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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 66 Documents
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Search results for , issue "Vol 37, No 1: January 2025" : 66 Documents clear
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.
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.
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%.
Application of data mining for diagnosis of ENT diseases using the Naïve Bayes method with genetic algorithm feature selection Wanti, Linda Perdana; Adi Prasetya, Nur Wachid; Awaludin, Ihza; Aditya Saputra, Muhammad Bintang; Furi, Syamaidzar Nadifa; Dwi Kumara, Dimas Maulana
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.pp398-405

Abstract

Ear, nose, and throat (ENT) disease is a disorder that occurs in the eustachian tube in one of the organs, be it the ear, nose, or throat. Early signs of ENT disease include sore throat, painful swallowing, swollen and red tonsils, runny nose, nosebleeds, blocked nose, discharge from the ears, and others. To determine the diagnosis, it is necessary to carry out a physical examination of the ears, nose, and throat as recommended by an expert, namely an ENT doctor. The research carried out was implementing data mining for the diagnosis of ENT diseases using the Naïve Bayes (NB) method. This method was chosen because it can increase the accuracy, efficiency, and accessibility of health services and is also easy to understand and apply to classify ENT disease symptom data. The NB method was used to build an ENT diagnosis classification model and the model performance was evaluated using accuracy, precision, and recall metrics. To increase the accuracy of the NB algorithm predictions, feature selection using a genetic algorithm can be used. Genetic algorithms can help select the most relevant and significant features, improving the accuracy of NB models by eliminating irrelevant or noisy features. By applying this method, predictions for ENT diseases can be produced with an accuracy of 95.67%.
Spread of harmful substances in the atmosphere of industrial cities of Kazakhstan: modeling and data refinement Temirbekov, Nurlan; Tamabay, Dinara; Tanashova, Moldir
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.pp636-647

Abstract

In Kazakhstan, air pollution in industrial cities poses a significant challenge that requires urgent attention. This study investigates the dispersion of harmful pollutants in the air across nine prominent industrial cities in Kazakhstan. The research involves modeling the emissions from major pollution sources for each city, which provides a comprehensive view of how these substances spread through the atmosphere. The study also examines the distribution patterns of these pollutants to gauge their concentration levels in each urban area. Additionally, it addresses the inverse problem of data assimilation from automated monitoring stations (AMS), aiming to refine the information on pollution sources. By utilizing the conjugate equations method, the study successfully converged to an accurate solution. Detailed visualizations for Almaty, Ust-Kamenogorsk, and Pavlodar illustrate the pollution dynamics and pinpoint the most affected regions. These findings are crucial for formulating strategies to mitigate the adverse effects of industrial emissions on both the environment and public health.
Enhancing hypertension prediction: a hybrid machine learning optimization approach Aouragh, Abd Allah; Bahaj, Mohamed; Toufik, Fouad
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.pp347-355

Abstract

Early identification of hypertension is crucial to prevent its serious complications, which can lead to devastating health effects by threatening lifestyle quality and significantly increasing premature mortality. This study aims to evaluate the effectiveness of machine learning techniques in predicting the presence of hypertension from an unbalanced dataset consisting of 4,363 records and 35 features. To balance the dataset, we employed the synthetic minority over-sampling technique (SMOTE) algorithm. In addition, to select the most relevant features, we used ant colony optimization. Next, we applied various algorithms, including logistic regression (LR), K-nearest neighbors (KNNs), support vector machine (SVM), extra trees (ETs), and AdaBoost (AB). We also evaluated the optimization of hyperparameters using two methods: Bayesian optimization (BO) and particle swarm optimization (PSO). The results reveal that the combination of AB with BO demonstrated superior performance, with an accuracy of 97.60%, a recall of 98.93%, and a precision of 98.59%. This research emphasizes the potential of machine learning techniques for anticipating hypertension and highlights the importance of optimization techniques in improving predictive models’ performance.
Convolutional neural network-based strategies for efficient content-based image retrieval Kamatchi, Chinnathambi; Rajendran, Rathiya; Nagarajan, Kopperundevi; Palanisamy, Brinda; Jeyabalan, Deepika; Paperananthamurugesan, Rama Subramanian
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.pp551-559

Abstract

Recent years have seen a meteoric rise in the usage of enormous image databases due to advancements in multimedia technologies. One of the most critical technologies for image processing nowadays is image retrieval. This study uses convolutional neural networks (CNNs) for content-based image retrieval (CBIR). With the ever-growing number of digital photos, practical methods for retrieving these images are crucial. CNNs are incredibly efficient in many computer vision applications. Improving the efficacy and precision of image retrieval systems is the primary goal of our research into using deep learning. The paper starts with a thorough analysis of the current state of CBIR methods and the difficulties they face. Afterwards, it explores CNN’s design and operation, focusing on CNN’s capacity to learn hierarchical features from images autonomously. This paper also looks at how the model performs when it alters its hyperparameters, transfer learning techniques, and CNN topologies. The insights obtained from these experiments enhance the comprehension of the elements impacting CNN effectiveness in CBIR. Finally, our study shows that CNNs can change the game for image search by transforming CBIR systems. This research adds to the expanding body of information about using cutting-edge deep learning algorithms to make image retrieval more efficient and accurate.

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