Implementasi Kecerdasan Buatan dalam Deteksi Cybercrime: Komparasi Model Naive Bayes dan SVM pada Pola  Komentar Judi Online

Ardiansyah Ardiansyah; Abdul Muin Nasution; Muhammad Syahputra Novelan

doi:10.60076/indotech.v3i3.1762

Authors

Ardiansyah Ardiansyah Universitas Pembangunan Panca Budi
Abdul Muin Nasution Randwick International Research and Analysis Institute
Muhammad Syahputra Novelan Universitas Pembangunan Panca Budi

DOI:

https://doi.org/10.60076/indotech.v3i3.1762

Keywords:

Kecerdasan Buatan, Machine Learning, Naive Bayes, Support Vector Machine, Judi online

Abstract

Maraknya promosi judi online di platform media sosial seperti YouTube telah menjadi ancaman serius dalam kategori cybercrime di Indonesia. Pola komentar yang bervariasi dan penggunaan bahasa non-formal menyulitkan identifikasi konten secara manual. Penelitian ini bertujuan untuk mengimplementasikan teknologi Kecerdasan Buatan (AI) melalui pendekatan Machine Learning untuk mendeteksi secara otomatis pola komentar judi online. Dua algoritma populer, yaitu Naive Bayes (NB) dan Support Vector Machine (SVM), digunakan dan dibandingkan kinerjanya untuk menentukan model klasifikasi terbaik. Data penelitian diekstraksi dari komentar YouTube berbahasa Indonesia, yang kemudian melewati tahap pra-pemrosesan teks meliputi case folding, tokenization, stopword removal, dan stemming. Fitur teks ditransformasikan menjadi bentuk numerik menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan perbandingan kinerja kedua algoritma berdasarkan metrik akurasi, precision, recall, dan f1-score. Temuan ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem filtrasi konten negatif otomatis guna memperkuat keamanan siber di ekosistem digital Indonesia.

Downloads

Download data is not yet available.

References

A. M. Nasution, M. Zarlis, and S. Suherman, “Analysis and implementation of Honeyd as a low-interaction honeypot in enhancing security systems,” Random International Social Science Journal, vol. 2, no. 1, pp. 124–135, Jan. 2021, doi: 10.47175/rissj.v2i1.209.

A. Widiyanto, M. Prameswari, and M. A. Latief, “Gambling comments detection on YouTube: A comparative study of tree-based boosting, LSTM, and GRU models,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 23, no. 2, pp. 144–160, Jul. 2025, doi: 10.12962/j24068535.v23i2.a1305.

D. Diffran N. C., R. Handayani, V. B. Lestari, and S. Febriani, “Sentiment analysis of public opinion on online gambling through social media using convolutional neural network,” Journal of Informatics and Telecommunication Engineering, vol. 9, no. 1, Jul. 2025, doi: 10.31289/jite.v9i1.15024.

M. Z. Ammar, R. E. Putra, and Y. Yamasari, “Deep learning-based detection of online gambling promotion spam in Indonesian YouTube comments,” Journal of Applied Informatics and Computing, vol. 9, no. 6, pp. 3632–3641, Dec. 2025, doi: 10.30871/jaic.v9i6.11240.

S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh text preprocessing terhadap analisis sentimen komentar masyarakat pada media sosial Twitter (studi kasus: Pilpres 2019),” Jurnal Media Informatika Budidarma, vol. 5, no. 2, pp. 406–414, Apr. 2021, doi: 10.30865/mib.v5i2.2835.

M. F. Madjid, D. E. Ratnawati, and B. Rahayudi, “Sentiment analysis on app reviews using support vector machine and naïve Bayes classification,” SinkrOn, vol. 7, no. 1, pp. 556–562, Feb. 2023, doi: 10.33395/sinkron.v8i1.12161.

