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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science
Wiharto Wiharto
Sebelas Maret University
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Squeeze-excitation half U-Net and synthetic minority oversampling technique oversampling for papilledema image classification Wiharto, Wiharto; Syaifuddin, Angga Exca Pradipta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1410-1419

Abstract

The emergence of various convolutional neural networks (CNN) architectures indicates progress in the computer vision field. However, most of the architectures have large parameters, which tends to increase the computational cost of the training process. Additionaly, imbalanced data sources are often encountered, causing the model to overfit. The aim of this study is to evaluate a new method to classify retinal fundus images from imbalanced data into the corresponding classes by using fewer parameters than the previous method. To achieve this, squeeze-excitation half U-Net (SEHUNET) architecture, a modification of half U-Net with squeeze-excite process to provide attention mechanism on each feature maps channel of the model, in combination with synthetic minority oversampling technique (SMOTE) is proposed. The test accuracy of SEHUNET is 98.52% with area under the curve of receiver operation characteristic (AUROC) of 0.999. This result outperforms the previous study that used CNN with Bayesian optimization, achieving accuracy of 95.89% and AUROC of 0.992. SEHUNET is also able to compete with the transfer learning methods used in previous research such as InceptionV3 with 96.35% accuracy, visual geometry group (VGG) with 96.8%, and ResNet with 98.63%. This performance can be achieved by SEHUNET with only 0.268 million parameters compared to the architecture parameters used in previous research ranging from 11 million to 33 million.
Detection of COVID-19 based on cough sound and accompanying symptom using LightGBM algorithm Wiharto, Wiharto; Abdurrahman, Annas; Salamah, Umi
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.pp940-949

Abstract

Coronavirus disease 19 (COVID-19) is an infectious disease whose diagnosis is carried out using antigen-antibody tests and reverse transcription polymerase chain reaction (RT-PCR). Apart from these two methods, several alternative early detection methods using machine learning have been developed. However, it still has limitations in accessibility, is invasive, and its implementation involves many parties, which could potentially even increase the risk of spreading COVID-19. Therefore, this research aims to develop an alternative early detection method that is non-invasive by utilizing the LightGBM algorithm to detect COVID-19 based on the results of feature extraction from cough sounds and accompanying symptoms that can be identified independently. This research uses cough sound samples and symptom data from the Coswara dataset, and cough sound’s features were extracted using the log mel-spectrogram, mel frequency cepstrum coefficient (MFCC), chroma, zero crossing rate (ZCR), and root mean square (RMS) methods. Next, the cough sound features are combined with symptom data to train the LightGBM. The model trained using cough sound features and patient symptoms obtained the best performance with 95.61% accuracy, 93.33% area under curve (AUC), 88.74% sensitivity, 97.91% specificity, 93.17% positive prediction value (PPV), and 96.33% negative prediction value (NPV). It can be concluded that the trained model has excellent classification capabilities based on the AUC values obtained.
Model Group Decision Support System Based on Depression Anxiety Stress Scales Using Ordered Weighted Averaging Aggregation Method Wiharto, Wiharto; Putri, Della K.; Sihwi, Sari W.; Salamah, Umi; Suryani, Esti; Atina, Vihi; Utomo, Pradityo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.678

Abstract

Depression, anxiety, and stress are common psychological conditions often triggered by the pressures of daily life. Depression Anxiety Stress Scale (DASS), is a widely used tool for assessing the severity of these disorders, available in different versions such as the DASS-21 and DASS-42. In line with these findings, DASS-21 consists of 21 symptom items, categorized into three types of disorders, with seven items assigned to each. In contrast, the DASS-42 includes 42 symptom items, with 14 items allocated per disorder. Both versions serve as standardized tools for assessing the severity of depression, anxiety, and stress, and the different versions show that one item only affects one disorder. In practice, it can affect several disorders with different priorities. This condition increases the risk of subjective bias in a psychologist's decision-making, as personal experiences and perceptions may influence their assessments. Therefore, this study aims to develop a Group Decision Support System (GDSS) model that considers the preferences of several psychologists in determining the priority of disorders based on the DASS-42 and DASS-21 items. The model has been built using the psychologist's preference method for DASS-42 and DASS-21 in fuzzy form, then combined using the Ordered Weighted Averaging (OWA) method to produce one decision. The alignment of top-priority items between GDSS and DASS was assessed as part of the evaluation. The results show a high degree of similarity, with GDSS matching 16 out of 21 symptom items in DASS-21 and 35 out of 42 items in DASS-42. The GDSS model can accommodate the preferences of decision-makers in providing weighting of the influence on each item in the DASS-21 and DASS-42, thereby providing more objective decisions.
Breast Cancer Classification on Ultrasound Images Using DenseNet Framework with Attention Mechanism Azka, Hanina Nafisa; Wiharto, Wiharto; Suryani, Esti
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.779

Abstract

Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide. Early detection of breast cancer being critical for increasing survival rates. Ultrasound image is commonly used for breast cancer screening due to its non-invasive, safe, and cost-effective. However, ultrasound images are often of low quality and have significant noise, which can hinder the effectiveness of classification models. This study proposes an enhanced breast cancer classification model that leverages transfer learning in combination with attention mechanisms to improve diagnostic performance. The main contribution of this research is the introduction of Dense-SASE, a novel architecture that combines DenseNet-121 with two powerful attention modules: Scaled-Dot Product Attention and Squeeze-and-Excitation (SE) Block. These mechanisms are integrated to improve feature representation and allow the model to focus on the most relevant regions of the ultrasound images. The proposed method was evaluated on a publicly available breast ultrasound image dataset, with classification performed across three categories: normal, benign, and malignant. Experimental results demonstrate that the Dense-SASE model achieves an accuracy of 98.29%, a precision of 97.97%, a recall of 98.98%, and an F1-score of 98.44%. Additionally, Grad-CAM visualizations demonstrated the model's capability to localize lesion areas effectively, avoiding non-informative regions, and confirming the model's interpretability. In conclusion, the Dense-SASE model significantly improves the accuracy and reliability of breast cancer classification in ultrasound images. By effectively learning and focusing on clinically relevant features, this approach offers a promising solution for computer-aided diagnosis (CAD) systems and has the potential to assist radiologists in early and accurate breast cancer detection.
Co-Authors Abdurrahman, Annas Anita Indrasari Atina, Vihi Azka, Hanina Nafisa Esti Suryani Katarina Novi Wahyudiani, Katarina Novi Meiyanto ES Mufidah, Yasmin Pamundi, Dias Lanjar Pradityo Utomo Pratama, Fadil Putri, Della K. Sihwi, Sari W. Syaifuddin, Angga Exca Pradipta Syamsul Arifin Umi Salamah Wina Isti Retnani