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All Journal Jurnal Simetris Jurnal Ilmiah Sistem Informasi
Rahmeisi, Nazli
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Computer Science & IT

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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.
The Role of Big Data Analytics and Artificial Intelligence in Business Strategy: A Systematic Review Rahmeisi, Nazli; Sachroni, Mu'alfi Fahrul Fanani; Andyanto, Yehezkiel Nesta; Aprilianto, Rizky Ajie; Pribadi, Feddy Setio
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.14909

Abstract

The accelerating digital transformation across industries has intensified the need for datadriven approaches in strategic business management. This study conducts a Systematic Literature Review (SLR) to examine how Big Data Analytics (BDA) and Artificial Intelligence (AI) influence business strategy formulation, risk management, and organizational competitiveness. Guided by the PICOC framework and PRISMA 2020 protocol, 27 peer-reviewed journal articles published between 2020 and 2024 were analyzed through thematic synthesis and bibliometric visualization using VOSviewer. The results indicate that BDA and AI enhance strategic decision-making, operational efficiency, and risk mitigation through predictive insights and real-time analytics. However, their strategic integration remains limited due to socio-technical challenges such as inadequate analytical capability, weak data governance, and organizational resistance. The review highlights that the true strategic value of BDA and AI emerges when these technologies are embedded within long-term strategic planning, data governance, and sustainability frameworks, rather than treated merely as operational tools. This study contributes to strategic management literature by synthesizing cross-sectoral evidence and offering insights into how data-driven intelligence fosters long-term competitiveness and sustainable business transformation.
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.
Co-Authors Andyanto, Yehezkiel Nesta Aprilianto, Rizky Ajie Arfriandi, Arief Augusta, Putri Safira Feddy Setio Pribadi Firmansyah, Mirza Putra Gani, Eksa Gani, Eksa Umar Nashir, Muhammad Naufal Sachroni, Mu'alfi Fahrul Fanani