A. Firda and M. Y. Harahap, “Optimalisasi sarana dan prasarana di MTS PAB 5 Klambir Lima untuk meningkatkan mutu pendidikan pada era digital,” Jurnal Ilmiah Al-Hadi, vol. 11, no. 1, pp. 133–143, Oct. 2025, doi: 10.54248/alhadi.v11i1.4966.

D. R. Arrayyan, R. G. Guntara, and M. R. Nugraha, “Deteksi komentar spam judi online berbahasa Indonesia menggunakan XGBoost dan TF-IDF,” Jurnal Algoritma, vol. 22, no. 2, pp. 2066–2075, Nov. 2025, doi: 10.33364/algoritma/v.22-2.3012.

M. Wahyudi and M. S. Novelan, “Perbandingan kinerja K-NN dan naïve Bayes dalam tata kelola teknologi informasi untuk menganalisa tingkat kepuasan pengguna aplikasi Shopee,” Journal of Science and Social Research, vol. 8, no. 3, pp. 3424–3431, Aug. 2025, doi: 10.54314/jssr.v8i3.3547.

N. L. Ratniathi et al., “Sentiment analysis of YouTube comments on Indonesian presidential election using SVM,” Journal of Physics: Conference Series, vol. 1469, no. 1, p. 012101, 2020, doi: 10.1088/1742-6596/1469/1/012101.

V. Nurmaylina and Y. Akbar, “Sentiment analysis of social media X users towards legislators engaged in online gambling using naïve Bayes algorithm,” International Journal of Software Engineering and Computer Science, vol. 4, no. 3, pp. 1128–1136, 2024, doi: 10.35870/ijsecs.v4i3.3079.

A. Irianti, H. Halimah, S. Sutedi, and M. Agariana, “Integration of BERT and SVM in sentiment analysis of Twitter/X regarding Constitutional Court Decision No. 60/PUU-XXII/2024,” Jurnal Teknik Informatika (JUTIF), vol. 6, no. 2, pp. 469–482, Apr. 2025, doi: 10.52436/1.jutif.2025.6.2.4068.

P. H. Putra and M. S. Novelan, “Perancangan aplikasi penentuan kualitas sayuran berdasarkan warna menggunakan data mining,” in Proc. Seminar of Social Sciences Engineering and Humaniora (SCENARIO), Univ. Pembangunan Panca Budi, Medan, Indonesia, Apr. 2022.

F. Fahrudin, “Komentar judi online dataset,” Kaggle, 2023. [Online]. Available: https://www.kaggle.com/datasets/fahruu/komentar-judi-online. [Accessed: 25-Dec-2025].

D. Tehare, V. Mogadpalli, N. R. Gupta, S. Takalkar, and M. Patil, “Role of Python programming language in data science and big data processing,” International Journal of Technology & Emerging Research, vol. 1, no. 3, pp. 5–18, Jul. 2025.

S. Octarina, F. M. Puspita, E. Yuliza, and I. Indrawati, “Pendampingan penggunaan Google Colab pada pembelajaran Python dan machine learning bagi dosen matematika di Palembang,” Jurnal Pepadu, vol. 6, no. 1, pp. 56–66, 2025, doi: 10.29303/pepadu.v6i1.6457.

V. B. Lestari and C. A. P. Hutagalung, “Evaluation of TF-IDF extraction techniques in sentiment analysis of Indonesian-language marketplaces using SVM, logistic regression, and naïve Bayes,” J-KOMA: Journal of Computer Science and Applications, vol. 8, no. 1, 2025, doi: 10.21009/j-koma.v8i1.05.

E. Helmud, F. Fitriyani, and P. Romadiana, “Classification comparison performance of supervised machine learning random forest and decision tree algorithms using confusion matrix,” Jurnal Sistem Informasi dan Komputer (SISFOKOM), vol. 13, no. 1, Feb. 2024, doi: 10.32736/sisfokom.v13i1.1985.

M. G. Pradana, “Penggunaan fitur wordcloud dan document term matrix dalam text mining,” Jurnal Ilmiah Informatika (JIF), vol. 8, no. 1, 2025