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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 14, No 1: February 2025" : 75 Documents clear
A novel hybrid SMOTE oversampling approach for balancing class distribution on social media text Raveendhran, Nareshkumar; Krishnan, Nimala
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8380

Abstract

Depression is a frequent and dangerous medical disorder that has an unhealthy effect on how a person feels, thinks, and acts. Depression is also quite prevalent. Early detection and treatment of depression may avoid painful and perhaps life-threatening symptoms. An imbalance in the data creates several challenges. Consequently, the majority learners will have biases against the class that constitutes the majority and, in extreme situations, may completely dismiss the class that constitutes the minority. For decades, class disparity research has employed traditional machine learning methods. In addressing the challenge of imbalanced data in depression detection, the study aims to balance class distribution using a hybrid approach bidirectional long short-term memory (BI-LSTM) along with synthetic minority over-sampling and Tomek links and synthetic minority over-sampling and edited nearest neighbors’ techniques. This investigation presents a new approach that combines synthetic minority oversampling technique with the Kalman filter to provide an innovative extension. The Kalman-synthetic minority oversampling technique (KSMOTE) approach filters out noisy samples in the final dataset, which consists of both the original data and the artificially created samples by SMOTE. The result was greater accuracy with the BI-LSTM classification scheme compared to the other standard methods for finding depression in both unbalanced and balanced data.
Cluster-based routing protocols through optimal cluster head selection for mobile ad hoc network Alayu Melkamu, Yenework; Purushothaman, Raguraman; Sujatha, Madugula; Kumar Napa, Komal; Zeleke Mekonen, Mareye; Admassu Assegie, Tsehay; Olalekan Salau, Ayodeji
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8863

Abstract

Mobile ad hoc networks (MANETs) operate without fixed infrastructure, with mobile nodes acting as both hosts and routers. These networks face challenges due to node mobility and limited resources, causing frequent changes in topology and instability. Clustering is essential to manage this issue. Significant research has been devoted to optimal clustering algorithms to improve cluster-based routing protocols (CBRP), such as the weighted clustering algorithm (WCA), optimal stable clustering algorithm (OSCA), lowest ID (LID) clustering algorithm, and highest connectivity clustering (HCC) algorithm. However, these protocols suffer from high re-clustering frequency and do not adequately account for energy efficiency, leading to network instability and reduced longevity. This work aims to improve the CBRP to create a more stable and long-lasting network. During cluster head (CH) selection, nodes with high residual energy or degree centrality are chosen as CH and backup cluster head (BCH). This approach eliminates the need for re-clustering, as the BCH can seamlessly replace a failing CH, ensuring continuous cluster maintenance. The proposed modified cluster-based routing protocol (MCBRP) evaluated network simulator 2 (ns2) demonstrates that MCBRP is more energy-efficient, selecting optimal CH and balancing the load to enhance network stability and longevity.
Optimal reactive power dispatch using modified-ant lion optimizer with flexible AC transmission systems devices Chaitanya, Sela Naga Venkata Sri Krishna; Bakkiyaraj, R. Ashok; Rao, Bathina Venkateswara; Jayanthi, Kalikrishnan
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.5882

Abstract

This study focuses on reactive power planning in the IEEE30-bus test system, specifically involving the integration of flexible AC transmission systems (FACTS) within the utility system. The primary objective is to minimize power loss and voltage deviation. To address this, a recently developed optimization algorithm called modified ant-lion optimizer (MALO) is applied to solve the optimal reactive power dispatch (ORPD) problem on the IEEE 30-bus system. A comparative analysis is conducted between the results obtained with and without FACTS devices. The findings reveal that the utilization of FACTS devices leads to significantly improved outcomes compared to scenarios without FACTS devices. Among the FACTS devices studied, the unified power flow controller (UPFC) demonstrates superior performance compared to the static synchronous compensator (STATCOM) and interline power flow controller (IPFC).
Hardware-based efficient Mickey-128 stream cipher with unrolling factors for throughput enhancement Ananth, Raghavendra; Rao Malode V., Panduranga; Swamy Ramaiah, Narayana
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8270

Abstract

The emerging trend known as "ubiquitous computation" aims to incorporate intelligent gadgets into commonplace items. The lightweight cryptographic techniques are being researched and developed to minimize the gadgets' resources and a perpetual desire to reduce production expenses. A key element of symmetric cryptography, the stream cipher has unique benefits in terms of scalability as well as performance. The Mickey-128 stream cipher is designed and implemented in this manuscript. Additionally, unrolling features are incorporated with Mickey-128 cipher to enhance the throughput. The Mickey-128 contains a 128-bit key, an initialization vector (IV), and two clocking registers (R and S) with mapping units. The finite state machine (FSM) controller initializes and controls the key, IV and RS- registers data. The proposed Mickey-128 cipher runs on an Artix-7 field programmable gate array (FPGA) at 639.1 MHz and uses less than 1% of the chip's area (Slices). For unrolling factors 8 and 16, the Mickey-128 cipher achieves a throughput of 5.12 Gbps and 10.23 Gbps, accordingly. Finally, a comparison is made between the proposed Mickey-128 cipher and the existing ciphers' better hardware efficiency and throughput.
Continual learning on audio scene classification using representative data and memory replay GANs Daqiqil ID, Ibnu; Abe, Masanobu; Hara, Sunao
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8127

