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INDONESIA
Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Published by IAES Indonesia Section and Partners
ISSN : 20893272     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is a peer reviewed International Journal in English published four issues per year (March, June, September and December). The aim of Indonesian Journal of Electrical Engineering and Informatics (IJEEI) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the engineering of Telecommunication and Information Technology, Applied Computing & Computer, Instrumentation & Control, Electrical (Power), Electronics, and Informatics.
Arjuna Subject : -
Articles 783 Documents
 67   68   69   70   71 
Advanced Classification of Agricultural Plant Insects Using Deep Learning and Explainability Vo, Hoang-Tu; Thien, Nhon Nguyen; Mui, Kheo Chau; Tien, Phuc Pham; Le, Huan Lam; Phuc, Vuong Nguyen; Trung, Hieu Nguyen; Tan, Phuong Lam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6419

Abstract

This paper investigates the effectiveness of six pre-trained deep learning models to classify images of agricultural plant insects. We utilized the BAUInsectv2 dataset, which includes images from nine classes. Aphids, Armyworm, Beetle, Bollworm, Grasshopper, Mites, Mosquito, Sawfly, and Stem borer. The models, namely Xception, MobileNetV2, ResNet50, EfficientNetV2B3, ResNet101, and DenseNet121, are fine-tuned by transfer learning from ImageNet. This approach significantly reduces training time while improving classification accuracy. Our experiments reveal that each model reliably distinguishes between insect species even when faced with varying lighting conditions and diverse viewpoints. To further clarify how these models make predictions, we employ Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight critical regions in the images. The results demonstrate that each model focuses on unique biological features and offers clear explanations for its decisions. The research results contribute to demonstrating the potential of pre-trained deep learning architectures for agricultural monitoring and pest management, paving the way for promising future applications.
Benchmarking of OFDM Spectrum Exchange for Mobile Cognitive Radio Networks Marwanto, Arief; Kamilah, Sharifah; Satria, M. Haikal
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.4094

Abstract

The local spectrum sensing objective in spectrum sensing is to detect the PU's signal. The sensing node's (SN) capacity to detect the PU's signal is of paramount importance. However, it is presumed to be stationary in the majority of SN in cognitive radio networks. The detection performance on local observation is significantly influenced by the mobility of the PUs and SNs. The SNs' movement generates spatial diversity in the PU's signal observation. The signal's condition would fluctuate during the sensing process as a result of Doppler effect, spatial distance, velocity, movement, and geolocation information. Therefore, a benchmark is required to compare the primary user signal detection level of stationary and moving SNs from each sensing node. The performance results have demonstrated that static nodes with SCM are superior to conventional subcarrier mapping (SCM) methods in the case of a subcarrier mapping width of α = 2. Additionally, the quantization width is uniform. It has been determined that the performance disparity is substantial, ranging from 2 dB to 4 dB. The results indicate that the static nodes SCM have achieved acceptable performance detection at a low subcarrier detection threshold (SDT) value of 0 dB up to 5 dB. Conversely, the probability of conventional SCM detection is less than 1 of probability detection (PD) value at the same low SDT value. The detection probability (PD) of static nodes with SCM is satisfactory at an SDT value of 15 dB. Moreover, the probability begins to decline until 20 dB at an SDT value of 11.5 dB, a substantial decrease that is rendered negligible. In contrast to the new subcarrier mapping (N-SCM) method, which has a false alarm probability (PFA) of approximately 0 dB to 9.5 dB, conventional subcarrier mapping (SCM) has a high false alarm probability in mobility networks. Furthermore, it is evident that the PFA curves for the conventional SCM method are lower than those of other methods at low speeds, as they approach the null value at SDT 7.5 dB. The PFA curve for both methods is higher than other velocities by attaining a null value at 10 dB, in contrast to high velocity. In general, the mobility parameter has the potential to meet the detection performance and perform well in the false alarm probability of mobile spectrum exchange. Consequently, it could be employed to provide information on spectrum exchange in the future.
BCDNN: Enhancing CNN Model for Automatic Detection of Breast Cancer Using Histopathology Images Anumalla, Koushik; Kumar, G. Sunil  ; Vani, M. Sree; Rao, Kuncham Sreenivasa
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.5854

