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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.
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Articles 10 Documents
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Search results for , issue "Vol. 8 No. 1 (2026): February" : 10 Documents clear
Modified Starch-Based Materials for Sustainable Food Packaging Kusuma, Isnainul; Rahmadhia, Safinta Nurindra; Ma'arif, Alfian; Aktawan, Agus; Juwitaningtyas, Titisari; Ramli, Nor Hanuni; Olunusi, Samuel Olugbenga
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.v8i1.15092

Abstract

Food packaging is a significant contributor to plastic waste, prompting a search for sustainable alternatives. Among these alternatives, modified starch-based materials have emerged as promising solutions due to their biodegradability, renewability, and abundance. However, the hydrophilic nature, poor mechanical properties, and limited thermal stability of native starch pose challenges for its use in food packaging. This review explores various modification techniques—chemical, physical, and enzymatic—that enhance the performance of starch-based materials for food packaging. The methods discussed include acetylation, crosslinking, heat-moisture treatments, and enzymatic hydrolysis, each improving the material's strength, flexibility, and barrier properties. Results demonstrate that starch modifications significantly improve the mechanical, thermal, and water vapor barrier properties of packaging films. Notably, the combination of modified starch with other biopolymers such as chitosan or gelatin further enhances these properties, making them suitable for active packaging applications. The incorporation of antimicrobial agents and nanofillers into starch-based films has expanded their functionality, enabling food shelf-life extension and quality monitoring. Despite these advancements, challenges remain in balancing the biodegradability and durability of starch-based films. Future research should focus on optimizing modification processes, enhancing scalability, and addressing regulatory concerns to ensure the commercial viability of modified starch as an eco-friendly packaging material.
A Novel Approach to Energy Efficient Wireless Communication in Internet of Things Networks Alfaisaly , Noor Nateq; Saeed, Elaf A.; Younis, Saad B.; Naeem, Suhad Qasim
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.v8i1.13868

Abstract

One of the key issues of Internet of Things (IoT)-based wireless sensor networks (WSNs) is energy efficiency because battery-powered nodes have to work within a set of severe resource limitations. Conventional protocols do not always work well in nonhomogeneous dynamic environments and this results in poor performance and longevity. The design and validation of an unified framework that intelligently operates network clustering, routing, and resource allocation with the use of machine learning are the research contributions. The framework is represented through a dynamic clustering scheme based on neural networks, routing scheme based on reinforcement learning (Q-learning) and a scheme of Lagrangian optimization-based resource allocation. MATLAB and NS-3 simulations were run with different sizes of networks (100-500 nodes) and traffic. The flow of methodology has formed a scheme whereby the adaptive decision-making was to be made at several levels of the communication stack. The average power savings, increment in network lifetime, and improvement in the percentage packet delivery ratio of the proposed model was 31, 17.9 and 6.2, respectively, over the classical schemes like LEACH and TEEN. Findings were also uniform at various levels of deployments and statistical validation was made to prove it is significant (p < 0.01). The model exhibits better adaptability and performance aspects in both the case of a static network and dynamic network as compared to the recent machine learning-based approaches. To sum up, the paper provides a scaled, smart communication system of IoT networks. Its applications in a real world can be found in smart farming, industrial IoT, and healthcare. The next steps involve the prototype development and integration of the blockchain based node authentication.
Design and Development of ALU using Multi Chiplet Methodology for High-Performance Computing Rai, Amrita; Shah, Owais Ahmad; Khan, Imran Ahmed; Khan, Mubeen Ahmad; Jindal, Latika; Chouhan, Piyush
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.v8i1.14107

