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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
Arjuna Subject : -
Articles 308 Documents
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Mitigating Economic Denial of Sustainability (EDoS) Attacks in Cloud Computing Using an AI-Driven Cost-Aware Defense System Saeed, Zubaidi Maytham Sahar; Zainal, Anazida Binti; Ghaleb, Fuad A.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15187

Abstract

The pay-per-use billing model of cloud computing makes cloud infrastructures highly vulnerable to Economic Denial of Sustainability (EDoS) attacks, where adversaries exploit auto-scaling mechanisms to trigger excessive resource consumption and inflated operational costs. Existing mitigation approaches, such as rate limiting and conventional anomaly detection, struggle to accurately distinguish legitimate traffic from attack-traffic requests, often leading to false negative alarm and unnecessary financial overhead. This paper proposes a Cost-Aware Adaptive Defense System (CADS), a novel artificial intelligence-driven (AI-driven) defense system that integrates deep learning-based (DL-based) traffic classification, Trust-based resource access control, and Software-Defined Networking-based (SDN-based) traffic filtering to mitigate EDoS attacks while preserving economic sustainability. The Trust-based access control mechanism dynamically assigns trust scores to incoming requests and restricts suspicious entities from triggering auto-scaling, thereby preventing fraudulent resource allocation. The proposed defense system introduces a lightweight computational overhead of approximately 85 ms for detection and 210 ms for mitigation response, ensuring real-time protection with minimal performance impact. Experimental evaluation was conducted in an OpenStack-based simulated cloud environment, modeling multiple EDoS attack strategies, including HTTP flood, ICMP-based, and workload-based attacks. Results demonstrate that CADS achieves a detection performance such as 97.1% for (F1-score), 97.5% for Recall and 96.8 for Precision, indicates significantly reducing missed attacks and false alarm. More importantly, CADS reduces overall cloud billing costs by approximately 25% compared to state-of-the-art EDoS mitigation mechanisms, such as Advanced EDoS Attack Defense Shell (EDoS-ADS) and Multi-head Attention Network (MAN-EDoS). The results highlight the practical effectiveness of CADS in enhancing cloud security resilience while substantially lowering operational expenses for cloud service providers. Although CADS has not been tested in real-world environments, it demonstrates strong performance under simulated conditions. Future work will focus on large-scale real-world deployments and the integration of reinforcement learning techniques to adapt to evolving attack patterns.
Indirect Matrix Converter Based Synchronous Reluctance Motor Drive Systems using Model Predictive Control Laksmi B., Nur Vidia; Mubarok, Muhammad Syahril; Aribowo, Widi; Purwanto, Didik; Isaac, Jacob Raglend; Abdullayev, Vugar Hacimahmud
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15222

Abstract

This paper proposes a speed control strategy of Synchronous Reluctance Motors (SynRM) using an Indirect Matrix Converter (IMC) combined with a finite model predictive speed control (MPSC) and PI current control. This control algoritm is chosen than fully PI in both loops due to improve overall system stability and dynamic response. The IMC architecture offers advantages such as compactness, bidirectional power flow, and the elimination of bulky passive components, making it ideal for efficient motor drive systems. The proposed control method employs predictive algorithm using augmented state variable and cost function minimization technique. In addition, PI controllers here using a pole-assignment method. Both proposed controls aim to guarantee stability and responsiveness for dynamic performances. The MATLAB/Simulink is used here to simulate the system, incorporating practical motor parameters and space vector modulation techniques. Simulation results show that the control algorithm attains satisfactory speed performance, with minimal steady-state error 0.47%, overshoot below 2%, and fast settling time under various load 0.035 seconds and speed profiles. Additionally, the system performs robustly under reversed and sinusoidal speed commands, demonstrating its effectiveness and suitability for real-world industrial applications also need to implement in the experiment for the future works.
Application of AI-IoT Technologies to Develop the Smart LED Display Management and Monitoring System for the Laboratory Mien, Trinh Luong; Duy, Vu Van; Huong, Trinh Thi; Dung, Nguyen Trung
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15263

