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All Journal IJIE (Indonesian Journal of Informatics Education) Jurnal Ilmiah Sistem Informasi
Rahmeisi, Nazli
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Tinjauan Literatur : Pendekatan Machine Learning Dalam Deteksi Serangan Web Gani, Eksa Umar; Rahmeisi, Nazli; Gani, Eksa; Arfriandi, Arief
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 3 (2025): November: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3w0vwc80

Abstract

The rapid growth of web technologies and online services has increased the exposure of web applications to cyber threats such as Cross-Site Scripting (XSS) and SQL Injection (SQLi). Conventional rule-based mechanisms, such as Web Application Firewalls (WAFs), often fail to detect emerging attack patterns. To address this, Machine Learning (ML) and Deep Learning (DL) have emerged as adaptive approaches for enhancing web attack detection. This study performs a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to analyze recent ML/DL-based detection methods. Of the 263 retrieved studies, 15 met the inclusion criteria for detailed review. The findings reveal that Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) are the most applied algorithms. At the same time, recent works emphasize Transformer-based and hybrid ML–DL models. These approaches achieved robust performance (accuracy 85–97%, F1-score >90%) but still face challenges in dataset representativeness, class imbalance, and computational cost. This review highlights future research directions in Explainable Artificial Intelligence (XAI), Federated Learning (FL), and adversarial robustness to develop more efficient and trustworthy web attack detection systems.
Tinjauan Literatur Sistematis tentang Deteksi Anomali Berbasis Kecerdasan Buatan untuk Intrusi Jaringan pada IoT Firmansyah, Mirza Putra; Nashir, Muhammad Naufal; Rahmeisi, Nazli; Augusta, Putri Safira; Arfriandi, Arief
Jurnal Ilmiah Sistem Informasi Vol. 5 No. 1 (2026): January: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/eqne0j35

Abstract

Pertumbuhan eksponensial Internet of Things (IoT) menghasilkan tantangan keamanan jaringan yang signifikan, terutama intrusi jaringan, di mana metode deteksi tradisional gagal melawan serangan adaptif. Kajian ini menyajikan Systematic Literature Review (SLR) berpedoman PRISMA untuk memahami pemanfaatan Artificial Intelligence (AI) dalam deteksi anomali pada IoT. Tujuannya adalah mengidentifikasi algoritma AI yang dominan, mengevaluasi performa, dan menilai pertimbangan efisiensi energi dalam penelitian terkini. SLR menganalisis 24 studi primer dari basis data Scopus yang diterbitkan antara tahun 2021 hingga 2025. Temuan utama menunjukkan dominasi algoritma deep learning: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), dan model hibrida/ensemble. Pendekatan ini terbukti sangat efektif, dengan akurasi deteksi seringkali melebihi 99% pada dataset benchmark. Selain itu, efisiensi sumber daya diidentifikasi sebagai isu sentral. Solusi yang diimplementasikan meliputi optimalisasi model ringan, kompresi Quantized Autoencoder (QAE), dan seleksi fitur, yang secara signifikan mengurangi konsumsi energi dan beban komputasi. Penelitian ini memberikan gambaran komprehensif mengenai state-of-the-art deteksi anomali IoT berbasis AI, menegaskan perlunya keseimbangan antara akurasi tinggi dan efisiensi sumber daya. Implikasinya, riset mendatang disarankan untuk memprioritaskan pengujian di dunia nyata, mengintegrasikan Explainable AI (XAI), dan mengembangkan metrik efisiensi yang terstandarisasi demi solusi keamanan IoT yang lebih praktis dan terpercaya.
Real-Time Emotion Recognition in Online Learning Using Google Teachable Rahmeisi, Nazli
IJIE (Indonesian Journal of Informatics Education) Vol 9, No 2 (2025): (IJIE) Indonesian Journal of Informatics Education - December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v9i2.110565

Abstract

Understanding learners’ emotional engagement in e-learning environments remains challenging due to the limited availability of non-verbal cues, despite its importance for motivation and participation. This paper proposes a facial emotion recognition approach using Google's Teachable Machine to support real-time emotion detection within online learning environments. The system analyzes facial expressions captured through a standard webcam to classify four basic emotional states: happy, sad, neutral, and angry. An experimental design was employed using simulated emotional expressions collected under controlled conditions, including adequate lighting and front-facing facial images. The results indicate that the system can provide instructors with additional affective cues to support formative assessment and instructional awareness in synchronous online learning. The proposed approach emphasizes practical instructional feasibility and accessibility compared to more complex emotion recognition models, as it does not require specialized hardware or advanced programming skills.
Co-Authors Arfriandi, Arief Augusta, Putri Safira Firmansyah, Mirza Putra Gani, Eksa Gani, Eksa Umar Nashir, Muhammad Naufal