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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer JURNAL NASIONAL TEKNIK ELEKTRO Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Serambi Engineering Jurnal Teknik Informatika (JUTIF) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saddami, Khairun
Universitas Syiah Kuala
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology

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Performance Evaluation of EfficientNetB3-Based Deep Learning Model for the Classification of Acute Lymphoblastic Leukemia and Normal Blood Cells Muchallil, Sayed; Fitria, Maya; Arrahman, Ridha; Saddami, Khairun
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 3 (2025): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i3.113

Abstract

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer that predominantly affects children and requires early and accurate diagnosis to improve patient survival rates. Traditional diagnostic methods rely heavily on manual examination of blood smear images by pathologists, which is not only time-consuming but also susceptible to human error and variability. To address this limitation, this study proposed an automated detection model based on deep learning, specifically employing the EfficientNetB3 convolutional neural network architecture. A publicly available dataset containing microscopic images of ALL and normal blood cells was used for training and evaluation. The images were preprocessed using normalization and augmentation techniques and resized to 300×300 pixels to align with the EfficientNetB3 input requirements. The model was trained using the Adam optimizer and monitored with EarlyStopping to prevent overfitting. Experimental results showed that the proposed model achieved an accuracy of 92.23%, precision of 92.75%, and recall of 95.57%, significantly outperforming conventional approaches such as Canberra distance, K-Nearest Neighbor, and ensemble CNN methods. In addition to the classification model, a web-based ALL detection system was developed to make the solution more accessible and user-friendly. The frontend was built using ReactJS, while the backend API, built with Flask, handles image input, model inference, and output delivery. The interface allows users to upload cell images, input patient names, and receive instant classification results along with confidence scores. This integrated system demonstrates a practical application of AI in medical diagnostics and holds potential for use in real-world, resource-limited clinical settings.
The Role of U-Net Segmentation for Enhancing Deep Learning-based Dental Caries Classification Yassar, Muhammad Keysha Al; Fitria, Maya; Oktiana, Maulisa; Yufnanda, Muhammad Aditya; Saddami, Khairun; Muchtar, Kahlil; Isma, Teuku Reza Auliandra
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 2 (2025): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i2.75

Abstract

Dental caries, one of the most prevalent oral diseases, can lead to severe complications if left untreated. Early detection is crucial for effective intervention, reducing treatment costs, and preventing further deterioration. Recent advancements in deep learning have enabled automated caries detection based on clinical images; however, most existing approaches rely on raw or minimally processed images, which may include irrelevant structures and noise, such as the tongue, lips, and gums, potentially affecting diagnostic accuracy. This research introduces a U-Net-based tooth segmentation model, which is applied to enhance the performance of dental caries classification using ResNet-50, InceptionV3, and ResNeXt-50 architectures. The methodology involves training the teeth segmentation model using transfer learning from backbone architectures ResNet-50, VGG19, and InceptionV3, and evaluating its performance using IoU and Dice Score. Subsequently, the classification model is trained separately with and without segmentation using the same hyperparameters for each model with transfer learning, and their performance is compared using a confusion matrix and confidence interval. Additionally, Grad-CAM visualization was performed to analyze the model's attention and decision-making process. Experimental results show a consistent performance improvement across all models with the application of segmentation. ResNeXt-50 achieved the highest accuracy on segmented data, reaching 79.17%, outperforming ResNet-50 and InceptionV3. Grad-CAM visualization further confirms that segmentation plays a crucial role in directing the model’s focus to relevant tooth areas, improving classification accuracy and reliability by reducing background noise. These findings highlight the significance of incorporating tooth segmentation into deep learning models for caries detection, offering a more precise and reliable diagnostic tool. However, the confidence interval analysis indicates that despite consistent improvements across all metrics, the observed differences may not be statistically significant.
Comparative Study of BiLSTM and GRU for Sentiment Analysis on Indonesian E-Commerce Product Reviews Using Deep Sequential Modeling Nasution, Khairunnisa; Saddami, Khairun; Roslidar, Roslidar; Akhyar, Akhyar; Fathurrahman, Fathurrahman; Aulia, Niza
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4878

Abstract

Sentiment analysis plays a crucial role in understanding customer perspectives, especially within Indonesian e-commerce platforms. Despite the success of deep learning in high-resource languages, its application to Indonesian sentiment data remains underexplored. Previous studies using models like BERT-CNN or fine-tuned IndoBERT achieved modest results, highlighting the need for more effective architectures for Indonesian language. This study aims to investigate the effectiveness of Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) models in classifying buyers’ sentiment from Indonesian product reviews on the PREDECT-ID dataset comprising 5,400 annotated product reviews. Standard NLP preprocessing techniques—including text normalization, tokenization, stopword removal, and stemming—were applied. Both models were trained using Adam and Stochastic Gradient Descent (SGD) optimizers, and their performance was evaluated using accuracy, precision, recall, and F1-score metrics. The GRU model trained with SGD achieved the highest performance, with an accuracy of 94.07%, precision of 93.84%, recall of 94.53%, and F1-score of 94.18%. Notably, the BiLSTM model combined with SGD resulted in competitive results, achieving 93.61% accuracy and 93.84% F1-score. The results confirm that GRU with SGD optimizer, are highly effective for sentiment classification in Indonesian language datasets. By leveraging deep sequential modeling for a low-resource language, this study contributes to the advancement of scalable sentiment analysis systems in underrepresented linguistic domains. The results contribute to the advancement of NLP systems for Indonesian by providing a benchmark for the future development of sentiment analysis tools in low-resource languages.
Co-Authors . Roslidar Abdul Malek Akhyar Akhyar Arnia, Fitri Arrahman, Ridha Aulia, Niza Bahri, Syamsul Fathurrahman Fathurrahman Fitri Arnia Fitri Arnia Isma, Teuku Reza Auliandra Kahlil Muchtar Khairul Munadi Khairul Munadi Maya Fitria Muchtar, Kahlil Muchtar, Kahlil Nasaruddin Nasaruddin Nasution, Khairunnisa Noviantika, Fina Oktiana, Maulisa Rizka Ramadhana Sayed Muchallil Suriadi Suriadi Syamsul Bahri Yassar, Muhammad Keysha Al Yudha Nurdin Yufnanda, Muhammad Aditya Yuwaldi Away Yuwaldi Away Zaita, Yandri