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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 295 Documents
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A Comprehensive Review of Optimization Techniques in Industrial Applications: Trends, Classifications, and Future Directions Asih, Hayati Mukti; Mohamad, Effendi; Irianto, Irianto; Ma’arif, Alfian
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13261

Abstract

In recent years, optimization techniques have played a central role in enhancing operational efficiency and decision-making across diverse industrial sectors, including manufacturing, logistics, and transportation, energy, healthcare, and agriculture. These sectors face complex, large-scale, and often nonlinear challenges that demand both precision and adaptability. The research contribution of this review is to provide a structured classification of optimization methods—namely exact algorithms, heuristics, metaheuristics, and AI-integrated hybrid models—and to critically evaluate their practical applications, limitations, and emerging trends across industries. This study adopts a review approach to identify and compare those techniques in solving various optimization problems. Through a detailed analysis of over 30 recent publications for last four years, the review highlights how these techniques are being applied in real-world industrial environments, including cold chain logistics, smart energy systems, precision agriculture, and healthcare scheduling. The results indicate a growing reliance on hybrid and AI-enhanced models due to their superior scalability, adaptability, and potential alignment with Industry 4.0 and Sustainable Development Goals (SDGs). However, challenges remain in areas such as computational efficiency, model interpretability, and real-time data integration. In conclusion, this study provides valuable insights for both researchers and practitioners seeking to apply optimization techniques more effectively in industrial systems, while also identifying critical research gaps for future exploration by addressing the growing complexity and sustainability demands of modern industry.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Understanding Time Series Forecasting: A Fundamental Study Furizal, Furizal; Ma’arif, Alfian; Kariyamin, Kariyamin; Firdaus, Asno Azzawagama; Wijaya, Setiawan Ardi; Nakib, Arman Mohammad; Ningrum, Ariska Fitriyana
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13318

Abstract

Time series forecasting plays a vital role in economics, finance, engineering, etc., due to its predictive power based on past data. Knowing the basic principles of time series forecasting enables wiser decisions and future optimization. Despite its importance, some researchers and professionals find it difficult to use time series forecasting techniques effectively, especially with complex data settings and selection of methods for a particular problem. This study attempts to explain the subject of time series forecasting in a comprehensive and simple manner by integrating the main stages, components, preprocessing steps, popular forecasting models, and validation methods to make it easier for beginners in the field of study to understand. It explains the important components of time series data such as trend, seasonality, cyclical components, and irregular components, as well as the importance of data preprocessing steps, proper model selection, and validation to achieve better forecasting accuracy. This study offers useful material for both new and experienced researchers by providing guidance on time series forecasting techniques and approaches that will help in enhancing the value of decision making.
Modified Orca Algorithm Based on the Navigation Behavior for Optimal Unit Commitment in Power Systems Widayanti, Lilis; Afandi, Arif Nur; Herwanto, Heru Wahyu
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13645

Abstract

This study presents the Novel Navigation Orca Algorithm (NNOA), an innovative optimization algorithm derived from Orca Algorithm (OA). NNOA addresses the unit commitment (UC), a complex issue in power systems that focuses on scheduling generator units to meet power demand while taking into account each generator's limitations, with the goal of lowering operating costs and gas emissions. NNOA exhibits orca hunting behavior through echolocation, utilizing the Doppler effect principle to promote adaptive movement and circumvent local optima, as in contrast to OA's wave-based exploration. The algorithm was evaluated utilizing IEEE 30-bus system data, focused on the Integrated Economic and Emission Dispatch (IEED) objective. The performance was evaluated against OA and Particle Swarm Optimization (PSO) through convergence analysis over 10 and 30 trials, each consisting of 100 iterations. NNOA decreased the IEED value by 1.33% in regard to OA and 1.51% in regard to PSO. NNOA achieved convergence in 10 iterations, whereas OA required 35, indicating 71.4% faster convergence rate. Wilcoxon rank-sum tests demonstrated significant differences between NNOA, OA, and PSO pairings. NNOA's per-iteration computation time exceeds the time needed by PSO, but it remains economical and profitable. Significantly, NNOA contributes minimizing the fuel consumption and emissions cost, which has a positive environmental impact. It effectively adheres to the required constraints, which include the hourly power demand and generator output limits. Future research is encouraged to apply NNOA to larger-scale power systems and explore its hybridization with PSO to enhance computational efficiency, result consistency, and robustness in practical grid operations.
A Blockchain-Enabled Internet of Things Framework for Enhancing Trust and Privacy in Social Work Case Management Chen, Yih-Chang; Lin, Chia-Ching
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13653