Abstract

This paper proposes a methodology aimed at resolving catastropic forgetting problem by choosing a limited portion of the historical dataset to act as a representative memory. This method harness the capabilities of generative adversarial networks (GANs) to create samples that expand upon the representative memory. The main advantage of this method is that it not only prevents catastrophic forgetting but also improves backward transfer and has a relatively stable and small size. The experimental results show that combining real representative data with artificially generated data from GANs, yielded better outcomes and helped counteract the negative effects of catastrophic forgetting more effectively than solely relying on GAN-generated data. This mixed approach creates a richer training environment, aiding in the retention of previous knowledge. Additionally, when comparing different methods for selecting data as the proportion of GAN-generated data increases, the low probability and mean cluster methods performed the best. These methods exhibit resilience and consistency by selecting more informative samples, thus improving overall performance.
Enhancing costumer churn prediction with stacking ensemble and stratified k-fold Rofik, Rofik; Unjung, Jumanto; Prasetiyo, Budi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8112

Abstract

In the era of rapid technological advancement, the telecommunications industry undergoes significant changes. Factors such as the speed of technological change, high customer expectations, and changing preferences are the main obstacles that affect the dynamics of telecommunications companies. One major issue faced is the high customer churn rate, adversely impacting company revenue and profitability. Previous studies indicate that customer churn prediction remains complex in the telecommunications industry, with opportunities to optimize algorithm selection and prediction model construction methods. This research aims to improve the accuracy of customer churn prediction by employing a complex model that utilizes stacking ensemble learning techniques. The proposed model combines 6 base algorithms: extreme gradient boosting (XGBoost), random forest, light gradient boosting machine (LightGBM), support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN), with XGBoost as the meta-learner model. The research process involves preprocessing, class data balance with synthetic minority oversampling technique (SMOTE), training using stratified k-fold, and model evaluation. The model is tested using the Telecom Churn dataset. The evaluation results show that the constructed stacking model achieves 98% accuracy, 98.74% recall, 98.03% precision, and 98.38% F1 score. This study demonstrates that optimizing the stacking ensemble model with SMOTE and stratified k-fold enhances customer churn prediction accuracy.
Hybrid deep learning: a comparative study on ai algorithms in natural language processing for text classification Mahmudul Hasan, Md.; Kumar Das, Rajesh; Hassan, Mocksidul; Razia, Sultana; Ferdous Ani, Jannatul; Akter Khushbu, Sharun; Islam, Mirajul
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7617

Abstract

The objective of this research project is to assess the effectiveness of various machine learning algorithms, including deep learning and combination approaches, in performing tasks such as categorizing products into specific categories using data from an e-commerce platform named "OTHOBA." In this study, a dataset consisting of 19,087 data samples is used to evaluate the effectiveness of seven supervised machine learning models. Among these models are three based on deep learning: long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and 1D convolutional (Conv1D), as well as a multi-layer model that combines Conv1D and LSTM approaches. The task at hand is the classification of product categories. The LSTM-based model demonstrates the highest accuracy rate of 96.23% among the deep learning models, while the logistic regression (LR) models achieve the highest accuracy scores of 97.00% for product category classification. Overall, the proposed models and techniques show significant progress in natural language processing (NLP) research for text classification, specifically in English, and have practical applications for e-commerce sites.
An efficient snow flake schema with hash map using SHA-256 based on data masking for securing employee data Bharath, Tumkur Shankaregowda; Channakrishnaraju, Channakrishnaraju
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8767

Abstract

In various organizations and enterprises, data masking is used to store sensitive data efficiently and securely. The data encryption and secret-sharing-based data deploying strategies secure privacy of subtle attributes but not secrecy. To solve this problem, the novel snowflake schema with the hash map using secure hash algorithm-256 (SHA-256) is proposed for the data masking. SHA-256 approach combines data masking by secret sharing for relational databases to secure both privacy as well as the confidentiality of secret employee data. The data masking approach supports preserving and protecting the privacy of sensitive and complex employee data. The data masking is developed on selected database fields to cover the sensitive data in the set of query outcomes. The proposed method embeds one or multiple secret attributes about multiple cover attributes in a similar relational database. The proposed method is validated through different performance metrics such as peak signal-to-noise ratio (PSNR) and error rate (ER) and it achieves the values of 50.084dB and 0.0281 when compared to the existing methods like Huffman-based lossless image coding and quad-tree partitioning and integer wavelet transform (IWT).
Doppler radar-based pothole sensing using spectral features in k-nearest neighbors Aiman Dani Asmadi, Muhammad; Zainuddin, Suraya; Mohd Nasir, Haslinah; Syafiza Md Isa, Ida; Emileen Abd Rashid, Nur; Pasya, Idnin
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8398

Abstract

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning.
Hybrid approach for tweets similarity classification founded on case based reasoning and machine learning techniques Bensassi, Ismail; Kouissi, Mohamed; Ndama, Oussama; En-Naimi, El Mokhtar; Zouhair, Abdelhamid
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8452

Abstract

Twitter sentiment analysis becomes a popular research subject in the last decade. It aims to extract sentiments of users through their public opinion about a given topic. This article proposes a hybrid approach for Twitter sentiment analysis founded on dynamic case based reasoning (DCBR), multinomial logistic regression machine learning algorithm and multi-agent system. Our approach proposes a method to find similar tweets based on content similarity measure using the scientific measurement of keyword weight term frequency-inverse document frequency (TF-IDF). This approach includes gathering and pre-processing tweets, getting score and polarity of tweets, the use of multinomial logistic regression machine learning algorithm to classify our tweets into various classes, using the feature extraction method to extract useful features and then the K-nearest neighbors (KNN) algorithm to make it easier to find similar tweets to our tweet target case. This approach is adaptive and generic and able to track users' tweet to predict their behavior and sentiments in critical situations and delivering personalized content. The current study focuses on Covid-19 tweets, and a public Twitter dataset is used for this purpose.

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