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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 65 Documents
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Search results for , issue "Vol 37, No 2: February 2025" : 65 Documents clear
Optimizing dynamic response and stability of pressure-controlled swash plate type axial piston pump Verma, Vivek; Kumar, Sachin; Anand, Apurva
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp771-780

Abstract

The main objective of the paper is to explore the role of lead-lag compensators in improving the performance of control systems for variable delivery hydraulic axial piston pumps (VAPP). These compensators offer a range of benefits, including stability enhancement, transient response optimization, frequency response modification, disturbance rejection, and robustness improvement. A mathematical model of the hydro-mechanical system is developed, and the transfer function for the dynamic system is established. The simulation of the model with lead-lag compensator significantly enhanced the phase margin to 55.70 and gain margin to 12.3 dB ensuring robust control for pressure-controlled VAPP, whereas the uncompensated system is marginally stable. The compensated system exhibits better transient and steady-state response. The optimized lead-lag compensated system achieves a maximum percentage overshoot of 12.1% and a settling time of 1.95 sec. This is a substantial improvement compared to the uncompensated system with a maximum % overshoot of 20.5% and a settling time of 2.39 sec. The improved response tends to induce greater damping (ζ) in the compensated system from 0.015 to 0.108 and increases leakage coefficient (K) from 3.38×10-12 m3/Pa.s to 24.34×10-12 m3/Pa.s. Optimized lag-lead compensator ensures stability, responsiveness adapting effectively to dynamic operating conditions of VAPP for aerospace application.
Hydrophobicity signal analysis for robust SARS-CoV-2 classification Jamhuri, Mohammad; Irawan, Mohammad Isa; Mukhlash, Imam; Tri Puspaningsih, Ni Nyoman
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1294-1305

Abstract

Rapid and accurate classification of viral pathogens is critical for effective public health interventions. This study introduces a novel approach using convolutional neural networks (CNN) to classify SARS-CoV-2 and non-SARS-CoV-2 viruses via hydrophobicity signal derived from DNA sequences. Conventional machine learning methods grapple with the variability of viral genetic material, requiring fixed-length sequences and extensive preprocessing. The proposed method transforms genetic sequences into image-based representations, enabling CNNs to handle complexity and variability without these constraints. The dataset includes 8,143 DNA sequences from seven coronaviruses, translated into amino acid sequences and evaluated for hydrophobicity. Experimental results demonstrate that the CNN model achieves superior performance, with an accuracy of over 99.84% in the classification task. The model also performs well with extended sequence lengths, showcasing robustness and adaptability. Compared to previous studies, this method offers higher accuracy and computational efficiency, providing a reliable solution for rapid virus detection with potential applications in bioinformatics and clinical settings.
Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm Sangeetha, Velu; Vaneeta, Maniyambadi; Mamatha, Arjuna; Shoba, Muniya; Ramamurthy Deepa, Sugatur; Sujatha, Velusamy; Sujatha, Shanmugam
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp849-858

Abstract

Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.
Navigating the smart contract threat landscape: a systematic review Ibekwe, Unyime Ufok; Mbanaso, Uche M.; Nnanna, Nwojo Agwu; Ibrahim, Umar Adam
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1209-1224

Abstract

Smart contracts have emerged as a transformative technology within the blockchain ecosystem, facilitating the automated and trustless execution of agreements. Their adoption spans diverse sectors such as education, agriculture, healthcare, government, real estate, transportation, supply chain, and global initiatives like Central Bank Digital Currencies (CBDCs). However, the security of smart contracts has become a significant concern, as vulnerabilities in their design and implementation can lead to severe consequences such as financial losses and system failures. This systematic review consolidates findings from 78 selected research articles, identifying key vulnerabilities affecting smart contracts and categorizing them into a taxonomy encompassing code-level, environment-dependent, and user-related vulnerabilities. It also examines the threats that exploit these vulnerabilities and the most effective detection techniques. The domain-based classification presented in this review aims to assist researchers, software engineers, and developers in identifying and mitigating significant security flaws related to the design, implementation, and deployment of smart contracts. A comprehensive understanding of these issues is essential for enhancing the security and reliability of the blockchain ecosystem, ultimately fostering the development of more secure and robust decentralized applications for end users.
Enhancing malware detection capabilities using deep learning with advanced hyperparameter tuning El Mouhtadi, Walid; Maleh, Yassine; Mounir, Soufyane
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp985-994

Abstract

As the threat landscape evolves with sophisticated malware and advanced persistent threats (APTs), the need for effective detection solutions increases. Traditional methods, such as signature-based and heuristic analysis, struggle to keep up with rapidly changing malicious activities. While machine learning offers a promising approach, it often falls short due to the manual extraction and selection of features, leading to time-consuming and error-prone processes. This research introduces a novel malware detection solution leveraging deep learning and focusing on portable executable (PE) file analysis to address these weaknesses. By customizing the hyperparameters of artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN), the proposed approach enhances detection capabilities. The primary objective is to overcome the limitations of traditional and machine learning methods by tailoring these deep learning algorithms. The methodology includes a comparative study to demonstrate the advantages of the customized approach over conventional methods. Key findings reveal the proposed solution’s superior performance, accuracy, and adaptability in combating evolving cyber threats. This research contributes to the development of robust and adaptive malware detection solutions.
Implementation of augmented reality as a revolutionary approach in computer stores Gunawan, Dedi; Mutia Dawis, Aisyah; Setiawan, Ismail; Permatahati, Ita; Ardhani, Rahmad; Setiyanto, Sigit
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp913-927

