The Detection of Bullying Against Indonesian National Team Players Using Support Vector Machine

Sunggito  Oyama; Desty Nur  Kumalasari

doi:10.47709/cnahpc.v7i2.5701

Authors

Sunggito Oyama Informatics Study Program, Faculty of Science and Technology, Universitas PGRI Yogyakarta, Indonesia
Desty Nur Kumalasari Informatics Study Program, Faculty of Science and Technology, Universitas PGRI Yogyakarta, Indonesia

DOI:

https://doi.org/10.47709/cnahpc.v7i2.5701

Keywords:

Detection, Bullying, Instagram, Indonesian National Team Players, Support Vector Machine (SVM)

Abstract

Detection is a process to check or conduct an examination of something using certain methods and techniques. Detection can be used for various problems, for example in detection bullying, especially on social media, is a significant problem with negative impacts on mental health, especially for public figures such as Indonesian National Team players. This study aims to detect bullying comments on the Instagram platform using the Support Vector Machine (SVM) algorithm. The research dataset consists of 3,100 comments collected from the official Indonesian National Team account, which are classified into bullying and non-bullying categories. The data preprocessing stages include case folding, tokenizing, normalization, removing stopwords, and stemming. The processed data was analyzed using the Term Frequency-Inverse Document Frequency (TF-IDF) method for feature weighting before being classified using SVM with a linear kernel and Naïve Bayes. The results showed that SVM performed better with an accuracy of 89%, a bullying category precision reaching 93%, and a recall of 83%. Meanwhile, the Naïve Bayes method produced an accuracy of 79%, with a bullying category precision of 76% and a recall of 86%. The non-bullying category in Naïve Bayes has higher precision (84%) but lower recall (72%). Thus, SVM is proven to be more effective in detecting negative comments due to a better balance between precision and recall. However, challenges such as informal language variations and data imbalance remain obstacles in the development of this model. This study contributes to the development of cyberbullying detection technology and supports the creation of a healthier social media environment.

Downloads

Download data is not yet available.

References

Ashilah, K., Digital, M., Sosial, M., & Media, S. (2021). INSTAGRAM SEBAGAI MEDIA PEMASARAN DIGITAL WASABI SUSHI JAPANESSE FOOD JEMBER Khittah Ashilah Universitas Islam Jember. 1(September), 149–160.

Dietrich, L., Jurkowski, S., Schwarzer, N., & Zimmermann, D. (2023). The role of teachers in the bullying involvement of students with emotional and behavioral difficulties. Teaching and Teacher Education, 135(August), 104311. https://doi.org/10.1016/j.tate.2023.104311

Galahartlambang, Y., & Khotiah, T. (2021). Visualisasi Data Dari Dataset COVID-19 Menggunakan Pemrograman Python. 4902(x).

Graham, M., Avery, E. J., & Ph, D. (2013). Government Public Relations and Social Media?: An Analysis of the Perceptions and Trends of Social Media Use at the Local Government Level. 7(4), 1–21.

Ikbal, M., Ghofur, M. A., Studi, P., Komunikasi, I., Tribhuwana, U., & Malang, T. (2019). EFEKTIVITAS UNGGAHAN FOTO DI AKUN INSTAGRAM. 8(1), 52–64.

Muhaddisi, A., Prastowo, B. N., Utami, D., & Putri, K. (2021). Sentiment Analysis With Sarcasm Detection On P olitician ’ s Instagram. 15(4), 349–358.

Noor, F. R., Agustin, D., & Sirodj, N. (2024). Penerapan Support Vector Machine untuk Klasifikasi Opini Masyarakat Terhadap Isu Bullying. 57–66.

Pambudi, A. R., Informatika, T., Komputer, F. I., Karawang, U. S., Segmentasi, M., Based, R., Contour, A., Tepi, D., Metode, M., & Dengan, C. (2016). Deteksi keaslian uang kertas berdasarkan watermark dengan pengolahan citra digital. 69–74.

Pratama, F., Nasir, M., & Sauda, S. (2020). Implementasi Metode Klasifikasi Dengan Algoritma Support Vector Machine Untuk Menentukan Stok Persediaan Barang Pada Koperasi Karyawan Pangan Utama. 1(2), 71–81.

Purwokerto, U. A., & Kunci, K. (2021). PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN DECISION TREE UNTUK ANALISIS SENTIMEN REVIEW KOMENTAR PADA APLIKASI TRANSPORTASI ONLINE Abstraksi Keywords?: Pendahuluan Tinjauan Pustaka. 2(2).

Rodríguez, Velastequí, M. (2019). PERANAN NEW MEDIA DALAM TRANSFORMASI FUNGSI KOMUNIKATOR DAN FUNGSI KONSTRUKSI (DRAMATURGY AKUN INSTAGRAM WALIKOTA DENPASAR RAI MANTRA TAHUN 2019). 1–23.

Septiana, T., Muda, M. A., Budiyanto, D., Pratama, M., & Putra, W. (2024). Analisis Penggunaan Support Vector Machine pada Deteksi Dini Penyakit Diabetes Melitus. 4(3), 1631–1640.

Siswanto, S., Mar’ah, Z., Sabir, A. S. D., Hidayat, T., Adhel, F. A., & Amni, W. S. (2022). The Sentiment Analysis Using Naïve Bayes with Lexicon-Based Feature on TikTok Application. Jurnal Varian, 6(1), 89–96. https://doi.org/10.30812/varian.v6i1.2205

Soraya, A. (2023). Klasifikasi sentimen pada opinion cyberbullying di twitter menggunakan metode support vector machine dan naïve bayes.

Wahyuningsih, N. (2023). Perbandingan metode klasifikasi dalam analisis sentimen masyarakat terhadap identitas kependudukan digital (ikd). 8(4), 1218–1227.

Yuliani, N. (2019). Fenomena kasus bullying di sekolah.