Abstract

The United Nations has identified health and well-being for all as one of its sustainable development goals. Research efforts in the healthcare domain worldwide are aligned with this goal. According to the World Health Organization (WHO), there has been an increasing incidence of breast cancer globally. The emergence of Artificial Intelligence (AI) has enabled learning-based approaches for diagnosing various ailments in the healthcare domain. Numerous efforts have been designed to efficiently diagnose breast cancer using deep learning algorithms, with the Convolutional Neural Network (CNN) being the widely used model due to its efficiency in processing medical images. However, CNN-based models may experience deteriorated performance without empirical studies to improve the underlying architecture. Motivated by this fact, our paper proposes a deep learning-based system for breast cancer diagnostic automation by enhancing a CNN model called the Breast Cancer Detection Neural Network (BCDNN). We also introduce an algorithm called Enhanced Deep Learning for Breast Cancer Detection (EDL-BCD), which leverages the enhanced deep learning model for better disease diagnosis performance. Our evaluation with a benchmark dataset comprising breast histopathology images shows that our suggested framework significantly outperforms state-of-the-art models, achieving an impressive accuracy of 97.99%. Therefore, the proposed system can be integrated with healthcare applications to assist in automatic screening by utilizing histopathology pictures to visualize breast cancer.
HybridPPI: A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction Reddy, Desidi Narsimha; Venkateswararao, Pinagadi; Vani, M. Sree; Pranathi, Vodapelli; Patil, Anitha
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6278

Abstract

Protein-protein interactions (PPIs) are key to cellular functions and disease mechanisms and are crucial for drug discovery and systems biology. Though experimental approaches, including yeast two-hybrid systems, provide informative discoveries, they are time-consuming, costly, and frequently yield significant false-positive rates. Newer computational tools, including DeepPPI and PIPR, have demonstrated their potential, but their reliance on single-modal features or specific machine-learning models limits their generalization and robustness. These limitations highlight the need for an enhanced framework that assimilates different types of features while integrating a diverse array of machine learning models to exploit the strengths offered by each model class. In this paper, we present a hybrid machine learning framework, HybridPPI, to effectively incorporate the power of sequence-based, structure-based, and network-based features based on wellknown ensemble learning techniques to predict PPIs. Our proposed algorithm is a stacking ensemble of multiple models (Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM)), with Gradient Boosting as the metamodel. Results show that HybridPPI (94.5% accuracy, 95.2% precision, and Area Under Curve of 0.97) outperforms the most advanced methods, indicating its robustness for PPI prediction. This scalable and generalizable framework can accommodate various biological applications. HybridPPI overcomes significant shortcomings of current methodologies and contributes to biological discovery. 
Wn-Based Skin Cancer Lesion Segmentation of Melanoma Using Deep Learning Methods M, Jayasree; K, Kavin Kumar; Chandrasekaran, Gokul; M, Saranya; S, Gopinath; T, Rajasekaran
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6151

Abstract

The incidence rate of skin cancer, particularly malignant melanoma, has risen to high levels during the last decades. The biopsy method used for cancer treatment was found to be a painful and time-consuming one. Also, laboratory sampling of skin cancer leads to the spread of lesions to other body parts. Due to the different colours and shapes of the skin, segmentation and classification of melanoma are more challenging to analyze. An automatic method of dermoscopic skin lesion detection will be introduced. Recognizing the skin lesions at an early stage is essential for effective treatment. Proposed an efficient skin cancer image segmentation method using Fixed-Grid Wavelet Network (FGWN) and developed a novel classification method using deep learning techniques. FGWNs constitute R, G and B values of three inputs, a hidden layer and an output. Input skin cancer image is segmented, and the exact boundary is determined accordingly. The features of the segmented images were extracted using the Orthogonal Least Squares (OLS) algorithm. The AlexNet model was first used to classify pictures of melanoma cancer. Next, ResNet-50 and Ordinary Convolutional Neural Networks (CNN) was deployed. Wavelet Network (WN)-Based segmentation achieved an accuracy of 99.78% in detecting skin cancer lesion boundaries. Ordinary CNN shows an accuracy of 93.37% for 100 epochs. ResNet-50 models show 88.37% accuracy for melanoma classification. The number of training epochs and the volume of training data both impact accuracy. Deep learning algorithms can significantly improve categorization efficiency.
DCDNet: A Deep Learning Framework for Automated Detection and Localization of Dental Caries Using Oral Imagery Reddy, Desidi Narsimha; Venkateswararao, Pinagadi; Patil, Anitha; Srikanth, Geedikanti; Chinnareddy, Varalakshmi
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6245

