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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 2,901 Documents
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A novel recommender system for adapting single machine problems to distributed systems within MapReduce Orynbekova, Kamila; Kadyrov, Shirali; Bogdanchikov, Andrey; Oktamov, Saidakmal
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.v14i1.8370

Abstract

This research introduces a novel recommender system for adapting single-machine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression model's effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.
Swift and efficient cinnamon plant disease classification using robust feature extraction and machine learning techniques Thandapani, Sujithra; Selvaraj, Durai; Iqbal Mahaboob, Mohamed; Chakravarathi Bharathi, Venkatakrishnan; Vinayagam, Prabhu
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.v14i1.8254

Abstract

The extraction of features and textures holds a crucial significance in the realm of image processing and machine vision systems. Even though artificial intelligence (AI) techniques are superior in attaining the best results in image processing, several challenges remain open for further research in computation complexity, memory, and power requirements. In this context, robust preprocessing techniques are required to address such shortcomings and reduce the computational cost of predictive tasks. This paper employed two feature extraction levels to extract the best possible features from images of the cinnamon plant. Local directional positional pattern (LDPP) extracts global image features, while local triangular coded pattern (LTCP) extracts local features. It helps to provide detailed and more relevant information about the image texture. Once features are extracted, identifying and categorizing diverse textures within an image relies on recognizing their unique features. Typically, descriptors serve as the means for representing images in our work. Afterwards, we used ensemble learning to attain better classification results with the help of weak classifiers. Extracted features are provided to machine learning (ML) models like support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) for better classification of the cinnamon category.
Predicting graduation in Moroccan open-access bachelors: early indicators and re-enrollment data Oqaidi, Khalid; Aouhassi, Sarah; Mansouri, Khalifa
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.v14i1.8580

Abstract

The primary aim of higher education institutions is the successful graduation of their students. This study explores open-access higher education in Morocco, introducing a predictive model for assessing the probability of students achieving a science bachelor's degree. We analyzed data from 2012 to 2022, initially encompassing 45,573 student entries, and narrowed it down to 14,054 records after data cleaning. Focusing on early academic indicators from enrollment onwards-excluding current program performance—we used popular machine learning classifiers to examine the predictive capacity for student graduation and early dropout. Our comparison included analyses with and without re-enrollment data. Upon analyzing various machine learning algorithms, we attained accuracies between 79% and 86%, identifying random forest (RF) as the superior model for predicting outcomes both with and without incorporating re-enrollment data. This analysis was grounded on initial indicators observed during enrollment and throughout subsequent years, deliberately excluding current academic performance metrics from consideration.
An opinionated sentiment analysis using a rule-based method Zeleke Mekonen, Mareye; Assegie, Tsehay Admassu; Palit, Shamik; Kalyan Kumar, Angati; Sinha Roy, Chandrima; Priya Kompala, Chandi; Kumar Napa, Komal
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.v14i1.8568

Abstract

The categorization of opinions into positive, negative, or neutral facilitates information gathering, pinpointing individual weaknesses, and streamlining the decision-making process. Precision in opinion classification enables decision-makers to extract valuable insights, make well-informed decisions, and execute suitable actions. Sentiment analysis is language-specific due to the distinct morphological structures unique to each language, distinguishing them from one another. This study implemented a rule-based sentiment analysis approach for Kafi-noonoo opinionated texts, leveraging a rule-based system tailored for smaller datasets that operate based on a predefined set of rules. The rule-based mechanism calculates the overall polarity of a given sentence by applying a set of rules and categorizes it into positive, negative, or neutral sentiments upon identifying sentimental terms from a dedicated file. While the analysis utilized 1,500 words sourced from Facebook and music review samples, the modest sample size yielded satisfactory results. Performance evaluation metrics such as precision, recall, and F-measure were employed, indicating positive word scores of 91%, 86%, and 88.4%, and negative word scores of 80%, 75%, and 77%, respectively.
Impact of usability on continuance usage intention in language learning apps with gamification features Ulfiah, Ulfiah; Rahthin Rasyadan, Muhammad Faris; Utami, Wening Tyas; Sunardi, Sunardi; Fitria Murad, Dina
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.v14i1.8023

Abstract

With the increasing popularity of language learning applications, gamification has become one of the approaches frequently integrated to enhance continuance usage intention (CUI). However, how the usability of these gamification features influences the intention to continue usage is not yet fully understood. Through the usability testing and system usability scale (SUS) method, this study evaluates the level of usability of gamification features in language learning applications with a novel research approach that also involves specific analysis regarding usability aspects according to Nielsen, including learnability, efficiency, memorability, errors, and satisfaction towards the CUI, categorized through the SUS statements grouping and then processed using the SPSS application. The study results indicate that the SUS scores show above-average scores for all three applications: Duolingo application at 77.08, Elsa Speak at 70, and Cake Learn at 70.58. Other findings suggest that usability aspects generally significantly influence CUI; however, only the satisfaction variable impacts CUI, which was observed only in Duolingo and Elsa Speak. These findings indicate that the overall usability of gamification features positively impacts CUI using language learning applications, thereby implying the need for continuous development.
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.

Page 248 of 291 | Total Record : 2901

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