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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 38, No 2: May 2025" : 65 Documents clear
Temperature-dependent based optimal reactive power dispatch by chaotic equilibrium optimization algorithm Dao, Minh Trung; Vo, Ngoc Dieu
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp698-712

Abstract

The optimal reactive power dispatch (ORPD) problem is considered as an important aspect in power system operation of the reactive power, which is vital to maintain network voltage within desirable limit for system reliability. In conventional ORPD problem, the resistance of components in power systems is considered to be independent to their temperature variations. Actually, there is a correlation between the branch resistance and temperature, thus the temperature should be taken into account when performing power flow analysis to improve the accuracy in the calculation of the power flow and power loss on branches. This paper proposes a new chaotic equilibrium optimization (CEO) method to solve the temperature-dependent based optimal reactive power dispatch (TDORPD) problem in power systems by optimizing the reactive power loss and voltage deviation. The proposed CEO algorithm is implemented for the conventional ORPD and TDORPD problems on the benchmark IEEE 30 bus testing network. Moreover, the effects of temperature variations on the considered TDORPD problem are also considered. The obtained results have demonstrated a better performance of the proposed CEO algorithm compared to the original EO and other methods in the literature review for the problem in terms of the solution quality, which confirms its efficacy to effectively resolve the ORPD and TDORPD problem.
Enhancing accessibility: deep learning-based image description for individuals with visual impairments Shah, Nidhi B.; Ganatra, Amit P.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1051-1060

Abstract

Technological developments in artificial intelligence, namely in the area of deep learning, have created new avenues for enhancing accessibility for those with visual impairments. In order to improve the capacity of people who are blind or visually impaired to understand and interact with visual material, this research investigates the creation and use of deep learning-based image description systems. We provide a comprehensive method that uses recurrent neural networks (RNNs) to generate natural language descriptions and convolutional neural networks (CNNs) and Autoencoders for extracting picture features. Our technology automatically creates comprehensive, context-aware descriptions of photographs by incorporating these models, giving users a better knowledge of their surroundings. We show the accuracy and reliability of the system on a wide range of photos through comprehensive testing. According to our research, deep learning-based picture description systems and converting the description in audio and making a promise to empower people who are visually impaired and foster diversity in the digital sphere.
Credit card fraud detection using CNN and LSTM Upadhyay, Nishant; Bansal, Nidhi; Rastogi, Divya; Chaturvedi, Rekha; Asim, Mohammad; Malik, Suraj; Jayant, Khel Prakash; Vajpayee, Abhay Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1402-1410

Abstract

Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
Acute lymphoblastic leukemia diagnosis and subtype segmentation in blood smears using CNN and U-Net Reza, Hamim; Tareq, Nazrul Islam; Rabbi, M M Fazle; Tanim, Sharia Arfin; Rudro, Rifat Al Mamun; Nur, Kamruddin
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp950-959

Abstract

Acute lymphoblastic leukaemia (ALL) is a severe disease requiring invasive, expensive, and time-consuming diagnostic tests for definitive diagnosis. Initial diagnosis using blood smear pictures (BSP) is crucial but challenging due to the similar indications and symptoms of ALL, often leading to misdiagnoses. This study presents a custom approach using Convolutional Neural Networks (CNNs) to detect all cases and categorize subtypes. Utilizing publicly available databases, the study includes 3562 blood smear images from 89 patients. The innovative combination of U-Net for segmentation and various CNN architectures (U-Net, MobileNetV2, InceptionV3, ResNet50, NASNet) for feature extraction, with DenseNet201 being the most effective, forms the core of this method. The U-Net model achieved a segmentation accuracy of 98% by recognizing patterns within blood smear images. Following segmentation, CNN architectures extracted high-level features, with DenseNet201 proving the most effective in diagnostic and classification tasks. Our proposed custom CNN model achieved a test accuracy of 98%, with a training accuracy of 99.31% and validation accuracy of 97.09%. This approach enables an accurate distinction between ALL and non-pathologic cases.
Plant leaf disease detection and classification using artificial intelligence techniques: a review R, Kusuma; Rajkumar, R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1308-1323

Abstract

Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Calibration of phased array antenna with the minimum point finding method of the array factor Luong, Nguyen Xuan; Nhan, Nguyen Trong; Thanh, Tran Van; Thanh Thuy, Dang Thi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp854-864

