Implementasi Locally Adaptive K-Nearest Neighbor Algorithm based on Discrimination Class (DC-LAKNN) pada Kasus Deteksi Fake Account Instagram

Authors

  • Yosefani Kurniawan Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
  • Lily Puspa Dewi Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
  • Silvia Rostianingsih Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya

Keywords:

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Abstract

Instagram is one of the social media that has many users. Because of the ease of creating an account, many people create a fake account for stalking, spam attempts, fraud, photo or password theft, and even attacks another account with virus. Therefore, users need to be wary of unknown followers. Detecting account, which is real or fake can help users to be careful accepting some unknown follower. In addition, users can report to Instagram so that account can be deactivated.

In this thesis, a website-based application is designed that can detect the possibility of an Instagram account being a real or fake account. The detection is carried out using the Locally Adaptive K-Nearest Neighbor algorithm classification method based on Discrimination Class (DC-LAKNN) which is an adaptive algorithm from the K-Nearest Neighbor algorithm. This algorithm pay attention at discrimination class as the basis for classification. The attributes used in the classification are user follower count, following count, biography length, media count, username digit count, username length, user has profile picture, user is private. The end result is that the Locally Adaptive K-Nearest Neighbor algorithm based on Discrimination Class (DC-LAKNN) can be used to classify Instagram accounts with an accuracy of 96.23%.

References

[1] Akyon, F. C., & Esat Kalfaoglu, M. (2019). Instagram Fake and Automated Account Detection. In Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ASYU48272.2019.8946437

[2] Leidiyana, H. (2013). PENERAPAN ALGORITMA K-NEAREST NEIGHBOR UNTUK PENENTUAN RESIKO KREDIT KEPEMILIKAN KENDARAAN BEMOTOR. Jurnal Penelitian Ilmu Komputer, System Embedded & Logic, 1(1), 65-76. https://media.neliti.com/media/publications/155541-ID-penerapan-algoritma-k-nearest-neighbor-u.pdf

[3] kumparan. (2018, 07 17). Waspada, Punya Banyak Follower Palsu di Instagram Bisa Berbahaya. kumparanTECH. Retrieved 12 4, 2020, from https://kumparan.com/kumparantech/waspada-punya-banyak-follower-palsu-di-instagram-bisa-berbahaya-27431110790547314/full

[4] Mustakim, & F, G. O. (2016, Juni). Algoritma K-Nearest Neighbor Classification Sebagai Sistem Prediksi Predikat Prestasi Mahasiswa. Jurnal Sains, Teknologi dan Industri, 13(2), 195-202. http://ejournal.uin-suska.ac.id/index.php/sitekin/article/view/1688

[5] Pan, Z., Wang, Y., & Pan, Y. (2020, 06 26). A new locally adaptive k-nearest neighbor algorithm based on discrimination class. Knowledge-Based System, 204. https://doi.org/10.1016/j.knosys.2020.106185

[6] Setiawan, S. (2020, July 12). Membicarakan Precision, Recall, dan F1-Score. https://stevkarta.medium.com/membicarakan-precision-recall-dan-f1-score-e96d81910354

[7] Sukwadi, R., Inderawati, M. W., & Indah, M. Y. (2016). Perilaku Konsumen dalam Pemilihan Online Shop Instagram. Jurnal Metris, 17(02), 123 – 132. http://ojs.atmajaya.ac.id/index.php/metris/article/view/476

[8] T. T. Hanifa, S. Al-faraby, F. Informatika, and U. Telkom,“Analisis Churn Prediction pada Data Pelanggan PT . Telekomunikasi dengan Logistic Regression dan Underbagging,” vol. 4, no. 2, pp. 3210–3225, 2017.

[9] Zadorsky, J. (2020, December 10). Fake Instagram account asking youth for explicit photos interacted with 300 accounts: police. CTV NEWS. https://london.ctvnews.ca/fake-instagram-account-asking-youth-for-explicit-photos-interacted-with-300-accounts-police-1.522527

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Published

2022-01-28

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