The DOI matches its Crossref record.
Mobile Forensics for Cyberbullying Detection using Term Frequency - Inverse Document Frequency (TF-IDF)
Open accessAbstract
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.
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Few cyberbullying detection works use TF-IDF.
“In our context, we found few cyberbullying detection works using TF-IDF [46,47].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [46]
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Research works printed but not linked: 11
- I. Riadi, A. Fadlil, and A. Fauzan, "Evidence Gathering and Identification of LINE Messenger on Android Device," Int. J. Comput. Sci. Inf. Secur. (IJCSIS), vol. 16, no. June, pp. 201-205, 2018. Research Gate
- A. Fauzan, I. Riadi, and A. Fadlil, "Analisis Forensik Digital Pada Line Messenger Untuk Penanganan Cybercrime," Annu. Res. Semin. ISBN 979-587-626-0, vol. 2, no. 1, pp. 159-163, 2017. Online
- I. Riadi, A. Fadlil, and A. Fauzan, "A Study of Mobile Forensic Tools Evaluation on Android-Based LINE Messenger," Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 10, pp. 201-206, 2018. DOI: 10.14569/IJACSA.2018.091024 · DOI
- R. Umar, I. Riadi, and B. F. Muthohirin, "Live forensics of tools on android devices for email forensics," TELKOMNIKA, vol. 17, no. 4, pp. 1803-1809, 2019. DOI: 10.12928/telkomnika.v17i4.11748 · DOI
- I. Riadi, S. Sunardi, and A. A. Kadim, "Monitoring Log Aplikasi Mobile Native Menggunakan Framework Grr Rapid Response," J. Buana Inform., vol. 10, no. 1, p. 1, 2019. DOI: 10.24002/jbi.v10i1.1909 · DOI
- G. M. Zamroni and I. Riadi, "Instant Messaging Forensic Tools Comparison on Android Operating System," KINETIK, vol. 4, no. 2, pp. 137-148, 2019. DOI: 10.22219/kinetik.v4i2.735 · DOI
- J. Feldman, R., & Sanger, The Text Mining HandBook. New York: Cambridge University Press., 2007. DOI: 10.1017/CBO9780511546914 · DOI
- F. J. Weiss, S. M., Indurkhya, N., Zhang, T., & Damerau, Text Mining: Predictive Methods for Analyzing Unstructered Information. New York: Springer, 2005. Online
- W. Dragut, E., Fang, F., Sistla, P., Yu, C., & Meng, Stop Word and Related Problems in Web Interface. Chicago: Computer Science Department University of Illinois, 2009. DOI: 10.14778/1687627.1687667 · DOI
- S. Suprianto, A. Fadlil, and S. Sunardi, "Aplikasi Sistem Temu Kembali Angket Mahasiswa Menggunakan Metode Generalized Vector Space Model," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 6, no. 1, pp. 33-40, 2009. DOI: 10.25126/jtiik.2019611184 · DOI
- S. S. and S. K. J. Santosh Kumar Sahu, "A Detail Analysis on Intrusion Detection Datasets," Int. Adv. Comput. Conf., 2014. DOI: 10.1109/IAdCC.2014.6779523 · DOI
Cited by
Sorted by citations
A Review on Deep-Learning-Based Cyberbullying Detection
Hasan, M. T., Hossain, M. A. E., Mukta, M. S. H., Akter, A., Ahmed, M., & Islam, S. (2023) · Future Internet · doi:10.3390/fi15050179 · Cited by 103
“In our context, we found few cyberbullying detection works using TF-IDF [46,47].”
Method
Metadata
- Authors
- Riadi, I., Sunardi, S., & Widiandana, P.
- Affiliations
- Universitas Ahmad Dahlan (ID)
- Venue
- Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
- Publisher
- Universitas Ahmad Dahlan
- Published
- 2020-02-10
- Volume
- 5
- Issue
- 2
- Pages
- 68
- ISSN
- 2338-3062 (ISSN-L)
- 2338-3070
- OpenAlex
- W3008695072
- Cited by
- 11
- References
- 0
- Citations by year
- 2025: 1 · 2023: 5 · 2022: 2 · 2020: 3
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-sa
- Locations
- Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (publishedVersion, cc-by-sa)
- Optimum: Journal of Economics and Development (University of Ahmad Dahlan Yogyakarta) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Keywords
- cyberbullying detection, mobile forensics, TF-IDF, WhatsApp, chat conversations, cybercrime investigation, text classification
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