Abstract

The adoption of augmented reality (AR) spans various fields, from education to business. Currently, many businesses utilize AR to boost customer engagement and enhance product understanding. This research focuses on developing and examining an AR application on a Solo computer store’s website to improve customer engagement. Results indicate that AR significantly enhances the shopping experience, deepens product comprehension, and increases website interactions. Features like 3D product visualization and detailed information enable customers to make more informed purchasing decisions. A questionnaire with 25 respondents revealed a high acceptance rate of the AR application, averaging 92%. Additionally, AR was shown to increase customer engagement, potentially boosting sales by up to 35%, reducing operating costs by 20%, and enhancing productivity by 15%. The study also found differing preferences across age groups: older respondents (40-70 years) favored traditional website features without AR and were less comfortable with markerless technology, whereas younger consumers (18-39 years) were more attracted to AR-enhanced websites. These insights offer valuable guidance for the Solo computer store to craft innovative marketing strategies tailored to the diverse preferences and needs of their customers.
Efficient deep learning approach for enhancing plant leaf disease classification Belmir, Meroua; Difallah, Wafa; Ghazli, Abdelkader
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1112-1120

Abstract

The widespread occurrence of plant diseases is a major factor in the reduction of agricultural output, affecting both crop quality and quantity. These diseases typically begin on the leaves, influenced by alterations in plant structure and growing techniques, and can eventually spread over the entire plant. This results in a notable decrease in crop variety and yield. Successfully managing these diseases depends on accurately classifying and detecting leaf infections early, which is essential for controlling their spread and ensuring healthy plant growth. To address these challenges, this paper introduces an efficient approach for detecting plant leaf diseases. A concatenation of pre-trained convolutional neural networks (CNN) for enhanced plant leaf disease using transfer learning technique is implemented, with a specific focus on accurate early detection, utilizing the comprehensive new plant diseases dataset. The combined residual network-50 (ResNet-50) with densely connected convolutional network-121 (DenseNet-121) architecture aims to provide an efficient and reliable solution to these critical agricultural concerns. Various evaluation metrics were utilized to evaluate the robustness of the proposed hybrid model. The proposed ResNet-50 with the DenseNet-121 hybrid model achieved a rate of accuracy of 99.66%.
Performance analysis of 10 machine learning models in lung cancer prediction Zapata-Paulini, Joselyn; Cabanillas-Carbonell, Michael
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1352-1364

Abstract

Lung cancer is one of the diseases with the highest incidence and mortality in the world. Machine learning (ML) models can play an important role in the early detection of this disease. This study aims to identify the ML algorithm that has the best performance in predicting lung cancer. The algorithms that were contrasted were logistic regression (LR), decision tree (DT), k-nearest neighbors (KNN), gaussian Naive Bayes (GNB), multinomial Naive Bayes (MNB), support vector classifier (SVC), random forest (RF), extreme gradient boosting (XGBoost), multilayer perceptron (MLP) and gradient boosting (GB). The dataset used was provided by Kaggle, with a total of 309 records and 16 attributes. The study was developed in several phases, such as the description of the ML models and the analysis of the dataset. In addition, the contrast of the models was performed under the metrics of specificity, sensitivity, F1 count, accuracy, and precision. The results showed that the SVC, RF, MLP, and GB models obtained the best performance metrics, achieving 98% accuracy, 98% precision, and 98% sensitivity.
A review and bibliometric analysis of traceability system development in the agricultural and food sector in Indonesia Siregar, Yusnan Hasani; Purwandoko, Pradeka Brilyan; Harsonowati, Wiwiek; Nanda, Muhammad Achirul; Tjahjohutomo, Rudy; Budiman, Diana Atma; Rahmawati, Laila; Susanti, Novita Dwi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1064-1076

Abstract

Several technologies and methods for traceability systems in the agriculture and food sectors have developed rapidly in recent decades. There has been an increase in traceability system research in many developing countries, including Indonesia. Our review collects data from the Scopus database to study the development and dynamics of research on traceability systems and to identify emerging technological trends in the field. This paper uses bibliometric analysis by VOSviewer to find out studies regarding traceability. Our findings reveal traceability system research in Indonesia encompasses 1,264 documents within the Scopus database from 1998 to 2022. The number of studies on traceability systems has increased significantly after 2016. Most scholarly articles on traceability technology are disseminated as conference proceedings. These traceability systems have been established and are widely adopted to ensure the quality and safety of agricultural and food products, monitor species diversity, and oversee environmental parameters. The objective of the user influences the development of the traceability system. Technologies such as deoxyribonucleic acid (DNA) barcoding, unmanned aerial vehicles (UAVs), satellites, wireless sensor networks (WSNs), blockchain, product tagging, spectroscopy, and smart packaging rapidly advance to enhance traceability capabilities.
Gamification in work-based learning in vocational education to support students' coding abilities Jalinus, Nizwardi; Ganefri, Ganefri; Syahril, Syahril; Zaus, Mahesi Agni; Islami, Syaiful
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1262-1273

Abstract

This article studied the integration of gamification in work-based learning within vocational education as a means to support students' coding abilities. By applying game mechanics such as points, badges, leaderboards, and challenges, we aimed to motivate and engage students in coding activities that mirror real-world industry practices. The inclusion of gamified elements into the curriculum was designed to make the learning process more interactive, fostering a competitive yet collaborative environment that enhances students' interest and perseverance in coding tasks. This research employed a quasi-experimental design with pre-test and post-test measures to assess the impact of gamification on coding proficiency, comparing the outcomes of students participating in gamified learning environments with those in traditional settings. The findings indicate a significant improvement in the coding skills of students exposed to gamified work-based learning, suggesting that gamification can serve as an effective pedagogical tool in vocational education, better preparing students for industry demands.

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