Abstract

The fundamental programmable logic unit (PLU) in any microprocessors or a microcontrollers and real-time processor of integrated circuits is the arithmetic and logical unit (ALU). The conventional ALUs had exorbitant power consumptions, route delays, and transistor counts because they were created using complementary metal oxide semiconductor (CMOS) technology. Therefore, the motivation of this paper is on the design and development of ALU using Multi Chiplet design Methodology with FPGA kit and simulation is perform on vivado software. Multi-Chiplet systems helps reduce the cost of chip design, low power consumption and increases yield for complicated SoCs (System on Chips). Low power with less design space semiconductors will be the future of computing as the power requirements and size of the SoC cannot be expanded above the set limit. There is a need to reconsider how the design ALU to shorten the time needed for their development as designer continue to push the current limit boundaries of the present CMOS process. This paper proposed a Multi Chiplet SoC structure of ALU with low power, less area required and in small packaging for mostly used in CPU of all type computing devices. The basic function of ALU is to perform arithmetic and Logic operations, required multiplication and additions. In this paper booth multiplier and Kogee-Stone Adder are proposed with multi-chip module (MCM) for low power consumption, less area requirement, high processing speed and less delay. Due to the ever-growing requirements of increasing the Floating-Point Operations per Second (FLOPs) of the processing unit in the field of high-performance computing and AI, there needs to be changes in both the overall design and also the design methodology in fabricating an ALU.
Implementation of an Automatic Controlled Power Factor Correction System Utilizing Low-Cost Modules Sneineh , Anees Abu; Salah, Wael A.; Ma'arif, Alfian
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.v8i1.14215

Abstract

This paper presents the design and implementation of a PIC microcontroller-based power factor correction system using a stepped capacitor bank and low-cost analog measurement modules. The proposed design aimed to address the low power factor issue caused by inductive loads that intern increases the current, losses, and apparent power demand. The developed PIC-based controller integrated analog conditioning circuits for voltage, current, and phase-angle measurement. The proposed system acquires analog signals from a voltage transformer, a current transformer–op-amp module, and an AD8302-based phase detector, computes real, reactive, and apparent power in real time, and automatically connects or disconnects capacitor-bank steps to maintain the power factor within a predefined band (0.92–0.98). Experimental results on a 4 kW inductive load array indicated that the measurement error of the analog voltage module was approximately 1.32%, while the analog current module exhibited an error of around 3.02% in comparison to digital measuring instruments. Additionally, there was an improvement in the power factor from 0.865 to 0.935, with by a reduction in load current of approximately 7% and a decrease in load reactive power of about 35%. The proposed design confirms satisfactory operation for automatic capacitor-bank control in power factor correction applications.
Optimization of DC Fast Charging in CHAdeMO Systems Using Thunderstorm Algorithm with Thermal and Health Constraints Samsurizal, Samsurizal; Afandi, Arif Nur; Faiz, Mohamad Rodhi
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.v8i1.14505

Abstract

The significant increase in the use of electric vehicles (EVs) demands the development of fast charging systems that are not only efficient but also maintain battery integrity. One of the primary challenges in direct current (DC) charging is balancing speed with minimizing degradation caused by thermal stress. This study proposes a charging optimization model based on the Thunderstorm Optimization Algorithm (TA) for CHAdeMO-based DC systems. A lithium-ion equivalent circuit battery model was used to simulate electrochemical and thermal dynamics. The model introduces an adaptive charging current profile designed with a dynamic boundary configuration, defined here as the iterative adjustment of current limits according to real-time thermal and health constraints. Compared to conventional constant current–constant voltage (CC–CV) methods, TA considers maximum temperature, State of Health (SoH), and target State of Charge (SoC) simultaneously. The simulation (180 minutes, passive cooling, Python-based) showed that TA reduced SoH degradation to 1.3% and battery life usage to 18.4%—the latter defined as cumulative stress energy normalized to initial capacity—compared to 2.9% and 22.5% for CC–CV. Additionally, TA achieved a higher average charging power (26.1 kW vs. 24.8 kW) without exceeding 50 °C. Although the algorithm requires more computational effort than CC–CV, its moderate complexity suggests feasibility for real-time integration in battery management systems. These findings highlight TA as a promising adaptive and sustainability-oriented charging strategy.
Investigation of Manufacture Tolerances on Torque Pulsation Profile of Interior Permanent Magnet Motor with Third Harmonic Injected Sinusoidal Rotor Iron Pole Shuraiji, Ahlam Luaibi; Hameed, Kassim Rasheed; Shneen, Salam Waley
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.v8i1.14647

