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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 326 Documents
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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.
Implementation of East Javanese Local Culture in Graphic Design Elements in Students’ Final Projects: A Literature Review Patria, Asidigisianti Surya; Hafidz, Abdul; Nurwicaksono, Bayu Dwi; Wardani, Ayusta Lukita; Laksmi, Arieviana Ayu; Kurniawan, Yudiyanto Tri; Hidayanti, Henny; Machfud, Mochammad Abdul; Fatahillah, Rizal Sofyana; Kasdan, Junaini Binti; Tangkin, Hong; Apriyansyah, Bahalwan; Pratama, Hanny Chandra; Nugroho, David
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14932

Abstract

The background of this research departs from the challenges of globalization that cause visual homogenization and erode local cultural identity. Graphic design is a strategic medium in bridging tradition with modernity, by adapting cultural elements such as batik motifs, Javanese script typography, to cultural icons Reog and Karapan Sapi. This study aims to analyze the integration of East Java local wisdom values in contemporary graphic design through a literature study approach and descriptive qualitative analysis. The research method was carried out by reviewing 215 articles selected using the PRISMA protocol until there were 15 relevant main sources. The results of the study show that there are four main trends in graphic design based on local wisdom, namely the symbolization of performance culture and language (30%), culinary branding and local products (29%), the revitalization of cultural narratives on digital platforms (22%), and the abstraction of traditional crafts into visual assets (18%). The value of the interconnectedness between keywords shows that graphic design is now strongly integrated with interactive technology, education, and the creative economy. In conclusion, the application of East Java's local wisdom in graphic design not only strengthens the region's visual identity but also opens up opportunities for sustainable creative economy innovation.
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.
Artificial Intelligence and IoT for Riverine Oil Spill Detection: A Focused Review and Proposed Adaptive Edge Framework AL-Khafahji, Shireen M.; Abdullah, Hikmat N.
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.v8i1.14677

Abstract

Riverine oil spills are more challenging to detect than marine spills due to shallow depths, high turbidity, and rapidly changing hydrodynamics, which degrade the performance of satellite- and SAR-based detection methods. This review examines how artificial intelligence and the Internet of Things can deliver accurate, low-latency detection in freshwater and defines an AI-IoT system as distributed river-edge nodes with RGB/NIR cameras, thermal infrared sensors, UV/fluorometric probes, and turbidity/multispectral units running lightweight deep models on low-power hardware and networked via LPWAN or NB-IoT with optional federated coordination. The novel contribution is a hydrodynamics-aware, adaptive framework that embeds river flow and turbidity, couples explicit constraints on edge compute, energy, and inference latency, and derives multi-sensor fusion logic from comparative synthesis. Performance is organized along accuracy, decision latency, deployment cost, and environmental adaptability. Using a structured narrative review with scoping elements, the research screened 145 records from major databases. It synthesized 47 peer-reviewed studies (2020-2025), harmonized definitions, and applied descriptive synthesis to manage heterogeneous metrics and protocols. Results show that SAR and hyperspectral methods that excel in marine or controlled settings often degrade in narrow, turbid rivers because of clutter and revisit latency. In contrast, hybrid AI-IoT architectures employing compact CNN/Transformer variants at the edge report high accuracy with millisecond-scale inference and moderate power budgets. Limitations include heterogeneous reporting, non-standard datasets, and limited multi-site validation. The framework and synthesis motivate open benchmarks and coordinated river trials to standardize evaluation and accelerate translation.
Enhancing JSEG Color Texture Segmentation Using Quaternion Algebra Method Sharma, Vijay Kumar; Shah, Owais Ahmad; Ali, Kunwar Babar; C., Ravi Kumar H.; Chourasia, Ankita; Ahmed, Mohammed Mujammil
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.v8i1.14743

Abstract

This work uses quaternion algebra to implement a unique color quantization method on the JSEG color texture segmentation. Typically, RGB color orientations in the composite hyper-planes are inverted to produce the key vectors of the color-space. Because quaternion algebra offers a highly logical way to work with homogeneous coordinates, color is represented as a quaternion in the proposed system. In this illustration, the color pixels are seen like in the 3D space such as point. The recommended model has resulted in a unique quantization method that uses level set techniques and projective geometry. This approach will be used in the JSEG color texture segmentation. This current color quantization technique is splintering clustering mechanism since it makes use of the binary quaternion moment preserving threshold technique. With this technique, color constancy throughout the spectrum and in the physical space are taken into account when they divide the color clusters located inside the RGB cube. The segmentation results are contrasted with JSEG and some of the recent established segmentation methods. These comparisons demonstrate how the proposed quantization approach strengthens the JSEG segmentation.
A Lightweight Hybrid Template-Matching–CNN Framework with Attention-Guided Fusion for Robust Small Object Detection Zangana, Hewa Majeed; Omar, Marwan; Mirza, Mohammed Aquil; Cao, Xinwei; Wani, Sharyar
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.v8i1.14751

