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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF JUMANJI (Jurnal Masyarakat Informatika Unjani) Jurnal Informasi dan Komputer Jurnal Cendikia Jurnal ICT : Information Communication & Technology Jurnal Informatika dan Rekayasa Elektronik Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Ilmiah Wahana Pendidikan Istech Journal (International Science And Technology Journal) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Electrician : Jurnal Rekayasa dan Teknologi Elektro Journal Of Informatics And Busisnes JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Agus Komarudin
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Journal : Tematik : Jurnal Teknologi Informasi Komunikasi

 1 
Klasifikasi Aktivitas Pengguna yang Berpotensi Menyebabkan Kebocoran Informasi Sensitif Menggunakan Algoritma Random Forest Alda Amorita Azza; Asep Id Hadiana; Agus Komarudin
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2325

Abstract

Sensitive information leaks are a growing concern in cybersecurity, often caused by insider threats. To address this, a Random Forest classification model was developed to detect user activities that may lead to data leaks. By applying SMOTE-ENN for class balancing and optimizing model parameters, the study achieved remarkable accuracy. The model demonstrated a strong performance with an average F1-Score of 0.9167 in cross-validation and 0.9231 on the test data, reflecting its ability to identify abnormal activities with a balanced approach to precision and recall. Specifically, the model detected abnormal activities with Recall of 94.28%, meaning it effectively identified most of the risky activities while minimizing false positives. The AUC-ROC score of 0.9721 highlights the model's ability to distinguish between normal and abnormal behaviors. The results indicate that Random Forest, paired with SMOTE-ENN and parameter optimization, is an effective tool for detecting data leakage risks and insider threats, with potential for use in information security systems to monitor suspicious activities.
Co-Authors Aek Mustofa Aghnie Kurnia Alda Amorita Azza Bambang Suparapto Esmeralda C Djamal Esmeralda C. Djamal Ferry Kurniawan Galih Rakasiwi Hadiana, Asep Id Hafiz, Aliy Hartono, Rudi Ifo Wahyu Pratama Iman Muhdi Iskandar Iskandar Kuncoro Buwono Kurniawan Ikhsan Mia Narulita Anggraeni Mohamad Aris Hardianto Muhammad Dwi Laksono Nenza Nurfirmansyah Nuari Anisa Sivi Rasyid Ibnu Prasetyo Rezki Yuniarti Sri Hutamy Novianti Tacbir Hendro Pudjiantoro Wahidah, Nina Ikhwati Wina Witanti Yulison H. Crisnanto