/10.24002/jbi.v10i1.1909

Zamroni, G. M., & Riadi, I. (2019). Instant Messaging Forensic Tools Comparison on Android Operating System. 4(2), 137–148.

Christof Baron. (2019). Most popular global mobile messenger apps as of July 2019, based on number of monthly active users (in millions). Retrieved from https://www.statista.com/statistics/258749/most-popular-global-mobile-messenger-apps/

Feldman, R., & Sanger, J. 2007. The Text Mining HandBook. New York: Cambridge University Press.

Triawati, C. 2009. Metode Pembobotan Statistical Concept Based untuk Klastering dan Kategorisasi Dokumen Berbahasa Indonesia. Universitas Telkom.

Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, F. J. 2005. Text Mining: Predictive Methods fo Analyzing UnstructeredInformation. New York: Springer.

Dragut, E., Fang, F., Sistla, P., Yu, C., & Meng, W. 2009. Stop Word and Related Problems in Web Interface. Chicago: Computer Science Department University of Illinois.

Tala, F. Z. 2003. A Study Of Stemming Effects On Information Retrieval in Bahasa Indonesia. The Netherlands: Universiteitvan Amsterdam.

F. A. Hermawati and D. A. Zuhdi, “Aplikasi Sistem Temu Kembali Dokumen dengan Metode Vector Space Model,†KONVERGENSI, vol. 5, no. 2, pp. 38–49, 2009.

Santosh Kumar Sahu, Sauravranjan Sarangi and Sanjaya Kumar Jena, "A Detail Analysis on Intrusion Detection Datasets," in International Advance Computing Conference (IACC), 2014

Downloads

Published

2019-12-30

Issue

Section

Articles