Abstract

Small object detection in aerial and surveillance imagery remains challenging due to low resolution, occlusion, and background clutter. This study introduces a novel hybrid detection framework that fuses template matching with a deep learning detector (Faster R-CNN) through an attention-guided decision fusion mechanism. The novelty lies in (i) a dual-stage fusion pipeline that integrates precise structural cues from template matching with deep semantic features, and (ii) a custom scale-aware focal loss, adapted from Focal Loss to emphasize hard and small objects by dynamically increasing penalties for low-confidence predictions. Evaluated on a Pascal VOC subset (1000 images, 5 classes), the proposed system achieves an mAP improvement of 3.5% over the Faster R-CNN baseline and surpasses YOLO-Lite and R-CNN variants in precision and recall. The hybrid design adds only a minimal computational overhead (0.45 s/image vs. 0.42 s for Faster R-CNN), demonstrating favorable efficiency–accuracy trade-offs suitable for scalable deployment. These findings highlight the framework’s robustness, particularly in scenes containing occlusion, clutter, or visually small targets. Limitations regarding template dependency are discussed, along with future directions for automatic template generation and real-time video adaptation.
A Self-Balancing 13-Level Single-Phase Triple Gain Inverter Balakrishnan, Sakthisudhursun; Srinivasan, Muralidharan; Subramaniam, Sundaramahalingam; Narayanaswamy, Vanaja
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.v8i2.14920

Abstract

A potential single stage power electronics interface for integrating renewable sources like PV, fuel cells, etc. with an AC load is a switched capacitor based multilevel inverter with boosting capability. In this research, a thirteen-level MLI topology with voltage boosting factor of three a gain of three for the renewable energy integration is proposed. The proposed MLI requires twelve unidirectional switches, one bidirectional switch, three capacitors, and a single DC source. The voltage stress across each switch is lower than the peak output voltage since the proposed inverter doesn't need a back-end H-Bridge. The proper selection of switching sequence enables the self regulation of voltage across all three capacitors, is self-regulated eliminating the need of additional sensor/control. Simulation results obtained from MATLAB/Simulink confirm the stable operation of the MLI and the self-regulation of switched capacitor voltages under step variations in load, source voltage, and modulation index. A comprehensive comparison with existing topologies demonstrates the superiority of the proposed topology in terms of reduced total number of components and lower total blocking voltage.
Adaptive Protection Scheme Using Optimal Coordination of Directional Overcurrent Relays for Active Distribution Networks Neda, Omar Muhammed
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.v8i1.14954

Abstract

Directional overcurrent relays (DOCRs) are widely used for the protection of distribution and sub-transmission systems due to their simplicity and cost-effectiveness. Proper coordination of these relays is essential to ensure selectivity, reliability, and fast fault clearance. However, due to the complex and nonlinear nature of modern power systems with high-level constraints, achieving optimal coordination is challenging. Traditional protection schemes relying on fixed relay settings often fail under dynamic operating conditions, leading to increased relay operating times, coordination violations, and protection blind zones. This paper develops an optimization-based DOCR coordination framework to minimize total inverse relay operating time while preserving coordination constraints, including predefined Coordination Time Intervals (CTIs) between primary and backup relays. Two strategies are proposed: a combined method, applying a single relay setting group for all network configurations, and an adaptive method, generating specific relay setting groups for each configuration or cluster. The adaptive method also incorporates relay characteristic curve tuning, allowing each DOCR to select the most suitable inverse-time characteristic. Both strategies are implemented using a Genetic Algorithm (GA) and tested on IEEE 8-bus system. Simulation results show that the adaptive method significantly outperforms the combined strategy. In the IEEE 8-bus system, total operating time is reduced from 56.7742 s to 13.0026 s (≈81.7%). However, its practical deployment may require reliable communication and configuration detection. These results confirm that the GA-based adaptive DOCR strategy provides faster, more selective, and reliable protection, making it highly suitable for modern active distribution networks.

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