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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,174 Documents
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Energy efficient secured-quality of service routing protocol for mobile ad hoc network using multi-objective optimization Veeramani Ramasamy; Madhan Mohan Ramalingam; Mahesh Chitraivel
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1486-1495

Abstract

This article proposed a hybrid energy-efficient secured quality of service (QoS) based multipath routing protocol. A modified crow search combined with the tunicate swarm butterfly optimisation algorithm (TSBO) with a density-based clustering technique strategy is proposed for the selection of cluster heads after the initial cluster formations. Among all the nodes, the cluster head is selected, and employing the collaborative trust-based approach (CTBA), which employs the trust factor for mobile ad hoc network (MANET) data transmission, a node's authentication is supplied. Finally, to implement the safe routing technique, this article suggested a hybrid multipath routing protocol combining multi-objective grey wolf optimisation (MO-GWO) with a fruit fly algorithm. The NS3 simulator is used to assess the proposed work. The packet delivery ratio metric performs 4% better than the current models. As a result, the suggested approach performs better for the end-to-end delay, energy consumption, packet delivery ratio (PDR), and throughput; it also uses less energy and has a shorter delay. Additionally, single-path nodes with the same energy value have lower throughput than multi-path nodes.
Document retrieval using term term frequency inverse sentence frequency weighting scheme Mohannad T. Mohammed; Omar Fitian Rashid
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1478-1485

Abstract

The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
The blockchain internet of things: review, opportunities, challenges, and recommendations Mahmood A. Al-Shareeda; Murtaja Ali Saare; Selvakumar Manickam
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1673-1683

Abstract

A new technology known as the internet of things (IoT) allows both physical and virtual items to be linked and communicated with one another, creating new digital services that enhance our fineness of sustenance. The IoT system has a number of benefits, but because of its present centralized architecture, there are several problems with regard to data integrity, security, privacy, and single points of failure. The future development of IoT applications is hampered by these difficulties. To tackle these problems, it might be best to integrate the Internet of Things with one of the distributed ledger solutions. The blockchain is one of the most frequent and well-liked varieties of distributed ledger technologies. Numerous advantages can result from integrating blockchain technology with the IoT called blockchain internet of things (BIoT). In this paper, we show a brief overview of blockchain, its components of blockchain, and its features of blockchain. Meanwhile, we describe the architecture of BIoT, Issues of BIoT, and BIoT applications. Additionally, this paper provides a future research challenge and open issues.
Comparative study of nature-inspired maximum power point tracking algorithms for partially shaded photovoltaic systems Prasannati Kulkarni; Suresh Deshmukh
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1242-1249

Abstract

Photovoltaic (PV) systems are widely used for converting solar energy into electrical energy. However, PV systems are susceptible to partial shading, leading to fluctuations in temperature and irradiation that degrade the system's performance. To overcome this challenge, maximum power point tracking (MPPT) algorithms are implemented in PV systems. This research paper provides a comprehensive comparative analysis of three nature-inspired MPPT algorithms, namely cuckoo search, grey wolf and fish swarm optimization, to improve the performance of PV systems under partially shaded conditions. The study evaluates the speed, complexity, compatibility, and stability of each algorithm, and concludes that the fish swarm optimization algorithm is the most effective among the three. The novelty of this research lies in the in-depth comparison of nature-inspired MPPT algorithms (specifically fish swarm optimization) for partially shaded PV systems, offering valuable insights for researchers to improve the performance of PV systems.
Area control error enhancement of two-area power system using hybrid intelligence optimal controller Muhammad Abdillah; Herlambang Setiadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i3.pp1258-1265

Abstract

Area control error (ACE) is a critical factor in linked power systems. When a disturbance occurs, ACE is utilized to determine how much power should be deployed. As a result, it is critical that the ACE have as little inaccuracy as feasible. This research provided a strategy for improving the dynamic response of ACE in a power system. A hybrid optimal controller is the name given to this technology. Coordination between the proportional-integral (PI) controller and the state feedback controller based on the linear quadratic regulator (LQR) is the concept of a hybrid optimum controller. All controller parameters are created utilizing artificial immune system (AIS) clonal selection to improve coordination. The proposed control mechanism is demonstrated using a two-area power system as a test system. To investigate the efficacy of the suggested strategy, time domain simulation is used. The simulation results show that the suggested method outperforms the previous situations in this work (the overshot of frequency deviation in areas 1 and 2 is 0.00029 and 0.00015, respectively)
Pothole detection in bituminous road using convolutional neural network with transfer learning Mukesh Kumar Tripathi; Donagapure Baswaraj; Shyam Deshmukh; Kapil Misal; Nilesh P. Bhosle; Sunil Mahadev Sangve
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 3: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i3.pp1950-1957