Abstract

Torque ripple is a significant undesirable aspect of permanent magnet (PM) machine. It is mainly contributed by cogging torque, which is inherit feature of the PM machine. Interior permanent magnet (IPM) motor with a sinusoidal + third-order harmonic injected rotor pole shape has been introduced as one of the most efficient rotor pole arc iron shape techniques to minimize the cogging torque. Such method showed a reduction in the cogging torque compared to the traditional designs. Generally, imperfections in the manufacturing process can exacerbate cogging torque and, by extension, torque ripple. This research assesses how manufacturing tolerances influence the torque ripple of the IPM motor having sinusoidal + third order harmonic rotor pole shape. The investigation has been carried out using two-dimension finite element analysis(2D-FEA) method, ANSOFT MAXWELL program. Different models of the IPMs with sinusoidal + third order harmonic rotor pole shape have been made to simulate healthy, eccentricity and PM diversity cases. According to the simulation results, it has been found that PM diversity leads to introduce additional harmonics in the cogging torque waveforms, i.e., in addition to the fundamental harmonic, which is the 60th harmonic orders, the 12th harmonic and its multiples harmonic orders were presented, consequently resulting in increasing the torque ripple. Moreover, the obtained results have shown that the static eccentricity has more negative effect on the torque ripple compared to the dynamic counterpart, i.e. the torque ripple of the static eccentricity is about 20% higher than that of the dynamic counterpart.
A Systematic Review of Machine Learning and Deep Learning Approaches in MRI-Based Brain Tumour Analysis, Detection and Classification Omran, Hanan M.; Ibrahim, Khalil; Abdel-Jaber, Gamal T.; Sharkawy, Abdel-Nasser
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.v8i1.14673

Abstract

A brain tumour develops when abnormal cell growth happens in or near the brain. These tumours can grow slowly and not be cancerous, or they can grow quickly and spread, which is known as malignancy. Brain tumours put pressure on the surrounding brain tissues, causing symptoms like memory loss, migraines, movement dysfunction, and vision impairment. Brain tumours are often divided into two groups: primary tumours, which start in the brain, and secondary tumours, which are caused by cancers that spread to other regions of the body. Although brain tumours provide a significant medical challenge, patient outcomes have improved thanks to recent advancements in diagnostic and treatment methods. Because of its better soft-tissue contrast and noninvasive nature, magnetic resonance imaging (MRI) is one of the most important medical imaging modalities for the early identification and precise localization of brain tumours. Clinical practice also makes use of other imaging methods such as PET-CT and functional MRI (fMRI). Artificial intelligence and deep learning techniques have demonstrated significant promise in automated brain cancer analysis in recent years. These methods enable precise cancer diagnosis, classification, and segmentation by identifying intricate patterns from MRI data that are challenging to recognize through manual examination. A thorough study of current deep learning and machine learning techniques for MRI-based brain tumour analysis is provided in this paper. The current thorough literature search includes papers released between 2019 and 2024. 67 pertinent articles are chosen for in-depth analysis after predetermined inclusion and exclusion criteria is used. Many of these studies make use of publicly accessible datasets like Figshare, TCIA, and BraTS. The results show that deep learning models frequently outperform traditional machine learning methods in terms of accuracy and robustness, especially convolutional neural network-based designs. However, there are still issues with clinical generalisation, model interpretability, and data heterogeneity.
Advances in Brain-Computer Interfaces for Taste Perception: Current Insights and Future Directions Pamungkas, Yuri; Karim, Abdul; Yulan, Gao; Uda, Muhammad Nur Afnan; Hashim, Uda
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.v8i1.14718

