Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter

  • Maradona Maradona Universitas Amikom Yogyakarta
  • Kusrini Kusrini Universitas AMIKOM Yogyakarta
  • Alva Hendi Muhammad Universitas AMIKOM Yogyakarta
Keywords: cyberbullying, social media, twitter, decision tree, k-nearest neighbor

Abstract

This research focuses on analyzing the impact of social media on society, particularly addressing the issue of cyberbullying on the Twitter platform. Based on statistics, the majority of internet users in Indonesia actively utilize social networks, with Twitter being the most dominant platform used for communication and interaction. Therefore, cyberbullying cases often occur on this social media platform. In this study, two classification methods, namely Decision Tree and K-Nearest Neighbor (KNN), were employed to classify cyberbullying-related messages on Twitter. The aim of this research is to compare the performance of these two methods and to identify early signs of cyberbullying as relevant digital evidence for legal proceedings. The dataset used in this study consists of 650 comment records from the period 2019 to 2021, with predefined labels. The analysis results indicate that K-Nearest Neighbor achieved the highest accuracy, reaching 75.99%, compared to Decision Tree with 65.00%. Hence, K-Nearest Neighbor is considered a more effective method for cyberbullying analysis on the Twitter platform. Additionally, the identification of early signs of cyberbullying in comment id 2 can serve as relevant digital evidence for legal purposes. This research provides better insights into the effectiveness of classification in addressing cyberbullying issues on the Twitter platform.

References

A. Wijayanto, I. Riadi, Y. Prayudi, And T. Sudinugraha, “Network Forensics Against Address Resolution Protocol Spoofing Attacks Using Trigger , Acquire , Analysis , Report , Action Method,” Register: Jurnal Ilmiah Teknologi Sistem Informasi, Vol. 8, No. July, Pp. 156–169, 2022, Doi: Http://Doi.Org/10.26594/Register.V8i2.2953.

A. Wijayanto, I. Riadi, And Y. Prayudi, “Taara Method For Processing On The Network Forensics In The Event Of An Arp Spoofing Attack,” Jurnal Resti (Rekayasa Sistem Dan Teknologi Informasi), Vol. 7, No. 2, Pp. 208–217, Mar. 2023, Doi: 10.29207/Resti.V7i2.4589.

I. Riadi And N. H. Siregar, “Mobile Forensic Analysis Of Signal Messenger Application On Android Using Digital Forensic Research Workshop ( Dfrws ) Framework,” Ingénierie Des Systèmes D ’ Information, Vol. 27, No. 6, Pp. 903–913, 2022, Doi: Https://Doi.Org/10.18280/Isi.270606.

Kementerian Komunikasi Dan Informatika, “Warganet Meningkat, Indonesia Perlu Tingkatkan Nilai Budaya Di Internet.” Accessed: May 02, 2022. [Online]. Available: Https://Aptika.Kominfo.Go.Id/2021/09/Warganet-Meningkat-Indonesia-Perlu-Tingkatkan-Nilai-Budaya-Di-Internet/

C. H. C. Noh And M. Y. Ibrahim, “Kajian Penerokaan Buli Siber Dalam Kalangan Pelajar Umt,” Procedia Soc Behav Sci, Vol. 134, Pp. 323–329, 2014, Doi: Https://Doi.Org/10.1016/J.Sbspro.2014.04.255.

Herman, I. Riadi, And I. A. Rafiq, “Forensic Mobile Analysis On Social Media Using National Institute Standard Of Technology Method,” Ingénierie Des Systèmes D ’ Information, Vol. 12, No. 6, Pp. 707–713, 2022, Doi: Https://Doi.Org/10.18280/Ijsse.120606.

I. Riadi, Sunardi, And P. Widiandana, “Cyberbullying Detection On Instant Messaging Services Using Rocchio And Digital Forensics Research Workshop Framework,” Journal Of Engineering Science And Technology, Vol. 17, No. 2, Pp. 1408–1421, 2022.

T. K. H. Chan, C. M. K. Cheung, And Z. W. Y. Lee, “Cyberbullying On Social Networking Sites: A Literature Review And Future Research Directions,” Information And Management, Vol. 58, No. 2, P. 103411, 2021, Doi: Https://Doi.Org/10.1016/J.Im.2020.103411.

K. D. Gorro, M. J. G. Sabellano, K. Gorro, C. Maderazo, And K. Capao, “Classification Of Cyberbullying In Facebook Using Selenium And Svm,” 2018 3rd International Conference On Computer And Communication Systems (Icccs), Pp. 183–186, 2018, Doi: Https://Doi.Org/10.1109/Ccoms.2018.8463326.

L. Fazry And N. Cipta Apsari, “Pengaruh Media Sosial Terhadap Perilaku Cyberbullying Di Kalangan Remaja,” Jurnal Pengabdian Dan Penelitian Kepada Masyarakat, Vol. 2, No. 1, Pp. 28–36, 2021, Doi: Https://Doi.Org/10.24198/Jppm.V2i1.33435.

