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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JUSIFO : Jurnal Sistem Informasi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information Systems and Informatics SULUH: Jurnal Abdimas Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal Abdimas Bina Bangsa Smart: Journal of Sharia, Tradition, and Modernity
Bambang Tutuko
Sriwijaya University
Religion Automotive Engineering Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Exploration U-Net Architecture for Cervical Precancerous Lesions Segmentation Arum, Akhiar Wista; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Firdaus; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Ananda, Dea Agustria
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

The automatic analysis of images for the early detection of cervical cancer relies on the segmentation of cervical precancerous lesions. This paper investigates the incorporation of various CNN-based backbones into a U-Net model for improved segmentation accuracy. A set of twelve backbones was tested, including VGG16, VGG19, ResNet50, ResNext50, EfficientNetB7, InceptionResNetv2, DenseNet201, InceptionV3, MobileNet V2, SE-ResNet50, SE-ResNext50, and SE-Net154. Evaluation metrics were computed using Intersection over Union, pixel accuracy, and Dice coefficient. The findings demonstrate that U-Net with EfficientNetB7 backbone outperforms all other models with an IoU of 73.13%, pixel accuracy of 89.92%, and a Dice coefficient of 77.64%. These results were visually confirmed; segmentation outputs were examined, showing accurate delineation of lesion borders. The dominating performance of EfficientNetB7 was observed to be due to high feature extraction efficiency coupled with powerful spatial information representation. The study is, however, limited by a lack of clinical validation and expert evaluation from trained medical personnel. The results demonstrate the effectiveness of combining the U-Net architecture with advanced CNN backbones towards designing automated systems to analyze medical images.
Deep Learning for ECG-Based Arrhythmia Classification Based on Time-Domain Features Sari, Ririn Purnama; Darmawahyuni, Annisa; Tutuko, Bambang; Firdaus; Rachmatullah, Muhammad Naufal; Sapitri, Ade Iriani; Islami, Anggun; Arum, Akhiar Wista
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Arrhythmia is a disturbance in the electrical activity of the heart that can affect the rhythm and duration of the heartbeat. Early detection of arrhythmia is crucial to prevent more serious complications. Electrocardiogram (ECG) is an effective non-invasive diagnostic tool in detecting arrhythmia, but manual detection by experts takes time. To overcome this limitation, this research develops an arrhythmia classification system by utilizing deep learning. This study involves a series of stages, starting from pre-processing, feature extraction, and arrhythmia classification models using convolutional neural networks (CNN) and long short-term memory (LSTM). The results showed that feature extraction successfully improved model efficiency and accuracy. Evaluation of model performance using accuracy, recall, precision, specificity, and F1-score metrics showed that the LSTM model achieved 95% accuracy, 96% recall, 96% precision, 99% specificity, and 96% F1-score, outperforming the CNN model which achieved 91% accuracy, 90% recall, 89% precision, 98% specificity, and 89% F1-score. Thus, these results indicate that the LSTM model is superior in arrhythmia classification.
A deep learning-based myocardial infarction classification based on single-lead electrocardiogram signal Darmawahyuni, Annisa; Sari, Winda Kurnia; Afifah, Nurul; Tutuko, Bambang; Nurmaini, Siti; Marcelino, Jordan; Isdwanta, Rendy; Khairunnisa, Cholidah Zuhroh
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp352-360

Abstract

Acute myocardial infarction (AMI) carries a significant risk, emphasizing the critical need for precise diagnosis and prompt treatment of the responsible lesion. Consequently, we devised a neural network algorithm in this investigation to identify myocardial infarction (MI) from electrocardiograms (ECGs) autonomously. An ECG is a standard diagnostic tool for identifying acute MI due to its affordability, safety, and rapid reporting. Manual analysis of ECG results by cardiologists is both time-consuming and prone to errors. This paper proposes a deep learning algorithm that can capture and automatically classify multiple features of an ECG signal. We propose a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) for automatically diagnosing MI. To generate the hybrid CNN-LSTM model, we proposed 39 models with hyperparameter tuning. As a result, the best model is model 35, with 86.86% accuracy, 75.28% sensitivity and specificity, and 83.56% precision. The algorithm based on a hybrid CNN-LSTM demonstrates notable efficacy in autonomously diagnosing AMI and determining the location of MI from ECGs.
Legal Protection of Child Victims of Prostitution and its Contribution to the Development of Child Protection Law in Indonesia Tutuko, Bambang
SMART: Journal of Sharia, Traditon, and Modernity Vol. 4 No. 1 June (2024)
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/smart.v4i1.20521

