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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 9,138 Documents
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Virtual inertia evaluation for frequency instability in renewable energy integration Kathad, Shilpa Keshubhai; Pandya, Dharmesh Jagdishchandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp380-388

Abstract

In recent trends, the increasing integration of renewable energy sources (RES) into grids has provides a transition in electricity generation and distribution in terms of frequency instability. Recently, the concept of virtual inertia (VI) has developed as a promising solution to minimize frequency instability in interconnected RES. Therefore, this research introduces VI evaluation technique to decrease the frequency instability. Advanced control algorithms are used to create VI, which simulates the stabilizing effect of traditional rotating mass in conventional power systems. The high penetration of RES based on power converters has suggestively decreased the VI which making them susceptible to frequency instability. This work recommends another use of VI control to further develop recurrence dependability of the connected power framework because of high entrance level of RESs. ∆f values differ between 17.4215 and 20.3621 with significant frequency variations due to conventional control. Equally, VI control exhibits a high level of efficiency in reducing frequency deviations; The ∆f values were consistently smaller between 0.0236 and 0.0369 than the conventional control. These findings signify the potential of VI control to improve frequency stability in power systems with RES.
Bibliometric analysis of model vehicle routing problem in logistics delivery Zuhanda, Muhammad Khahfi; Hartono, Hartono; Sidik Hasibuan, Samsul Abdul Rahman; Abdullah, Dahlan; Gio, Prana Ugiana; Caraka, Rezzy Eko
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp590-600

Abstract

This bibliometric analysis focuses on the vehicle routing problem (VRP) model in the field of logistics delivery. The study utilizes a comprehensive dataset of 2,000 VRP-related publications obtained from the Scopus database, spanning the years 2007 to 2023. Through the application of bibliometric methods, this research aims to uncover key insights regarding research trends, country contributions, and recent topics within the VRP research network. Various bibliometric indicators, including publication count, author productivity, relevant sources, institutional affiliation, and citation frequency, are employed to conduct the analysis. The findings shed light on the evolution and trajectory of VRP research, while also highlighting noteworthy countries and topics that have received significant attention. This study not only enhances the overall understanding of VRP but also serves as a foundation for future investigations aimed at enhancing the efficiency and effectiveness of logistics delivery.
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.
Efficient model for cotton plant health monitoring via YOLO-based disease prediction Pavate, Aruna; Kukreja, Swetta; Janrao, Surekha; Bankar, Sandip; Patil, Rohini; Bidve, Vijaykumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp164-178

Abstract

Protecting plants from diseases involves recognizing the symptoms and identifying practical, safe, and reasonable treatment methods. Holistic approaches based on particular times or seasons can reduce plant resistance and minimize tedious work. Technological advancements have led to the development of microscopic examinations and computational methods using machine learning techniques to detect diseases automatically and quickly using leaf images. This study builds the prediction model using EfficientNet and YOLO neural network architectures from computer vision. The development of a model that assists farmers in identifying cotton disease so that they use pesticides that may treat it further utilizes this concept. In the physical world, the input is accepted from many different sources, so observing the model’s output is necessary. This work concentrates on model response to the inputs from physical devices, and analysis shows that the monitoring varies the results. A novel convolutional neural network (CNN) based on the EfficientNet architectures and variations of YOLO architectures is used to classify and identify the objects in cotton leaf. The EfficientNetB4 yielded 100% accuracy for healthy leaf and powdery mild leaf classes, and YOLO v4 version with 96%, 98.3%, 99.2%, and 0.70 for precision, recall, mAP@0.5, mAP120.5:095 respectively. These results indicate that consequences vary in real-time per environmental parameters such as light effect and devices, and analysis shows that monitoring affects the results.
Elevating intelligent voice assistant chatbots with natural language processing, and OpenAI technologies Korade, Nilesh B.; Salunke, Mahendra B.; Bhosle, Amol A.; Asalkar, Gayatri G.; Lal, Bechoo; Kumbharkar, Prashant B.
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp507-517

Abstract

Businesses can offer support to customers outside of usual business hours and across time zones by employing chatbots, which can provide round-the-clock support. Chatbots can react to user inquiries quickly, reducing wait times and improving customer satisfaction. It becomes challenging for the chatbot to differentiate between two queries that users pose that carry the same meaning, making it harder for it to understand and react appropriately. The aim of this research is to develop a chatbot capable of understanding the semantic meaning of questions as well as recognizing various speech patterns, accents, and dialects to provide accurate responses. In this research, we have implemented a voice-enabled chatbot system where users can verbally pose questions, and the chatbot provides responses through voice assistance. The architecture incorporates several key components: a question-answer database, OpenAI embedding for semantic representation, and OpenAI text-to-speech (TTS) and speech-to-text (STT) for audio-to-text and text-to-audio conversion, respectively. Specifically, OpenAI embedding is utilized to encode questions and responses into vector representations, enabling efficient similarity calculations. Additionally, extreme gradient boosting (XGBoost) is trained on OpenAI embeddings to identify similarities between user queries and questions within the dataset. This framework allows for seamless interaction between users and the chatbot, leveraging state-of-the-art technologies in natural language processing (NLP) and speech recognition. The outcome demonstrates that the XGBoost model delivers excellent outcomes when it is trained on OpenAI embedding and tuned with the particle swarm optimizer (PSO). The OpenAI-generated embedding has good potential for capturing sentence similarity and provides excellent information for models trained on it.
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.
A new intensity-modulated radiation therapy with deep learning heart rate prediction framework for smart health monitoring Sivalingam, Saravanan Madderi; Thisin, Syed
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp300-314

Abstract

This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.
Transformer oil degradation detection system based on color scale analysis Hakim, Muhammad Fahmi; Prasojo, Rahman Azis; Duanaputri, Rohmanita; Wijaya, Bustani Hadi; Fidya Amaral, Hanifiyah Darna; Emzain, Zakki Fuadi
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp15-25

Abstract

The rise in power transformer load results in degradation of the condition of the transformer oil and ultimately a deficiency in the distribution of electrical energy. This degradation can be slowed down by reconditioning transformer oil based on oil color detection. This research aims to design, test and validate a transformer oil color testing system based on color sensor and microcontroller. To obtain an accurate system, tests were carried out on selecting the types of sensors, the color of the chamber walls, and the shapes of transformer oil sample vessel used. The oil color scale of the samples was determined visually according to the ASTMD1500, 2009 standard as a benchmark. The test results showed that the TCS3200 color sensor was able to detect the color of all transformer oil samples. White chamber wall and test tube as oil sample containers were chosen to increase system accuracy. Overall, the system is able to detect the color of transformer oil, convert to the ASTMD1500, 2009 standard transformer oil color scale, determine the condition of the transformer oil and conclude the level of transformer oil degradation according to CIGRE-761, 2019. Validation results showed the system had an accuracy level of 92.65%.
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.

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