Abstract

The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.
Diagnosis and treatment of Guillain-Barre using the prolog expert system Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Pucuhuayla-Revatta, Félix; Yactayo-Arias, Cesar

Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp333-342

Abstract

This research is mostly about Guillain-Barre syndrome (GBS), a complicated neurological condition with many subtypes that make diagnosis and treatment hard, even though medical care is always getting better. The main goal of this study is to build and test an expert system that can correctly diagnose these subtypes, with a focus on early detection and personalized treatments. The evaluation of the system was carried out using a dataset composed of 20 cases (12 positive and 8 negative). A confusion matrix was used to evaluate key metrics such as precision, sensitivity, and specificity. The findings demonstrate precision and sensitivity of 83% and specificity of 75%. These findings unambiguously demonstrate the efficacy of the system in correctly identifying positive Guillain-Barre cases while substantially reducing false negatives. In conclusion, this expert system offers a potentially useful tool to improve the accuracy of the diagnosis and treatment of Guillain-Barre patients. However, to take advantage of its full potential in clinical practice, it should be used as diagnostic support and not replace the medical staff, and it should be updated periodically to reflect new findings in medicine.
Efficient packaging defect detection: leveraging pre-trained vision models through transfer learning Wiwi Prastiwinarti; Mera Kartika Delimayanti; Hendra Kurniawan; Yoga Putra Pratama; Hanin Wendho; Rizky Adi
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i3.pp2096-2106

Abstract

The inspection of packaging defects is a crucial aspect of maintaining the quality of industrial production, especially in the case of boxed products. This study introduces a novel approach for detecting physical defects in product packaging boxes by integrating image processing with deep learning, specifically transfer learning with two images as an input. The proposed method utilizes both top view and side view images of the packaging to determine its condition, a significant departure from the conventional single image input. Our approach incorporates 16 pre-trained model variants from EfficientNetV2, MobileNetV3, and ResNetV2 for transfer learning as feature extractors. The experimental findings demonstrate that the best model that leverages EfficientNetV2 variant achieves 100% accuracy and F1 score in terms of classification performance. However, the most optimal model in terms of classification performance and inference speed was the one that leveraged ResNetV2 variant. This model scored 95% accuracy and 95.24% F1 score, with an inference speed of 91 ms per prediction.
A comparative analysis of cervical cancer diagnosis using machine learning techniques Abdikadir Hussein Elmi; Abdijalil Abdullahi; Mohamed Ali Bare
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1010-1023

Abstract

This study undertakes a comprehensive analysis of cervical cancer diagnosis using machine learning (ML) techniques. We start by introducing the critical importance of early and accurate diagnosis of cervical cancer, a significant health issue globally. The objective of this research is to compare the effectiveness of three ML algorithms: K-nearest neighbors (KNN), linear support vector machine (SVM), and Naive Bayes classifier, in predicting biopsy results for cervical cancer. Our methodology involves utilizing a substantial dataset to train and test these algorithms, focusing on performance measures like accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve (AUC). The findings reveal that KNN demonstrates superior performance, with high precision, recall, accuracy, and F1 score, alongside a notable AUC. This suggests KNN's potential utility in clinical applications for cervical cancer prognosis. Meanwhile, linear SVM and Naive Bayes exhibit certain limitations, indicating a need for further optimization. This study highlights the promising role of ML in enhancing medical diagnostic processes, particularly in oncology.
High-performance InP/InGaAs heterojunction bipolar phototransistors for optoelectronic applications Jihane Ouchrif; Abdennaceur Baghdad; Aicha Sahel; Abdelmajid Badri
Indonesian Journal of Electrical Engineering and Computer Science Vol 32, No 1: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v32.i1.pp80-89

Abstract

Phototransistors are attractive devices for applications in optical fiber telecommunication systems. They are used for the detection of optical signals and the amplification of these signals. This paper presents an investigation of how the technological parameters of indium phosphide (InP)/indium gallium arsenide (InGaAs) heterojunction bipolar phototransistor can impact its responsivity at two wavelengths, 1310 nm and 1550 nm. Based on the results of this investigation, we proposed optimized structures for the studied phototransistor. In this work, we used the software technology computer aided-design (TCAD)-Silvaco to simulate the physical and the electrical behavior of the different structures. The proposed optimized phototransistors can be used for various optoelectronic applications.

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