Abstract

The problem of phased array antenna calibration with the minimum point finding method of the array factor is investigated. A mathematical model of the minimum point finding method is presented. Then, the proposed method is applied to the phased array antenna and compared with the traditional rotating-element electric-field vectors method. Experimental verification of the mathematical model of the proposed method showed the following: the minimum point finding method determines the phase shift more accurately than the maximum point finding method of the array factor; the proposed method showed a better detection range per phase change corresponding to a 35 dB higher resolution. The error ranges of the minimum and the maximum point finding methods were 50 and 700 , respectively. The peak of the combined beam when using the minimum point finding method is higher than the maximum point finding method which is 3.7 ... 4.1 dB. One can use the research results in large-scale phased array antenna calibration systems during the production phase.
Artificial intelligence approaches for cardiovascular disease prediction: a systematic review Hammadi, Jasim Faraj; Abdul Latif, Aliza Binti; Che Cob, Zaihisma Binti
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1208-1218

Abstract

Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Internet of things meteorological station for climate monitoring and crop optimization in Carabayllo-Perú Rumiche-Cardenas, Jeremy Jared; Figueroa-Guevara, Axel Walter; Gamarra-Pahuacho, Deyvis Jhosmar; Quiroz-Grados, Josue Daniel; Segovia-Ojeda, Jamil; Cabana-Cáceres, Maritza; Castro-Vargas, Cristian
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp755-766

Abstract

In the agricultural sector, monitoring environmental variables such as temperature, humidity, and atmospheric pressure is crucial for efficient and sustainable agriculture. However, conventional monitoring systems are expensive and need more autonomy, making their implementation difficult in small- and medium-scale agricultural operations. This study presents the design, implementation, and evaluation Internet of things (IoT)-based autonomous for watch remote critical climate variables in the Carabayllo region, Peru. The system uses a data acquisition, processing, and transmission architecture based on the ESP32 microcontroller, DHT22 sensors for measure climatic aspects, BMP180 for detection barometric, and the ThingSpeak cloud platform for data storage and visualization. Results show that the proposed system achieves accuracy comparable to commercial weather stations, making it accessible to small farmers. The implementation demonstrated the system’s ability to detect feasible local microclimates to monitor and predict weather patterns for proper crop growth. This approach enables farmers to monitor conditions in real time, receive early alerts on adverse weather events, and optimize agricultural practices such as irrigation and fertilization. The study concludes that the proposed IoT weather station represents a viable and cost-effective solution to improve agricultural decision-making in developing regions, potentially contributing to increasing crops.
Android malware detection through opcode sequences using deep learning LSTM and GRU networks Lakshmanarao, Annemneedi; Mantena, Jeevana Sujitha; Thota, Krishna Kishore; Chandaka, Pavan Sathish; Murali Krishna, Chinta Venkata; Jetty, Madhan Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1106-1114

Abstract

Android malware detection was a complex task due to the intricate structure of Android applications, which consisted of numerous Java methods and classes. Effective detection required the extraction of meaningful features and the application of advanced machine learning (ML) or deep learning (DL) algorithms. This paper presented a novel approach to detecting Android malware by leveraging opcode sequences extracted from Android applications. These opcode sequences, which differed between malicious and benign apps, formed the basis of the detection model. The methodology involved extracting opcode sequences from decompiled Android APK files using the “Androguard” tool and applying recurrent neural networks (RNN) with long short-term memory (LSTM), Bi-LSTM, and gated recurrent unit (GRU) architectures to classify the apps as either malware or benign. The combination of these advanced DL techniques allowed for capturing temporal dependencies in opcode sequences, resulting in a significant improvement in detection capabilities. This work underscored the potential of using opcode sequences in conjunction with RNN, LSTM, and GRU for robust and accurate malware detection, while also highlighting the importance of further exploring additional features for comprehensive classification.
Data mining and cardiac health: predicting heart attack risks Paucar, Inoc Rubio; Andrade-Arenas, Laberiano
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1010-1023

Abstract

In a context where heart attacks continue to be a global health concern, the lack of precision in predicting who is at higher risk poses a critical challenge due to the variability of risk factors and complex interactions among them. The research aims to develop predictive models for heart attack risks using data mining techniques, employing the knowledge discovery in databases methodology (KDD) and the k-means algorithm with RapidMiner studio. The primary objective is to identify patterns and risk profiles, allowing for early identification of at-risk individuals, considering factors like obesity, diabetes, alcoholism, and stress, to reduce preventable deaths and improve cardiac healthcare. This innovative approach combines cardiac health, data mining, and KDD methodology to address the challenge of predicting heart attack risks and has the potential to enhance medical care and save lives. The predominant results obtained were that cluster 1 with a fraction of 0.312 and a percentage of 31.2% of the attribute diabetes was one of the most prevalent causes of cardiac risk. Finally, the research concluded that people with diabetes are more likely to have cardiac risk associated with dietary factors or consumption of other substances.

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