IDENTIFYING FAKE ACCOUNTS IN SOCIAL MEDIA COMMERCIAL VIDEOS USING SUPPORT VECTOR MACHINE METHOD

Windi Rabiatul Aini; I Gede Iwan Sudipa; I Putu Dedy  Sandana; Desak Made Dwi Utami Putra; I Gusti Agung Indrawan

Authors

Windi Rabiatul Aini Institut Bisnis dan Teknologi Indonesia
I Gede Iwan Sudipa Institut Bisnis dan Teknologi Indonesia
I Putu Dedy Sandana Institut Bisnis dan Teknologi Indonesia
Desak Made Dwi Utami Putra Institut Bisnis dan Teknologi Indonesia
I Gusti Agung Indrawan Institut Bisnis dan Teknologi Indonesia

Keywords:

Fake Account, Commercial Video, Social Media, Instagram, Support Vector Machine

Abstract

In today's society, many fake accounts actively comment on commercial videos on social media, especially Instagram. These fake accounts are often used to manipulate public perception of certain products or services, either to improve the product image and the image of the influencer, to damage the reputation of competitors. Therefore, this research applies the Support Vector Machine (SVM) method to detect and classify fake accounts based on characteristics such as number of followers, interactions, and activity patterns. SVM was chosen because of its ability to handle classification problems with a high degree of accuracy and the ability to separate complex data. The results showed that the SVM model was able to achieve 71% accuracy, with 68% precision, 84% recall, and 75% f1-score. These numbers indicate that SVM is quite effective in detecting fake accounts. The SVM model successfully identified a number of fake accounts with uniform interaction patterns and unusual activity, suggesting that most fake accounts tend to make repetitive and inauthentic comments. This research successfully contributes to the development of techniques to tackle the abuse of fake accounts on social media, thereby reducing the negative impact on consumer perception of commercial videos on the Instagram platform.

References

Adawiyah, B. R., & Boer, K. M. (2023). Analisis Customer Engagement Pada Akun Media Sosial Instagram Beda Sepatunya. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 4(3), 1523–1540. https://doi.org/10.35870/jimik.v4i3.368

Adellia, T. P. (2024). ANALISIS INFLUENCER MARKETING PADA PLATFORM MEDIA SOSIAL INSTAGRAM HOTEL ROYAL PALM JAKARTA. 3(2), 47–55.

Aditya, D. R., Supriyati, E., & Listyorini, T. (2022). Analisis Sentimen Pengguna Twitter Terhadap Rokok Elektrik (Vape) Di Indonesia Menggunakan Metode Naïve Bayes. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 7(1), 43–50. https://doi.org/10.29100/jipi.v7i1.2145

Alamsyah, H., Cahyana, Y., & Pratama, A. R. (2023). Deteksi Fake Review Menggunakan Metode Support Vector Machine dan Naïve Bayes Di Tokopedia. Jurnal Ilmiah Teknik Informatika Dan Sistem Informasi.

Armaeni, P. P., Wiguna, I. K. A. G., & Parwita, W. G. S. (2024). Sentiment Analysis of YouTube Comments on the Closure of TikTok Shop Using Naïve Bayes and Decision Tree Method Comparison. Jurnal Galaksi, 1(2), 70–80. https://doi.org/10.70103/galaksi.v1i2.15

Danu, N. I. (2021). Identifikasi Berita Hoax Menggunakan Kombinasi Metode K-Nearest Neigbor (KNN) dan TF-IDF Berbasis Web Dengan Menggunakan Framework Codeigniter. eprints.uwp.ac.id.

Duei Putri, D., Nama, G. F., & Sulistiono, W. E. (2022). Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Informatika Dan Teknik Elektro Terapan, 10(1), 34–40. https://doi.org/10.23960/jitet.v10i1.2262

Ernianti Hasibuan, & Elmo Allistair Heriyanto. (2022). Analisis Sentimen Pada Ulasan Aplikasi Amazon Shopping Di Google Play Store Menggunakan Naive Bayes Classifier. Jurnal Teknik Dan Science, 1(3), 13–24. https://doi.org/10.56127/jts.v1i3.434

Gunawan, H., Yulia, Y., & Budhi, G. S. (2022). Penerapan Machine Learning dalam mendeteksi Fake Account pada Instagram. Jurnal Infra, 121.

Hikmah Nur Karimah, R., & Fadillah, F. (2021). Buzz Marketing as a New Practice of Digital Advertising. IMOVICCON Conference Proceeding, 2(1), 102–110. https://doi.org/10.37312/imoviccon.v2i1.46

Louis Paskalis Dwinanto, Diana RW Napitupulu, A. B. (2024). Analisis Yuridis Terhadap Aktivitas Fake Engagement Pada Social Media. Jurnal Cahaya Mandalika, 1801–1807.

Moh. Fatur Gifahri, Harifuddin Thahir, Syamsul Bahri Dg. Parani, & Suryadi Samudra. (2024). Analisis Efektivitas Marketing Media Sosial Instagram Terhadap Keputusan Pelanggan Dezato Brownies. Maeswara : Jurnal Riset Ilmu Manajemen Dan Kewirausahaan, 2(2), 73–80. https://doi.org/10.61132/maeswara.v2i2.728

Purbolaksono, M. D., Irvan Tantowi, M., Imam Hidayat, A., & Adiwijaya, A. (2021). Perbandingan Support Vector Machine dan Modified Balanced Random Forest dalam Deteksi Pasien Penyakit Diabetes. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(2), 393–399. https://doi.org/10.29207/resti.v5i2.3008

Putra, A., Herdiani, A., & Asror, I. (2019). Identifikasi Fake Account Twitter Menggunakan Support Vector Machine. EProceedings of Engineering, 6(2), 8852–8858.

Sahputri, S., Pristika, F. J., Kasih, A. C., Irwan, M., & Nasution, P. (2023). Analisis Pemanfaatan Media Sosial Sebagai Sarana Promosi Produk UMKM “ Halua ” di Desa Paya Rengas. INNOVATIVE: Journal Of Social Science Research Volume, 3(5), 5181–5191.

Siti Aisah, I., Irawan, B., & Suprapti, T. (2024). Algoritma Support Vector Machine (Svm) Untuk Analisis Sentimen Ulasan Aplikasi Al Qur’an Digital. JATI (Jurnal Mahasiswa Teknik Informatika), 7(6), 3759–3765. https://doi.org/10.36040/jati.v7i6.8263

Wanda, P., Hiswati, M. E., Diqi, M., & ... (2021). Re-Fake: Klasifikasi Akun Palsu di Sosial Media Online menggunakan Algoritma RNN. … Seminar Nasional Sains ….

Yanti, N., & Asana, I. (2023). Sistem Klasifikasi Pengajuan Kredit dengan Metode Support Vector Machine (SVM). Jurnal Sistem Cerdas.

Yunianto, I., & Mada, U. G. (2021). Menghitung Probabilitas Akun Palsu Instagram dengan Bayesian Inference. February, 2–4. https://doi.org/10.13140/RG.2.2.17505.63847

Yuniar, E., Utsalinah, D. S., & Wahyuningsih, D. (2022). Implementasi Scrapping Data Untuk Sentiment Analysis Pengguna Dompet Digital dengan Menggunakan Algoritma Machine Learning. Jurnal Janitra Informatika Dan Sistem Informasi, 2(1), 35–42. https://doi.org/10.25008/janitra.v2i1.145