Abstract

Traditional social work case management systems face critical challenges including data silos, security vulnerabilities, and insufficient inter-agency collaboration, limiting service efficiency and compromising client privacy protection. This study addresses these challenges by developing and evaluating a novel technological framework that integrates blockchain consortium networks with Internet of Things (IoT) devices to establish multi-party trust mechanisms and enhance service delivery. The research contribution is a comprehensive four-layer system architecture featuring 28 smart contracts, decentralized trust mechanisms, and privacy-preserving technologies including homomorphic encryption and differential privacy for social work applications. The methodology employed a mixed-methods approach involving system design and development, followed by a six-month pilot implementation across three social work institutions in Taiwan with 249 participants. Data collection encompassed quantitative performance metrics from system logs and IoT sensors, alongside qualitative feedback through interviews and focus groups. The blockchain network achieved 850 transactions per second with 99.2% system availability, significantly outperforming industry standards. Results demonstrated substantial operational improvements: 37.1% reduction in case processing time, 87.3% increase in service efficiency, and 26-fold increase in inter-agency collaboration frequency. The blockchain-based trust mechanism increased inter-agency data sharing willingness from 61.3% to 84.6%, while maintaining 100% anonymization coverage with 91.3% analytical accuracy. Cost-benefit analysis revealed a 2.8-year payback period with 41.2% return on investment. This research demonstrates the feasibility and effectiveness of blockchain-IoT integration in social work, providing a practical framework for digital transformation while ensuring data security and privacy protection in sensitive social service environments.
Solar-Powered EV Charging Using Modified SEPIC-Luo Converter with Recurrent Neural Network Technique Sreedevi, S. L.; Geetha, B. T.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13675

Abstract

In order to address issues with renewable energy utilization processes and the growing power consumption of Electric Vehicles (EVs) in the near future,a solar powered charging station for EV is developed. Initially, a highly efficient Modified Single Ended Primary Inductor Converter (SEPIC) Luo converter is used to increase a low voltage of the PV system. The maximum power of the Photovoltaic (PV) system is then tracked using the Recurrent Neural Network based Maximum Power Point Tracking (RNN-MPPT), whose parameters are adjusted using the Monarch Butterfly Optimization (MBO) algorithm. Then, the high frequency full bridge inverter effectively transforms the power and isolation transformer is utilized for decreasing electrical noise and interference. Furthermore, the interleaved synchronous rectifier is used for attaining effective charging by reducing conduction losses. The developed work is applied in Matlab/Simulink software, reveals that the developed work attains the converter efficiency of 97.44%when compared to 90% of Luo and 95.16% of Enhanced SEPIC, ensuring the stable and reliable power delivery. Also, The MBO-RNN approach exhibits 98.8% of tracking efficiency and a root mean square error of 0.0125.
A New Adaptive Flux-Oriented Control Framework for Induction Motors with Online Neural Network Training Bekhiti, Belkacem; Al-Sabur, Raheem; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13727

Abstract

Unlike conventional field oriented control methods, this paper presents a mathematically novel control strategy for induction motor drives, formulated using a two-loop nonlinear dynamic inversion (NDI) framework inspired by aeronautical control architectures. Sensorless operation is realized with a conventional rotor flux observer, while several additional enhancements are introduced to raise overall performance. In particular, a real time radial basis function (RBF) neural network is systematically embedded in a model reference adaptive system (MRAS), replacing the traditional PI adaptation loop with an online training mechanism that improves speed estimation accuracy under parameter variations and load disturbances. The single layer RBF network is trained by gradient descent and incorporated into the nonlinear observer without compromising closed loop stability. The complete controller was implemented on a 1.1 kW, 1430 rpm induction motor using a dSPACE DS1104 real time platform. Experimental results show clear superiority over classical FOC as well as DTSFC and DTRFC schemes, achieving the lowest measured flux ripple (0.002 Wb), minimal torque ripple (0.043 N·m), and the fastest torque response time (0.65 ms). The steady state speed error was reduced by 91 % (from 0.65 to 0.08 rad/s), settling times remained below 60 ms, and both RMSE and ISE metrics decreased appreciably across all tested conditions. Although the proposed design incurs moderate computational overhead, it is fully compatible with real time execution. Future work will examine scalability to high power drives, improved resilience to temperature induced parameter drift, and adaptation of the NDI based framework to permanent magnet machines.
Sensor Fusion of Laser and Inertial Units with Kalman-KMeans-Fuzzy Framework for Real-Time Railway Geometry Monitoring Fikri, Ahmad Atif; Subhan, Muhammad Ferindin Nuha; Suryanto, Heru; Muhdi, Krisna Dwipa; Pratama, Daniel Febrian; Iqbal, Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13780

