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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Informatika Terpadu
Gevindo, Aprilian
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Computer Science & IT Decision Sciences, Operations Research & Management Education

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PENERAPAN MACHINE LEARNING UNTUK MENDETEKSI SERANGAN ANOMALI DALAM JARINGAN KOMPUTER : SYSTEMATIC LITERATURE REVIEW Gevindo, Aprilian; Hendrik, Billy
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13746

Abstract

Jaringan komputer berperan krusial dalam berbagai aspek kehidupan di era digital. Serangan siber dengan sifat anomali mengancam stabilitas sistem, baik dalam dunia bisnis maupun bagi individu. Teknik deteksi anomali diperlukan untuk mengidentifikasi aktivitas mencurigakan yang berpotensi mengganggu operasional sistem. Penelitian ini merupakan kajian literatur sistematis yang membahas penerapan machine learning dalam mendeteksi serangan anomali pada jaringan komputer. Fokus kajian ini berangkat dari meningkatnya kebutuhan keamanan siber di berbagai sektor, termasuk kesehatan, keuangan, pendidikan, dan pemerintahan. Machine learning dipilih karena kemampuannya mengenali pola dari data historis, sehingga meningkatkan akurasi dalam deteksi anomali secara adaptif. Hasil tinjauan terhadap penelitian sebelumnya membuktikan bahwa machine learning efektif dalam mengidentifikasi serangan anomali. Beragam algoritma yang telah diterapkan menunjukkan kemampuan dalam menghadapi ancaman keamanan jaringan yang tidak terduga.
Deteksi Perokok Menggunakan Algoritma You Only Look Once (YOLO) dan Convolutional Neural Network (CNN) Gevindo, Aprilian; Arlis, Syafri
Jurnal Informatika Terpadu Vol 12 No 1 (2026): Maret, 2026 (On Going)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v12i1.2783

Abstract

Image processing technology continues to advance and is widely used for visual identification of human activities, including monitoring smoking behavior in no-smoking areas. This study develops an automated smoking activity detection and recognition system based on digital image processing, combining YOLO (You Only Look Once) for object detection and a CNN (Convolutional Neural Network) as an image classifier. YOLO detects and crops human objects, while the CNN classifies smoking and non-smoking activities based on visual features. The preprocessed dataset contains 560 valid images per class (smoking and not smoking). Training results show 96.09% accuracy on the training set and 94.44% on the validation set, with stable loss, while model evaluation yields 94.44% accuracy, 92.55% precision, 96.67% recall, 94.57% F1-score, and Average Precision (AP), indicating excellent classification performance. The model can also detect smoking activities in real-time images and camera feeds, demonstrating the effectiveness of combining YOLO and a CNN for automated detection, with potential applications in no-smoking areas.
Co-Authors Hendrik, Billy Syafri Arlis