Abstract

Smart LED display systems are widely used to provide useful information to users, ranging from simple LED screens to complex screens management and monitoring systems involving a large number of diverse devices, capable of integrating modern technologies. This research focuses on developing a smart LED display management and monitoring system for a laboratory using AI-IoT technologies, which combines deep learning, computer vision, edge computing, embedded system, IoT Communication (MQTT), and web-based management. The goal is to provide convenience, efficiency, and flexibility for users and managers, enabling easy remote information updates and real-time display on LED screens, while simultaneously automatically monitoring and accurately counting the number of people entering and leaving the laboratory. The development of the system includes designing an ESP32-based central LED control board, selecting the P2.5 LED modules, the jetson nano, the Logitech C505e camera, suitable for low-cost educational research. Subsequently, the article introduces the image processing algorithm for counting people based on YOLOv7 TensorRT inference and develops the web management interface based on the Next.js platform, combined with data communication via MQTT protocol. This research was then experimentally implemented at the Mitsubishi FA Laboratory at the university of transport and communications (UTC). The experimental results showed that the Web interface features a grid layout divided into three functional groups, allowing for display content configuration, graphical visualization, clear status display. It provides networked link-tags for updating date/time, temperature/humidity, and In/Out people counts in real-time on both the Web and the LED screen via MQTT/ WebSocket protocols. The experimental results also indicated that the proposed algorithm for counting people In/Out the laboratory achieves high accuracy, over 90%, under normal, stable lighting conditions. This confirms that the proposed smart LED display system operates efficiently, stably, and reliably, and suitable for promoting the digital management of laboratories at a low investment cost.
Profiling Digital Competency of Prospective Vocational IT Educators Using Generalized DINA Model: A Cognitive Diagnostic Approach Wiradika, I Nyoman Indhi; Hadi, Samsul; Khairudin, Mohammad; Fajaruddin, Syarief
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15340

Abstract

Indonesia faces a significant shortage of digitally competent vocational teachers, yet existing assessment instruments often rely on aggregate scores that fail to diagnose specific cognitive deficits. This study addresses this gap by developing a diagnostic assessment instrument for vocational IT pre-service teachers. The research contribution is the validation of a domain-specific Cognitive Diagnostic Model (CDM) framework that integrates general digital pedagogy with vocational IT technical expertise, enabling precise attribute mastery profiling. The study employed a cross-sectional survey design with a purposive sample of 270 informatics education students. Data were analyzed using a multi-stage psychometric approach, combining Classical Test Theory (CTT), Confirmatory Factor Analysis (CFA), and the Generalized Deterministic Inputs, Noisy "And" Gate (G-DINA) model to determine attribute mastery. The results demonstrated that the 32-item instrument achieved excellent model fit (RMSEA₂=0.011) and outstanding classification accuracy (93.71%). Systematic profiling revealed that female students consistently outperformed males across all dimensions, while a non-linear developmental trajectory was observed with a significant competency decline in the third semester followed by recovery. In conclusion, the G-DINA-based instrument provides a robust diagnostic tool for identifying specific learning needs, suggesting that teacher preparation programs require targeted interventions during critical transition periods to support continuous competency development.
Design of MMC-STATCOM Controller Using an Adaptive PID Controller Supported by a Grey Model Ali, Mohammed Moanes Ezzaldean; Kadhim, Qusay Shebeeb
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15426

Abstract

The Static Synchronous Compensator (STATCOM) is recognized as one of the most advanced and effective technologies within the Flexible AC Transmission Systems (FACTS) family, due to its rapid dynamic response and high efficiency in regulating reactive power flow. However, conventional two- and three-level STATCOM topologies suffer from limited scalability and high harmonic distortion. This paper addresses these challenges by employing a STATCOM based on Modular Multilevel Converter (MMC). The significant contribution of this work is the introduction of a novel control strategy for MMC-STATCOM systems which is an adaptive PID controller integrated with a Grey Prediction Model. In the proposed scheme, the PID gains are continuously adapted based on predicted future error values obtained from the GM(1,1) grey model, rather than instantaneous measured errors, enabling proactive compensation under dynamic operating conditions. The performance of the proposed controller is evaluated in MATLAB/Simulink environment and by using a 12 MVA, 34.5 kV MMC-STATCOM system with a full-bridge topology consisting of 22 submodules per phase. Under balanced load condition, the results demonstrated that the adaptive grey-PID controller significantly reduced the total harmonic distortion (THD) of the grid current by 43.75% as compared to conventional PI controller. Under a severe unbalanced load condition, the total harmonic distortion of the grid current is reduced by 33.42%. Furthermore, the proposed controller successfully restored balance to the grid voltage and current and maintained a stable DC-link voltage under unbalanced load conditions. Additionally, the suggested controller achieved a fast-settling time of 0.04 s during transient conditions, this conclusively demonstrates its superior robustness and rapid dynamic response. Despite the additional computational effort introduced by the grey predictor model, it remains suitable for real time implementation due to its low order structure and limited data window.
Genetic Algorithm Tuned Controllers for High-Performance Indirect Field-Oriented Control in DFIG-Based WECS Heroual, Samira; Belabbas, Belkacem; Ayati, Kheloud; Haloui, Rabia; Hassan, Ahmed Tawfik; Ma’arif, Alfian; Mahmoud, Mohamed Metwally; Blazek, Vojtech
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15529