Abstract

Maintaining railway track geometry integrity is essential to ensuring transportation safety and predictive maintenance. Conventional manual inspection methods are limited by low sampling frequency, subjective interpretation, and delayed anomaly detection. This study introduces a real-time, embedded monitoring system using VL53L0X infrared laser sensors and an MPU6050 IMU to measure gauge, cross-level height, and inclination. Sensors are mounted on a lightweight aluminum trolley and sampled every 0.5 seconds using an Arduino-based platform. A Kalman Filter reduces measurement noise, with tuned covariance matrices based on field calibration. Filtered outputs are clustered via K-Means (K = 2), validated by the Elbow Method and Silhouette Score (>0.6). Maintenance categories are assigned through a fuzzy logic system, with a ±1 mm sensitivity analysis confirming >85% decision stability. Field results demonstrate a measurement noise, achieving RMSE and MAE values of 0.8165 mm and 0.3175 mm for gauge and height, and 0.3086° and 0.0952° for inclination, respectively and a SNR gain from 0.5 dB to 21.7 dB. The low-cost, modular setup supports scalable, condition-based maintenance and demonstrates robustness in noisy environments. This approach offers a practical foundation for future integration with predictive analytics and digital twin technologies in smart rail infrastructure.
A Lightweight 1D CNN for Unified Real-Time Communication Signal Classification and Denoising in Low-SNR Edge Environments Alkhafaji, Mohammed Jasim A.; Mohammed, Rusul Nadhim; Mohammed, Hanan Chassab; Abed, Mohammed Albaker Najm
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13789

Abstract

The escalating complexity and pervasive noise in contemporary communication systems have increasingly rendered traditional signal processing methods insufficient for reliable real-time analysis. This research addresses a fundamental void in existing literature by proposing a novel and lightweight deep learning framework, primarily centered on Convolutional Neural Networks (CNNs) for the joint classification and denoising of communication signals. Distinct from prior methodologies that often segregate these crucial tasks our model integrates both objectives within a highly optimized, unified architecture engineered for ultra-low-latency inference, notably achieving a 30–50% reduction in inference time compared to deeper CNN-RNN hybrids or Transformer-based architectures. The framework's effectiveness was comprehensively evaluated using both synthetic and real-world datasets, including RadioML2018.01A which encompasses a diverse range of modulation schemes and signal-to-noise ratio (SNR) levels. Experimental results conclusively demonstrate that the proposed CNN achieved an impressive 96.8% classification accuracy significantly enhanced signal quality to an average of 22.3 dB SNR, and maintained an average processing latency of merely 11.3 ms. These figures consistently demonstrate superior performance compared to traditional baselines including FFT, SVM, and LSTM. Despite these promising results, the current model was primarily trained and evaluated under Additive White Gaussian Noise (AWGN) conditions, and future work will explore its generalization to real-world scenarios involving multipath fading, Doppler shifts, and dynamic channel interference. This study represents a significant leap forward in developing robust, efficient, and intelligent solutions essential for next-generation communication signal processing, particularly for real-time applications in resource-constrained environments.
Temperature-Controlled Process for Recycled Waste Tire Polymer-Polymer Composites: An Innovative and Sustainable Solution for Marine Fender Applications Zaibel, Ali Habel; Almtori, Safaa A. S.; Al-Sabur, Raheem; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13813

Abstract

Marine fender prototypes play a critical role in protecting the ship and the berthing infrastructure from damage during docking. Recycling waste polymers, such as waste tires, into composite materials for marine fenders, can contribute to environmental sustainability and resource conservation. In marine Fender applications, compression testing often plays a crucial role; we should also test factors such as elasticity, stiffness, and hardness. In this study, pressure and hardness were selected, and Young's modulus was calculated for two types of composite materials: one manufactured from waste tires and high-density polyethylene (HDPE) and the other from waste tires and room-temperature vulcanized (RTV) silicone both in varying proportions. These types of materials were produced using a press machine equipped with a PID controller, which enables the adjustment of the temperature to a desired value, thereby achieving the best results. Prototypes containing 85% waste tire with 15% HDPE and 50% waste tire with 50% RTV silicone showed superior energy absorption and durability for marine fender applications. Despite achieving satisfactory hardness and hardness values, the waste tire and RTV silicone composite did not exceed those of the waste tire and HDPE composites, which had Young's modulus and Shore hardness values of 1.74 MPa and 56.6, respectively. The compression test showed that the waste tire and RTV silicone composites achieved higher values, surpassing 1990 kN. The findings provide a crucial foundation for utilizing waste composite materials in marine fender production.

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