A. Rahman, N. Zaman, A. T. Asyhari, S. M. N. Sadat, P. Pillai, And R. Abdullah, “Ad Hoc Networks Spy-Bot : Machine Learning-Enabled Post Filtering For Social Network-Integrated Industrial Internet Of Things,” Ad Hoc Networks, Vol. 121, No. March, P. 102588, 2021, Doi: Https://Doi.Org/10.1016/J.Adhoc.2021.102588.

F. Tapia And C. Aguinaga, “Detección De Patrones De Comportamiento A Través De Redes Sociales Como Twitter , Utilizando Técnicas De Minería De Datos Como Método Para Detectar El Acoso Cibernético Detection Of Behavior Patterns Through Social Networks Like Twitter , Using Data Minin,” 2018 7th International Conference On Software Process Improvement (Cimps), Pp. 111–118, 2018, Doi: 10.1109/Cimps.2018.8625625.

W. M. Baihaqi Et Al., “Kombinasi K-Means Dan Support Vector Machine ( Svm ) Untuk K-Means And Support Vector Machine ( Svm ) Combination To Predict Sara Elements On Tweet,” Vol. 7, No. 3, Pp. 501–510, 2020, Doi: 10.25126/Jtiik.202072126.

A. Muhariya, I. Riadi, Y. Prayudi, And I. A. Saputro, “Utilizing K-Means Clustering For The Detection Of Cyberbullying Within Instagram Comments,” Ingénierie Des Systèmes D Information, Vol. 28, No. 4, Pp. 939–949, Aug. 2023, Doi: 10.18280/Isi.280414.

A. Muhariya, A. Riadi, And I. Prayudi, “Cyberbullying Analysis On Instagram Using K-Means Clustering,” Juita: Jurnal Informatika, Vol. 10, No. 2, Pp. 261–271, 2022, Doi: 10.30595/Juita.V10i2.14490.

N. F. Hasan, “Deteksi Cyberbullying Pada Facebook Menggunakan Algoritma K-Nearest Neighbor,” Journal Of Smart System, Vol. 1, No. 1, Pp. 35–44, 2021, Doi: 10.36728/Jss.V1i1.1605.

A. Pamuji And H. S. Setiawan, “Prediksi Cyberbullying Sebagai Alat Konseling Cyber Dengan Data Mining Classification,” Bit (Fakultas Teknologi Informasi Universitas Budi Luhur), Vol. 19, No. 1, Pp. 29–36, 2022.

Rsa, “2016: Current State Of Cybercrime,” P. 7, 2016.

S. Khairunnisa, A. Adiwijaya, And S. Al Faraby, “Pengaruh Text Preprocessing Terhadap Analisis Sentimen Komentar Masyarakat Pada Media Sosial Twitter (Studi Kasus Pandemi Covid-19),” Jurnal Media Informatika Budidarma, Vol. 5, No. 2, P. 406, Apr. 2021, Doi: 10.30865/Mib.V5i2.2835.

A. Fauzi, “Bulletin Of Data Science Penerapan Algoritma Text Mining Dan Lexrank Dalam Meringkas Teks Secara Otomatis,” Media Online), Vol. 1, No. 2, Pp. 65–72, 2022, [Online]. Available: Https://Ejurnal.Seminar-Id.Com/Index.Php/Bulletinds

S. K. Sahu, S. Sarangi, And S. K. Jena, “A Detail Analysis On Intrusion Detection Datasets,” Souvenir Of The 2014 Ieee International Advance Computing Conference, Iacc 2014, Pp. 1348–1353, 2014, Doi: 10.1109/Iadcc.2014.6779523.

R. Riyaddulloh And A. Romadhony, “Normalisasi Teks Bahasa Indonesia Berbasis Kamus Slang Studi Kasus: Tweet Produk Gadget Pada Twitter,” Eproceedings Of Engineering, Vol. 8, No. 4, Pp. 4216–4228, 2021.

A. Guterres, Gunawan, And J. Santoso, “Stemming Bahasa Tetun Menggunakan Pendekatan Rule Based,” Teknika, Vol. 8, No. 2, Pp. 142–147, Oct. 2019, Doi: 10.34148/Teknika.V8i2.224.

I. M. Suwija Putra, Y. Adiwinata, D. P. Singgih Putri, And N. P. Sutramiani, “Extractive Text Summarization Of Student Essay Assignment Using Sentence Weight Features And Fuzzy C-Means,” International Journal Of Artificial Intelligence Research, Vol. 5, No. 1, Jan. 2021, Doi: 10.29099/Ijair.V5i1.187.

A. H. Nasrullah, “Implementasi Algoritma Decision Tree Untuk Klasifikasi Produk Laris,” Jurnal Ilmiah Ilmu Komputer, Vol. 7, No. 2, 2021, [Online]. Available: Http://Ejournal.Fikom-Unasman.Ac.Id

Published
2023-12-29
Section
Articles