Abstract

Due to rising environmental awareness, rising fuel prices, and increasing power consumption, wind power is currently the world's fastest-growing electricity source. One essential form of renewable energy generation is wind energy conversion using a Doubly Fed Induction Generator (DFIG). Moreover, DFIGs are the best option, as wind turbines with variable speeds often have substantial megawatt capacity. Their cost-effectiveness, high operational efficiency, adaptable control mechanisms, and capacity to autonomously regulate the exchange of active and reactive power are the reasons for this selection. Classical control, which is based on PI regulators and employs several loops, is the most popular control approach that makes use of the indirect field-oriented vector method. In order to ensure stability across the whole speed range, it also requires strict regulation and is highly dependent on the correctness of the machine parameters. This paper presents a comparison between the classical PI and the metaheuristic Genetic Algorithm (GA), aiming to enhance the power extraction of DFIG under varying wind conditions. The simulation was carried out using MATLAB-SIMULINK, enabling the exploration of its performance across a range of operational scenarios. The results indicate that the PI controller optimized by GA demonstrates significant improvements over traditional controllers, particularly noted for its simplicity, faster convergence, and greater efficiency in power management.
Survey and Challenges: Event Extraction of Story Narrative in NLP Approach Daniati, Erna; Wibawa, Aji Prasetya; Irianto, Wahyu Sakti Gunawan; Nafalski, Andrew
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.15534

Abstract

Event extraction from story narratives remains a challenging yet underexplored area in natural language processing due to narrative complexity including implicit causality long-range dependencies and temporal ambiguity. This study addresses the research question: How have NLP and deep learning approaches been applied to extract events from story narratives and what gaps persist. Following the PRISMA 2020 guidelines we systematically reviewed 12 peer-reviewed studies published between 2017 and 2024. Our analysis reveals growing adoption of transformer-based models such as BERT alongside emerging architectures like DEEIA and PAIE which leverage prompt-based learning and event-specific contextual aggregation. Commonly used datasets include ROCStories and custom narrative corpora though few are standardized. Key challenges involve handling implicit events limited annotated data cross-domain generalization and integration of commonsense reasoning. The main contribution of this review is the first structured synthesis of event extraction techniques specifically for story narratives using a rigorous systematic methodology. We highlight the need for document-level modeling narrative-aware evaluation metrics and low-resource adaptation strategies. This work provides a foundation for future research aiming to bridge narrative understanding with robust event-centric NLP systems.
Language as the Semantic Bridge in Audio, Music, and Multimodal Artificial Intelligence: A Systematic Review (2021-2025) Ratnasari, Novia; Wibawa, Aji Prasetya
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 2 (2026): April
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i2.15564

Abstract

This study presents a systematic review of research in Audio, Music, and Multimodal Artificial Intelligence published between 2021 and 2025, investigating how language operates as a semantic mediation layer between acoustic signals and high-level meaning. The research addresses the fragmentation of existing surveys by introducing a Domain; Modality; Technique; Task (D-M-T-T) taxonomy that systematically differentiates domain focus, modality configuration, modeling techniques, and task objectives. The research contribution is a structured analytical framework that offers a more granular perspective than architecture-centered surveys of Multimodal Large Language Models. Following the PRISMA 2020 protocol, 2,197 Scopus-indexed publications were screened, yielding 369 eligible studies. Language is defined as a representational layer encompassing natural language and structured symbolic encodings that connect acoustic embeddings to semantic interpretation and generative reasoning. Multimodal systems aligning audio and vision without explicit textual grounding are included and analyzed as non-linguistic alignment architectures within the taxonomy. The findings reveal a shift from recognition-based models toward unified multimodal systems in which language conditions alignment, reasoning, and generative synthesis. For instance, text-conditioned music generation demonstrates how linguistic prompts guide compositional structure and emotional expression. These developments reflect an epistemic transition from signal recognition paradigms to language-mediated generative intelligence. Emerging gaps include limited explainability in generative audio systems and insufficient low-resource cross-modal semantic grounding.

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