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All Journal Jurnal AKSI (Akuntansi dan Sistem Informasi) DoubleClick : Journal of Computer and Information Technology JURNAL PENDIDIKAN TAMBUSAI Multitek Indonesia : Jurnal Ilmiah Adimas : Jurnal Pengabdian Kepada Masyarakat DIKEMAS (Jurnal Pengabdian Kepada Masyarakat) Scientific Journal of Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Daya Mas : Media Komunikasi dan Informasi Hasil Pengabdian dan Pemberdayaan Masyarakat Civitas Ministerium: Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Prosiding Seminar Nasional Program Pengabdian Masyarakat Journal of Information Technology and Cyber Security Journal of Moeslim Research Technik
Setia, Lutfiyah dwi
Politeknik Negeri Madiun
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation

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Physics-Informed Graph Neural Network for Industrial Process Monitoring with Limited Sensor Availability Lutfiyah Dwi Setia; Dhesinta Arrova Dewi
International Journal of Engineering, Science and Information Technology Vol 6, No 3 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i3.1920

Abstract

Reliable operation and decision-making in industrial process monitoring increasingly depend on sensor networks. However, monitoring accuracy can deteriorate when sensor availability is limited by equipment failures, communication interruptions, or deployment constraints, particularly in complex industrial environments. This study proposes a Physics-Informed Graph Neural Network (PI-GNN) framework for industrial process monitoring under incomplete sensor conditions. The proposed approach combines graph-based representation learning, which captures relationships among process variables, with physics-informed constraints derived from mass and energy conservation principles to improve prediction reliability and robustness. The framework was evaluated using Supervisory Control and Data Acquisition (SCADA) data collected from a palm oil mill in Aceh Utara, Indonesia. Sensor unavailability was simulated through random masking at missing-data rates of 20%, 40%, and 60%. Performance was benchmarked against Long Short-Term Memory (LSTM), Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Transformer models under identical experimental conditions. The results demonstrate that PI-GNN achieved the strongest prediction performance under moderate sensor loss, with an RMSE of 0.0785 and an MAE of 0.0532. Under severe sensor degradation, represented by a 60% missing-data rate, the proposed framework maintained an RMSE of 0.1297 and outperformed the Transformer baseline. Ablation analysis further demonstrated that the incorporation of physics-informed constraints contributed substantially to model robustness under incomplete sensor conditions. These findings indicate that integrating graph-based learning with domain-specific physical knowledge can provide a reliable approach for industrial process monitoring when sensor availability is constrained. The proposed framework therefore offers a promising foundation for resilient monitoring systems capable of maintaining predictive performance despite progressive sensor degradation and incomplete process observations in industrial environments
Intelligent Human-Centric Cybersecurity Framework For Adaptive Phishing Mitigation In Higher Education Institutions Lutfiyah Dwi Setia; Imam Riadi; Herman Yuliansyah
MULTITEK INDONESIA Vol 20 No 2 (2026): DECEMBER (ON PROGRESS)
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i2.14612

Abstract

Phishing in higher education combines technical exposure with user-level and organizational vulnerabilities, yet the literature often treats these dimensions separately. This study develops an Intelligent Human-Centric Cybersecurity Framework through a structured narrative review and thematic synthesis. Sources were drawn from Scopus, IEEE Xplore, SpringerLink, and ScienceDirect, with the main search window covering 2021–2026. The review screened 268 candidate records and retained 25 sources represented in the final synthesis. A hybrid deductive–inductive coding procedure was used to examine digital literacy, cybersecurity awareness, organizational security culture, behavioral monitoring, and adaptive response while allowing additional themes such as phishing simulation, user compliance, and security fatigue to emerge. The synthesis indicates that human capability and institutional reinforcement become more actionable when they are connected to behavioral evidence and a feedback mechanism. In the proposed framework, “intelligent” refers to the use of monitoring and analytics to support evidence-informed mitigation decisions, whereas “adaptive” refers to the adjustment of training, policy, or controls in response to observed behavior and evolving phishing patterns. The framework is conceptual rather than empirically validated; therefore, controlled phishing simulations and quantitative model testing are proposed as subsequent validation steps.
Co-Authors Ahmad Kudhori Ahmad Kudhori Aji, Mochamat Bayu Amanda, Riski Ardian Prima Atmaja Dhesinta Arrova Dewi Dirvi Eko Juliando Sudirman Fajar, Muhammad Syaeful Gus Nanang Syaifuddiin Hafidhoh, Nisa ul Hafidhoh, Nisa’ul Herman Yuliansyah Imam Riadi James Scott Kirowati, Dewi Kudhori, Ahmad Kumalasari, Rahmania Lely Kumalawati Lestariningsih, Tri Lestariningsih, Tri Mustofa, Muhammad Imron Abu Paksi, Angger Binuko R. Jasa Kusomo Haryo Rahmania Kumalasari Sara Hussain Susilo Veri Yulianto Susilo Veri Yulianto Tri Septianto WULANDARI, WIDYA