Abstract

Human taste perception is a complex multisensory process that integrates chemical, emotional, and cognitive responses within the brain. Traditional methods for evaluating taste rely on subjective reporting, which limits reproducibility and accuracy. Brain-Computer Interface (BCI) technology provides an objective solution by decoding neural activity associated with taste perception using non-invasive techniques such as EEG and fNIRS. The research contribution aims to deliver an extensive overview of the latest advancements in BCI-oriented taste research, emphasizing various applications, methodological frameworks, and potential future pathways that connect the domains of neuroscience and sensory technology. This review examines the use of EEG and fNIRS modalities for signal acquisition, preprocessing, feature extraction, and classification across 36 studies conducted between 2020 and 2025. These works employ both traditional algorithms and deep learning models, including SVM, CNNs, and Transformer-based frameworks, to decode neural signatures of basic tastes and multisensory interactions. Results show that BCIs have successfully identified distinct brain responses for sweet, sour, salty, bitter, and umami stimuli. They have also been applied in multisensory integration, hedonic evaluation, consumer behavior analysis, clinical diagnosis of taste disorders, and affective monitoring. However, challenges remain in signal noise, dataset standardization, and model interpretability. In conclusion, BCIs represent a promising and interdisciplinary approach for objectively studying and enhancing human taste perception through the integration of neuroscience, engineering, and artificial intelligence.
Legal and Public Health Governance for Sustainable Integration of Mobile Health (mHealth) Technologies in East Africa Aidonojie, Paul Atagamen; Mugabe, George Mulingi; Aidonojie, Esther Chetachukwu; Jufri, Muwaffig; Mustafa , Mundu M.; Ekpenisi, Collins; Eregbuonye, Obieshi; Antai, Godswill Owoche; Okpoko, Mercy; Kelechi, Uzoho; Alammari, Khalid Saleh Y
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.v8i1.14943

Abstract

Mobile health (mHealth), which comprises mobile health applications, telemedicine, SMS-based treatments, and wearable health monitors, has the power to change healthcare delivery, but at the same-time, it is going through a rapid developmental phase that regulators cannot keep up with. This is considered a necessity in balancing the Integration of mHealth technology innovation through enhanced laws within East Africa. It is in view of this that this examines the legal and public health framework in integrating mHealth technology in enhancing the healthcare system within East Africa. The study adopts a doctrinal and systematic analytical method of study directed by the PRISMA framework, allowing thorough legal analysis while at the same time guaranteeing a transparent, stringent, and comprehensive review of related literature. The study found that fragmentation of laws, lack of centralized public health and data governance, unequal access to mHealth services, and constraints on innovation, weakens the integration and regulation of mHealth. Hence, the study recommends and concludes that for effective integration of mHealth in enhancing the public health care system, the research insists on a unified legal system that states unambiguously which data protection benchmarks apply, what the liability conditions are, what the integration of different systems and regulations requirements is, and how to coordinate among different countries' regulators. Besides that, it suggests measures for strengthening the capacity of the targeted groups, such as: medical professionals, trainees, users’ digital literacy campaigns, and local mHealth technology developers’ institutions’ support.
Grid-Calibrated Patch Learning for Braille Multi-Character Recognition Widyadara, Made Ayu Dusea; Handayani, Anik Nur; Herwanto, Heru Wahyu; Yu, Tony; Mulya, Marga Asta Jaya
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.v8i1.15199

Abstract

The approach presents a multi braille character (MBC) recognition system for Indonesian syllablesdesigned to address real-world imaging variations. The proposed framework formulates 105-class visual classification task, where each class represents a two-character Braille unit. This design aims to preserve inter-character spatial relationships and reduce error propagation commonly found in single-character segmentation approaches. A carefully constructed dataset undergoes spatial pre-processing stages, including rotation normalization, grid assignment, and multicell cropping, resulting in uniform 89×89 pixel image patches that ensure geometric consistency across samples. To enhance model generalization under varying illumination conditions, single-dimension photometric augmentation is applied exclusively during training, including brightness (±25%), exposure (±20%), saturation (±40%), and hue (±30%). ResNet-101 is adopted as the backbone architecture based on prior comparative studies conducted on the same dataset, demonstrating its effectiveness in capturing fine-grained Braille dot shadow patterns. The network is trained for 300 epochs with a batch size of 32 under consistent experimental settings, and performance is evaluated using a confusion-matrix-based framework with overall accuracy as the primary metric. Experimental results indicate that moderate photometric reductions significantly improve recognition performance by preserving critical micro-contrast cues. In particular, an exposure reduction of −20% achieves the best balance between accuracy (86.13%) and training efficiency (14.12 minutes), outperforming the non-augmented baseline (74.37%, 22.10 minutes). A hue reduction of −30% further improves robustness to ambient color variations, while aggressive positive adjustments degrade performance due to structural distortion. These findings confirm the effectiveness of the proposed MBC framework for practical Braille recognition in real-world environments.

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