Abstract

This article analyzes the implementation of legal protection for child victims of prostitution in Lampung Province and its contribution to the development of child protection law in Indonesia. The research aims to evaluate the alignment between child protection practices and the provisions of Islamic law and positive law, and to identify regulatory needs that support comprehensive protection for child victims of sexual violence. The theory used is the theory of mashlahah mursalah, which emphasizes general welfare. This study employs field research with a qualitative approach and inductive reasoning. Data were collected through interviews and documentation from the Women and Children's Protection Agency of Lampung Province. The findings indicate that the implementation of legal protection for child victims of sexual violence falls short of the expectations set by the constitution and legislation. There is a need for more stringent policies to ensure that victims receive legal protection and free access to necessary facilities. The conclusion of this research is that efforts to protect child victims of sexual violence require regulatory improvements to align with the mandate of the 1945 Constitution. The contribution of this study lies in the development of policy recommendations that can enhance legal protection for children in Indonesia and provide insights into the importance of synergy between Islamic law and positive law in protecting children's rights.
Enhancing ultrasound image quality using deep structure of residual network Sapitri, Ade Iriani; Nurmaini, Siti; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Tutuko, Bambang; Arum, Akhiar Wista
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3779-3794

Abstract

Ultrasonography, a medical imaging technique, is often affected by various types of noise and low brightness, which can result in low image quality. These drawbacks can significantly impede accurate interpretation and hinder effective medical diagnoses. Therefore, improving image quality is an essential aspect of the field of ultrasound systems. This study aims to enhance the quality of ultrasound images using deep learning (DL). The experiment is conducted using a custom dataset consisting of 2,175 infant heart ultrasound images collected from Indonesian hospitals, and the model is subsequently generalized using other datasets. We propose enhanced deep residual network combined convolutional neural networks (EDR-CNNs) to improve the image quality. After the enhancement process, our model achieved peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) scores of 38.35 and 0.92 respectively, outperforming other methods. The benchmarking with other ultrasound medical images indicates that our proposed model produces good performance, as evidenced by higher PSNR, lower SSIM, a decrease in mean square error (MSE), and a lower contrast improvement index (CII). In conclusion, this study encapsulates the forthcoming trends in advancing low-illumination image enhancement, along with exploring the prevailing challenges and potential directions for further research.
Co-Authors Abied, Wildan Ade Iriani Sapitri Aditya Aditya Aditya, Aditya Agus Triadi Agus Triadi Ahmad Dahlan Malik Ahmad Hudaifah, Ahmad Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Ananda, Dea Agustria Anik Kustirini, Anik Annisa Darmawahyuni Arum, Akhiar Wista Asyhad, M. Aulia Rahman Thoharsin Bayu Wijaya Putra Borisman Richardson Darmawahyuni, Annisa Darmawahyuni, Annisa Dewi, Kemala Dimas Budianto Dwi, Galih Malik Fajri, Faisal Febrita, Suci Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Ganesha Ogi Hanif Habibie Supriansyah Huda Ubaya Isdwanta, Rendy Islami, Anggun Ivall, Mochammad Jasmir Jasmir Kemala Dewi Khairunnisa, Cholidah Zuhroh Maharani, Masayu Nadila Marcelino, Jordan Muhammad Afif Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmamtullah Muhammad Naufal Rachmatullah PATIYUS AGUSTIANSYAH, PATIYUS PP Aditya, PP, Aditya, PP Pratama, Yogi Tiara Pudjihardjo, Hari Setijo Pudjihardjo, Hari Setijo Purwanto Purwanto Rachmamtullah, Muhammad Naufal Reza Firsandaya Malik Rizal Sanif Rizky, Anandhita Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Sari, Ririn Purnama Sarifah Putri Raflesia Sarmayanta Sembiring Sastradinata, Irawan Setyati Budiningrum, Diah Siti Nurmaini Sukemi Sukemi Sutarno Sutarno Sutarno Sutarno Sutarno Sutrimo Sutrimo Tresna Dewi Triadi, Agus Velia Yuliza W, Prayogo Pandhu Wanto, Sri Winda Kurnia Sari