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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Infotekmesin JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Sistem Informasi dan Sains Teknologi Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Journal of Embedded Systems, Security and Intelligent Systems Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Accounting Law Communication and Technology Jurnal Testing dan Implementasi Sistem Informasi Repeater: Publikasi Teknik Informatika dan Jaringan INOVTEK Polbeng - Seri Informatika
Agus Tedyyana, Agus
Politeknik Negeri Bengkalis
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering

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Journal : journal of embedded systems security and intelligent systems

 1 
Multiclass IoT Intrusion Detection Based on Particle Swarm Optimization-Tuned Light Gradient Boosting Machine Fajar Ratnawati; Agus Tedyyana
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 2 (2026): June 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2612

Abstract

Purpose – This study aims to develop a robust multiclass intrusion detection system (IDS) for Internet of Things (IoT) environments by optimizing Light Gradient Boosting Machine (LightGBM) using Particle Swarm Optimization (PSO), with a focus on improving performance under severe class imbalance. Design/methods/approach – A PSO-based hyperparameter tuning framework is applied to LightGBM, where Macro F1-score is used as the fitness function to ensure balanced class performance. The model is evaluated on the RT-IoT2022 dataset using a leakage-safe stratified 70:15:15 split. Performance is assessed using Accuracy, Macro Precision, Macro Recall, Macro F1-score, Weighted F1-score, and Matthews Correlation Coefficient (MCC). Experiments are repeated across 10 runs, and statistical significance is validated using the Wilcoxon signed-rank test. Findings - The proposed PSO-LightGBM model significantly outperforms the baseline LightGBM. It achieves 99.75% accuracy, 97.10% macro F1-score, 99.75% weighted F1-score, and 99.37% MCC, compared to 85.36%, 25.44%, 84.23%, and 63.67%, respectively, for the baseline. The model demonstrates substantial improvement in minority-class detection, reducing misclassification and preventing class collapse observed in the baseline. Research implications/limitations – The findings highlight the effectiveness of Macro-F1-guided optimization for imbalanced multiclass IoT intrusion detection. However, the evaluation is limited to a single dataset and centralized experimental setting, which may affect generalizability. Originality/value – This study contributes a leakage-safe, Macro-F1-driven PSO-LightGBM framework with comprehensive evaluation, including class-wise analysis, repeated runs, and statistical testing, providing strong evidence for balanced multiclass IoT intrusion detection.
Edge-Based Early Warning for High-Speed Boat Stability Monitoring Muhammad Asep Subandri; Jamal; Fajar Ratnawati; I Gusti Agung Putu Mahendra; Agus Tedyyana; Budhi Santoso
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.11866

Abstract

Purpose - This study aims to develop and evaluate an edge-based early warning prototype for monitoring the stability of high-speed boats using real-time motion data. Design/methods/approach – The study employs an engineering prototype validation design consisting of system requirement analysis, architecture design, implementation, and validation. The system integrates an IMU (MPU-6050) for motion sensing, a Raspberry Pi-based edge computing unit for real-time processing, rule-based classification (Normal/Warning/Critical), and an MQTT-based communication framework connected to the SHISTAMO dashboard. Prototype validation includes functional testing, platform integration, and operational monitoring using controlled scenarios and expert-labeled events. Findings - The results show that the prototype successfully performs end-to-end integration from sensing to visualization. The system achieved 90.4% classification accuracy, with high recall in detecting critical conditions (96.7%), ensuring reliable identification of high-risk events. The local alarm response time was 182 ms, while the dashboard update delay averaged 1.24 s, indicating near-real-time performance. Communication reliability was also high, with 98.8% data delivery success and 97.2% offline synchronization. Research implications/limitations – The findings demonstrate prototype-level feasibility; however, validation is limited to controlled scenarios and does not yet represent diverse sea conditions. The rule-based thresholds and comfort proxy require further calibration and validation through extended sea trials and reference instrumentation. Originality/value – This study contributes an integrated edge-based maritime monitoring prototype that combines motion sensing, offline-capable alarming, real-time telemetry, and fleet-level logging in a single system, specifically tailored to the operational needs of high-speed boats.
Co-Authors Abda Abda Afis Julianto Apri Siswanto Danuri Danuri Danuri Danuri Danuri Danuri Danuri Danuri Danuri, Danuri Depandi Enda Deski Ari Sandi Effendy, Akmar Elgamar Syam Faizal Riza Fajar Ratnawati Fajar Ratnawati Fajri Profesio Putra Febri Isthifa Adha Ghazali, Osman I Gusti Agung Putu Mahendra I Gusti Agung Putu Mahendra Jamal Jaroji Jaroji Jaroji Jaroji, - Johny Custer Kasmawi Lidya Wati Lumban, Andre Pardamean Muhamad Azren Muhammad Asep Subandri Muhammad Asep Subandri Muhammad Ikhsan Wibowo Muhammad Luthfi Muhammad Noor Muhammad Syafiq Fikri Muhammad Zuhri Febriansyah Nur Ilham. A Nur Ziadah Harun Nurmi Hidayasari Onno W. Purbo Osman Ghazali Osman Ghazali Pretti Ristra Putu Mahendra, I Gusti Agung Rahmat Hidayat Ratnawati, Fajar Rezki Kurniati, Rezki Riduwan Syafitra Rima Anggraini Rima Anggraini, Rima Rosyidah Rosyidah, Rosyidah Santoso, Budhi Sobirin, M Suci Sekar Sari Suharyadi, Thoriq Nadaghoist Sunaryo Sunaryo Supria Supria Tryo Asnafi W. Purbo, Onno Wulan Febri Enjelina Yayan Albi Agustian Yudi Yudi