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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 13, No 6: December 2024" : 75 Documents clear
Analysis of the power sector in Bangladesh: current trends, challenges, and future perspectives Sarker, Md Tanjil; Farid, Fahmid Al; Alam, Mohammed Jaber; Ramasamy, Gobbi; Karim, Hezerul Abdul; Mansor, Sarina; Sadeque, Md. Golam
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7503

Abstract

Bangladesh’s economic development is largely dependent on the power sector, which promotes sustainability and growth. The country’s future energy security, however, is seriously threatened by the natural gas reserves running out by 2028. As a result, the current energy mix has to be modified right away to ensure Bangladesh’s sustained economic growth. This research paper offers a thorough analysis of Bangladesh’s power sector’s current state. With a focus on important metrics like installed capacity, electricity generation, and distribution infrastructure, the study seeks to provide insights into the sector’s opportunities, challenges, and strengths. The research highlighting the importance of energy security and forecasting the projected energy demand in Bangladesh. The study also looks at current projects and advancements that have shaped Bangladesh’s power industry. This research also provides an ideal energy option that supports Bangladesh's sustainable growth. This analysis offers significant insights into the dynamics of the power industry in Bangladesh, elucidating it is present trajectory, the challenges it encounters, and the potential avenues for achieving a more sustainable and resilient energy future.
A systematic review of radar technologies for surveillance of foreign object debris detection on airport runway Nugraha, Eka Setia; Apriono, Catur; Zulkifli, Fitri Yuli
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8040

Abstract

Flights are projected to reach eight billion globally by 2037, demanding airport operators manage operations effectively, including safety on the runway due to the high number of aircraft movements. One crucial issue is any foreign object, commonly known as foreign object debris (FOD), that must be detected and cleaned immediately to ensure aircraft safety when taking off, landing, and taxing. The International Civil Aircraft Organization (ICAO) reported that FOD causes 10.08% of aviation accidents. Most airports manually monitor and detect FOD, which could be more effective and dangerous. Therefore, it is important to provide FOD detection systems with proper technologies. Radar technologies are potential FOD detection techniques that offer robustness to weather fluctuation. However, some factors must be considered properly to provide an effective FOD system. This paper reviews radar technologies for FOD detection on airport runways by considering factors, including types of debris, detection coverage, mode of radars, frequencies, and attenuation. It was found that all critical factors considered contribute to the quality of detection. This paper will provide guidelines for developing FOD detection based on radar technologies regarding airport necessities and its specific environment.
Prediction of palm oil production using hybrid decision tree based on fuzzy inference system Tsukamoto Tundo, Tundo; Saifullah, Shoffan; Yel, Mesra Betty; Irawansah, Opi; Mubarak, Zulfikar Yusya; Saidah, Andi
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7773

Abstract

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models—J48, reduced error pruning (REP), and random—in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models—specifically J48-REP, REP-Random, and Random-J48—to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto’s fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry.
Speech emotion recognition with optimized multi-feature stack using deep convolutional neural networks Fadhil, Muhammad Farhan; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.6044

Abstract

The human emotion in communication plays a significant role that can influence how the context of the message is perceived by others. Speech emotion recognition (SER) is one of a field study that is very intriguing to explore because human-computer interaction (HCI) related technologies such as virtual assistant that are implemented nowadays rarely considered the emotion contained in the information relayed by human speech. One of the most widely used ways to perform SER is by extracting features of speech such as mel frequency cepstral coefficient (MFCC), mel-spectrogram, spectral contrast, tonnetz, and chromagram from the signal and using a one-dimensional (1D) convolutional neural network (CNN) as a classifier. This study shows the impact of implementing a combination of an optimized multi-feature stack and optimized 1D deep CNN model. The result of the model proposed in this study has an accuracy of 90.10% for classifying 8 different emotions performed on the ryerson audio-visual database of emotional speech and song (RAVDESS) dataset.
Anomaly intrusion detection using machine learning- IG-R based on NSL-KDD dataset Aljammal, Ashraf H.; Al-Oqily, Ibrahim; Obiedat, Mamoon; Qawasmeh, Ahmad; Taamneh, Salah; Wedyan, Fadi I.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7308