Abstract

Dental caries is a common oral health condition that requires early diagnosis and identification for effective intervention. Existing deep models, such as Faster R-CNN, YOLOv3, SSD, or RetinaNet, exhibit great effectiveness in generic medical imaging; however, they struggle to precisely and explicitly handle localization in complex dental radiographs. In this paper, we propose DCDNet, a convolutional neural network architecture specifically designed for the detection and segmentation of dental caries in oral X-ray images. However, such deep learning methods currently lack strong generalization due to imbalanced training data, limited lesion-localization ability, and noninterpretable features, which hamper their utility for large-scale clinical evaluation. In addition, most models overlook the severity distinction between classes, which is less ideal for the entire diagnosis and treatment planning process. DCDNet was trained and tested on the UFBA UESC Dental Image Dataset, which comprises over 1,500 labeled grayscale dental radiographic images. The proposed network incorporates multiscale feature extraction, residual connections, and non-maximum suppression (NMS) for more accurate classification and bounding box prediction. Data augmentation techniques were used to increase generalization. The model was evaluated based on accuracy, precision, recall, and F1-score, and compared with ResNet50, VGG16, AlexNet, Faster R-CNN, YOLOv3, SSD, and RetinaNet in terms of accuracy. DCDNet achieved excellent performance in all its performance indices, with precision at 97.23%, recall at 97.02%, F1-score at 97.12%, and overall accuracy at 97.61%. Experiments demonstrate that the proposed DCDNet surpasses all the baselines and state-of-the-art methods by a significant margin. Ablation experiments validated the importance of residual connections, NMS, and data augmentation for performance improvement. DCDNet represents a significant step toward automatic dental diagnosis, having successfully detected and localized carious lesions in X-ray images. Its design overcomes the drawbacks of previous models and is a ready option for integration into clinical routine.
Using The Combined Model Between MobileNetV2 and EfficientNetB0 to Classify Brain Tumors Based on MRI Images Thien, Nhon Nguyen; Mui, Kheo Chau; Vo, Hoang-Tu; Tien, Phuc Pham; Le, Huan Lam
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6197

Abstract

Brain tumors are extremely dangerous to one's health. If unchecked cell proliferation is not identified and treated promptly, it can lead to mortality, raise intracranial pressure, and endanger lifespan. To remove the tumor and lengthen the patient's life, early illness identification and drug administration are essential. In this research paper, we aim to improve the effectiveness of magnetic resonance imaging (MRI) equipment to identify cancerous brain tumour cells. It helps experts identify diseases faster. We classify brain tumour cells based on an image set of 3264 images with effective classification models such as ResNet50, InceptionV3, VGG19, EfficientNetB7, DenseNet201, MobileNetV2, Xception, etc. Besides, we also proposed two combined models: pooling (Xception + ResNet50) and pooling (MobileNetV2 + EfficientNetB0) to evaluate the effectiveness and found that the pooling model (MobileNetV2 + EfficientNetB0) gives the highest result, with 100% for the training set, 98% for the valid set, and 78% for the test set. We continued to improve the model by randomly re-dividing the data set with a Train-Valid-Test ratio of 60:20:20 and obtained an increased F1-score of 97%. We continued to improve the model again using the data augmentation techniques to create a larger data set, and the results far exceeded expectations with an F1-score of almost 100% for all classes. Based on the results, we found that combining MobileNetV2 with EfficientNetB0 is suitable for detecting brain tumour cancer cells. Aids in the early detection of dangerous cancers before they spread and endanger human health.
Exploration of Analyte Electrolyticity Using Multi-SRR-Hexagonal DNG Metamaterials and ZnO Thin Films Defrianto, Defrianto; Saktioto, Saktioto; Rini, Ari Sulistyo; Syamsudhuha, Syamsudhuha; Anita, Sofia; Soerbakti, Yan
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6085

