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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 75 Documents
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Search results for , issue "Vol 15, No 2: April 2026" : 75 Documents clear
Conditional toggle algorithm: an adaptive metaheuristic and its implementation on handling engineering problems Kusuma, Purba Daru; Widyantara, Helmy
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10048

Abstract

There have been numerous new metaheuristic algorithms in this decade. Unfortunately, the attention on taking stagnation is still less considered so that it is difficult to find new metaheuristic algorithms that are enriched with stagnation taking mechanism. This work introduces a new method called conditional toggle algorithm (CTA). CTA is designed to be adaptive on facing enhancement and stagnation during iteration as its novelty. When enhancement occurs, the exploitation-focused look is applied. Meanwhile, the exploration-focused look is applied when stagnation occurs. The efficacy of CTA is then measured by implementing to solve three cases: 23 functions, 4 engineering design problems, and economic emission dispatch (EED) problem in Java-Bali power system in Indonesia. CTA is compared with five new metaheuristic algorithms. The evidence provides that CTA is supreme in taking high dimension functions and competing in taking fixed dimension functions. CTA is also supreme in taking pressure vessel and speed reducer design problems and the EED problem. But its performance is average in taking welded beam and spring design problems. In the future, CTA can be modified with other metaheuristic algorithms to enhance its performance and challenged to take broader problems, especially in electrical engineering fields.
Optimized resource management system for precision farming using cluster based wireless sensor network Kallur, Sandeep B.; Singh, Kishan
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10265

Abstract

The deployment of wireless sensor networks (WSNs) in precision agriculture is essentially guarded by the energy limitations of sensor nodes, which can impede long-term, autonomous field monitoring. This paper introduces a hierarchical, cluster-based resource management system intended to prevail over these limitations. The central part of our approach is a dynamic clustering algorithm that intelligently groups sensor nodes to balance energy consumption and streamline data transmission across the network. Within each cluster, intra-cluster data aggregation is performed to fuse raw sensor data—encompassing critical parameters like soil temperature, humidity, and pH—thereby minimizing redundant packet transmissions. This aggregated data drives a predictive control model that automates decision-making for the precise actuation of irrigation and fertigation systems. Empirical validation demonstrates that our methodology achieves a dual objective: it significantly extends the network's operational lifespan by enhancing energy efficiency and throughput while reducing latency. Concurrently, this optimized resource allocation directly correlates with increased crop yield, presenting a robust and scalable framework for sustainable, high-efficiency agriculture, particularly in resource-intensive environments like urban and vertical farms.
THD reduction in novel asymmetrical 21 level MLI using FLO-MOA algorithm optimized cascaded controller technique Ramaiah, Mohandas; Sathyanarayanan, Bharath Vaniyambadi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10974

Abstract

Since multi-level inverters (MLIs) may operate at lower switching frequencies and reduce switching losses, they are frequently used in high and medium electric drives for renewable energy applications. Lower-order harmonics could result from using a lower switching frequency, which would increase line current distortion. MLIs provide fewer output harmonics than traditional converters. This research suggested a hybrid strategy for creating a 21-level MLI. The suggested cascaded fractional-order tilt integral fractional-order proportional tilt derivative (FOTI-FOPTID) controller is used to adjust the switching pulse for the 21-level MLI. In this work, the Mother Optimization algorithm (MOA) is combined with the Frilled Lizard optimization (FLO) method to improve it. This results in the FLO-MOA algorithm, which is used to adjust the controller's gain parameters. This strategy reduces the number of switches and asymmetrical sources of dc voltage. The inverter's efficiency is raised, power loss is minimized, total harmonic distortion (THD) is decreased, and output voltage levels are improved.
Real-time object detection and XAI-based activation map visualization using YOLOv8s Yusof, Ashaari; Hishamuddin, Muhammad; Hossen, Md. Jakir
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.9765

Abstract

This study introduced a methodology for real-time object detection and interpretability using YOLOv8s, trained on the MS common objects in context (COCO) dataset. The system captured live webcam footage, processes frames resized to 640×384, and applies YOLOv8s to detect objects with bounding boxes, labels, and confidence scores. YOLOv8s architecture comprising a CSPDarknet53-based backbone, neck, and head ensures efficient feature extraction and accurate detection. To enhance model transparency, activation map generation is implemented by attaching forward hooks to intermediate convolutional layers. Feature maps are captured during the forward pass, averaged, normalized, and resized to match the original image dimensions. This visualization highlights regions influencing the model’s predictions, aligning with explainable artificial intelligence (XAI) principles. Experimental results demonstrate high detection accuracy and effective interpretability in indoor environments, making the framework suitable for robotics applications requiring both precision and transparency. The proposed method offers a practical and explainable solution for real-time scene understanding in intelligent systems.
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Md Suhaimin, Mohd Suhairi; Wibowo, Adi; Moung, Ervin Gubin; Anthony, Patricia; Ahmad Hijazi, Mohd Hanafi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.

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