Abstract

Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process.
Public complaint tweet data feature analysis for sentiment classification Rasywir, Errissya; Pratama, Yovi; Irawan, Irawan; Istoningtyas, Marrylinteri
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
Zinc oxide-coated fiber-optic sensors for monitoring of edible oil adulteration with internet of things integration Haroon, Hazura; Othman, Siti Khadijah Idris@; Razak, Hanim Abdul; Zain, Anis Suhaila Mohd; Salehuddin, Fauziyah; Mukhtar, Wan Maisarah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7604

Abstract

The study proposes a novel approach for detecting adulteration in edible oils utilising a zinc oxide (ZnO)-coated optical sensor. The procedure included the development of a sensor probe using a plastic optical fiber (POF) with a ZnO nanolayer deposition. The ZnO nanorods were applied to the surface of the POF via a hydrothermal process. The sensitivity and accuracy of uncoated and ZnO-coated POFs were compared, and it was discovered that the ZnO-coated POF was more sensitive to changes in the refractive index of the samples under testing. The study ascertained a correlation between the optical power and voltage of the sensor and the refractive index of the medium. As the adulterant concentration in the oil mixture increased, the refractive index of the medium altered. As a result, both the sensor’s output voltage and optical power decreased. Upon completion, it was discovered that the uncoated POF had a sensitivity of 0.073 V/%, whereas the ZnO-coated POF had a sensitivity of 0.085 V/%. These findings highlight the effectiveness of ZnO-coated optical sensors, as well as their potential integration into internet of things (IoT) platforms for monitoring adulteration in edible oils.
Parasitic isolation structure for mutual coupling reduction in a multiple input multiple output antenna Godi, Revati C.; Patil, Rajendra R.; Kinagi, Revansiddappa S.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7860

Abstract

This paper reports a design of 2×1 multiple input multiple output (MIMO) structure of antenna with 23×45 mm2 dimension. Each element in the MIMO antenna is a quarter wave transformer fed microstrip patch antenna. To lessen the effect of coupling, a rectangular parasitic decoupler is positioned between the two elements. Results report that antenna resonates at 6 GHz, coupling is reduced by 14 dB using parasitic decoupler S12 and S21 obtained with parasitic decoupler are same which -33.06 dB. The diversity gain (DG) is 9.99 dB, which is nearly close to 10 dB, and the envelope correlation coefficient (ECC) is less than 0.00034. These values reflect the good diversity performance. Measured findings match those from the simulation. As we confront the delicate environment, the proposed antenna is suited for number of wireless applications, including 802.11, 802.16 standards of IEEE.
Efficient k-way partitioning of very-large-scale integration circuits with evolutionary computation algorithms P., Rajeswari; Chandra S., Theodore; Sasi, Smitha
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.5781

Abstract

The standardization of very-large-scale integration (VLSI) physical architecture for VLSI chips and multichip platforms is now in its early stages of development. The purpose of VLSI partitioning is to divide the circuit into numerous smaller circuits with few connections in between. Partitioning is the fundamental problem in circuit design and division. The efficient method of evolutionary computation may be used to tackle the partitioning problem in VLSI circuit design. It provides a heuristic approach to solve this problem by exploring the solution space and incrementally improving the quality of the solutions. In order to obtain the shortest wire length (WL), area, and connections, an evolutionary optimized simulated annealing memetic algorithm (OSAMA) that incorporates one or more local search phases inside its evolutionary cycle was developed.
Development and evaluation of a network intrusion detection system for DDoS attack detection using machine learning Ramachandra, Bharathi; Surekha, T. P.
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7565

Abstract

Distributed denial of service (DDoS) attacks involves disrupting a target system by flooding it with an immense volume of traffic originating from numerous sources. These attacks can disrupt online services, causing financial losses and reputational damage to various organizations. To combat this threat, the proposed network intrusion detection system (NIDS) utilizes machine learning (ML) algorithms trained on the KDDCup99 dataset. This dataset encompasses a diverse array of network traffic patterns, bounded by both regular traffic and various attack types. By training the NIDS on this dataset, it becomes capable of accurately identifying DDoS attacks based on their distinctive patterns. The NIDS model is constructed using ML approaches like random forest (RF), support vector machines (SVM), and naive Bayes (NB). The developed NIDS is evaluated using performance metrics such as accuracy, precision, recall, F1-score, and receiver operating characteristic (ROC) curve. The proposed method demonstrates the NIDS’s accuracy of about 93%, precision of 99% and recall of 92% in detecting DDoS attacks, transforming it into a valuable tool for network security in comparison with the current methods. The study contributes to the domain of network security by providing an effective NIDS solution for detecting the DDoS attacks in the wireless sensor network.

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