Abstract

Advanced engineered metamaterials (MTMs) significantly contribute to modern technological advancements, particularly through hybridization with semiconductor materials like zinc oxide (ZnO), which enhance sensor sensitivity and performance. This study aims to investigate the optical properties of hybrid MTMs and develop a novel sensor medium capable of detecting early electrolytic behaviors of analytes. Utilizing the finite-difference time-domain (FDTD) method, the sensor was designed, characterized, and integrated, featuring a hexagonal multi-cell split ring resonator (SRR) structure coated with a 200-nm ZnO thin film. The geometry of the SRR MTM was optimized using a modified Nicolson-Ross-Weir electromagnetic field function method. Results demonstrate that the MTM exhibits double-negative optical characteristics with a performance index reaching 102. Moreover, the sensor presents dual-band resonance frequencies for reflection and transmission attributed to the combination of the multi-SRR hexagonal design and ZnO coating, with an absorption peak at 8.71 GHz. Testing the sensor in varying electrolytic conditions, such as seawater, revealed a measurable reduction in resonance depth and increased sensitivity, characterized by a frequency shift of 5.25 MHz per 0.7 S/m increment in electrical conductivity. These findings highlight the MTM sensor's potential as an effective tool for enhancing spectrum readout accuracy and sensitivity in analyte detection applications.
Early Mental Health Detection with Machine Learning : A Practical Approach to Model Development and Implementation Hermawan, Latius; Syakurah, Rizma Adlia; Meilinda, Meilinda; Stiawan, Deris; Negara, Edi Surya; Ramayanti, Indri; Fahmi, Muhammad; Rizqie, Muhammad Qurhanul; Hermanto, Dedy
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6111

Abstract

Academic pressures, lifestyle changes, and socio-economic factors significantly impact mental health, a critical determinant of academic success and well-being. Early detection and intervention are crucial to mitigate severe outcomes like academic underperformance and suicidal tendencies. Leveraging tools like the DASS-42, this study examines mental health patterns using Support Vector Machine (SVM) models, achieving accuracies of 88% for depression, 71% for stress, and 57% for anxiety. While the model excels in identifying "Normal" cases, its performance for "Mild," "Moderate," and "Severe" cases highlights limitations due to class imbalance and feature representation. The findings reveal that anxiety is the most volatile and severe condition, with peaks in 2018 and 2022, while stress remains manageable and depression moderately stable. Gender and program-specific differences emphasize the need for tailored interventions. Addressing challenges related to data quality, algorithmic transparency, and ethical concerns is essential for real-world applications. This study highlights the potential of machine learning in early detection and intervention for mental health issues. Future research should explore advanced feature engineering techniques and develop more interpretable models to enhance clinical decision-making.
Electrocardiogram Waveforms Diagnosis based on Wavelet Representation and SqueezeNet Model Mohammed Merza, Ahmed; Sim, Hussein Tami; Abd Zaid Qudr, Lateef
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.5529

Abstract

AArrhythmia is an irregular in a person's beating heart that can happen occasionally. Heart rhythm problems can have disastrous results and seriously endanger health. Visually analyzing ECG data might be complex due to its large amount of information. Designing an automated method to assess the massive amount of ECG data is crucial. This research shows continuous wavelet transform (CWT) and deep learning strategies to automate detection and classification processes to examine three different ECG signals: congestive heart failure (CHF), normal sinus rhythm (NSR), and arrhythmia (ARR). CWT converts ECG signals into scalogram images for noise reduction and feature extraction. In deep learning, the modified SqueezeNet is employed to recognize the output of CWT, which is produced by the input of the ECG data. The proposed technique achieved 83.3%, 100%, and 94.7% accuracy in detecting CHF, NSR, and ARR. A comprehensive approach for classifying arrhythmias has been proposed, in which scalogram pictures of ECG waves are trained using the SqueezeNet model. The outcomes are superior to other current techniques and will significantly reduce wrong diagnoses

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