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Multi-scale input reconstruction network and one-stage instance segmentation for enhancing heart defect prediction rate Sutarno, Sutarno; Nurmaini, Siti; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Darmawahyuni, Annisa; Firdaus, Firdaus; Islami, Anggun; Samsuryadi, Samsuryadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3404-3413

Artifacts and unpredictable fetal movements can hinder clear fetal heart imaging during ultrasound scans, complicating anatomical identification. This study presents a new medical imaging approach that combines one-stage instance segmentation with ultrasound (US) video enhancement for precise fetal heart defect detection. This innovation allows real-time identification and timely medical intervention. The study acquired 100 fetal heart US videos from an Indonesian Hospital featuring cardiac septal defects, generating 1,000 frames for training, validation, and testing. Utilizing a combination of the multi-scale input reconstruction network (MIRNet) for image enhancement and YOLOv8l-seg for real-time instance segmentation, the method achieved outstanding validation results, boasting a 99.50% mAP for bounding box prediction and 98.40% for mask prediction. It delivered a remarkable real-time processing speed of 68.4 frames per second. In application to new patients, the method yielded a 65.93% mAP for bounding box prediction and 57.66% for mask prediction. This proposed approach offers a promising solution to early fetal heart defect detection using ultrasound, holding substantial potential for enhancing healthcare outcomes.

Pengaruh Penggunaan Literacy Cloud terhadap Minat Baca dan Keterampilan Membaca Pemahaman Islami, Anggun; Nulhakim, Lukman; Suhandoko, Astri Dwi Jayanti
EDUKATIF : JURNAL ILMU PENDIDIKAN Vol 6, No 1 (2024)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/edukatif.v6i1.6352

Pentingnya menggunakan media pembelajaran untuk generasi alpha dapat dilihat dari tuntutan zaman yang semakin serba digital, di mana teknologi telah menjadi bagian yang tak terpisahkan dalam aktivitas keseharian. Media pembelajaran yang terintegrasi dengan teknologi mampu memfasilitasi pengajaran yang lebih menarik dan cocok dengan gaya belajar generasi alpha yang dominan dengan penggunaan teknologi. Penelitian ini menggunakan kuasi eksperimen untuk mengevaluasi pengaruh penggunaan media pembelajaran literacy cloud terhadap minat baca dan keterampilan membaca pemahaman siswa kelas 4. Populasi dalam penelitian ini melibatkan total 749 siswa kelas 4 yang berada di gugus 056. Sampel nya terdiri dari 70 siswa di SDN Buaran Mangga II dengan teknik purposive sampling. Teknik pengumpulan data melibatkan angket, tes, observasi, dan wawancara. Hasil penelitian menunjukkan bahwa penggunaan literacy cloud berpengaruh positif terhadap peningkatan minat baca dan keterampilan membaca pemahaman siswa, dengan nilai rata-rata kelas eksperimen lebih tinggi dibandingkan kelas kontrol. Implikasi penelitian ini memberikan wawasan kepada guru dan sekolah untuk meningkatkan kualitas pembelajaran dengan memanfaatkan literacy cloud sebagai sumber rujukan yang berharga. Penelitian ini juga berkontribusi pada pemahaman tentang efektivitas literacy cloud sebagai media pembelajaran dalam konteks pendidikan, memberikan dasar bagi pengembangan kebijakan pendidikan berorientasi teknologi, dan memberikan panduan praktis bagi guru dalam meningkatkan minat baca dan keterampilan membaca pemahaman siswa.

TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions Rachmamtullah, Muhammad Naufal; Nurmaini, Siti; Agustiansyah, Patiyus; Sanif, Rizal; Sastradinata, Irawan; Arum, Akhiar Wista; Firdaus, Firdaus; Darmawahyuni, Annisa; Tutuko, Bambang; Sapitri, Ade Iriani; Islami, Anggun
Computer Engineering and Applications Journal Vol 14 No 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1197

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called Tele-OTIVA. The Tele-OTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, Tele-OTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The Tele-OTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, Tele-OTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis.

Comparative Analysis Multi-Robot Formation Control Modeling Using Fuzzy Logic Type 2 – Particle Swarm Optimization Islami, Anggun; Nurmaini, Siti; Satria, Hadipurnawan
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 3 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Multi-robot is a robotic system consisting of several robots that are interconnected and can communicate and collaborate with each other to complete a goal. With physical similarities, they have two controlled wheels and one free wheel that moves at the same speed. In this Problem, there is a main problem remaining in controlling the movement of the multi robot formation in searching the target. It occurs because the robots have to create dynamic geometric shapes towards the target. In its movement, it requires a control system in order to move the position as desired. For multi-robot movement formations, they have their own predetermined trajectories which are relatively constant in varying speeds and accelerations even in sudden stops. Based on these weaknesses, the robots must be able to avoid obstacles and reach the target. This research used Fuzzy Logic type 2 – Particle Swarm Optimization algorithm which was compared with Fuzzy Logic type 2 – Modified Particle Swarm Optimization and Fuzzy Logic type 2 – Dynamic Particle Swarm Optimization. Based on the experiments that had been carried out in each environment, it was found that Fuzzy Logic type 2 - Modified Particle Swarm Optimization had better iteration, time and resource and also smoother robot movement than Fuzzy Logic type 2 – Particle Swarm Optimization and Fuzzy Logic Type 2 - Dynamic Particle Swarm Optimization.

Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus; Fachrurrozi, Muhammad; Nurmaini, Siti; Tutuko, Bambang; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Maharani, Masayu Nadila; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long ShortTerm Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients.

TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions Rachmatullah, Muhammad Naufal; Nurmaini, Siti; Agustiansyah, Patiyus; Sastradinata, Irawan; Arum, Akhiar Wista; Firdaus; Darmawahyuni, Annisa; Tutuko, Bambang; Sapitri, Ade Iriani; Islami, Anggun
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 1 (2025)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called TeleOTIVA. The TeleOTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, TeleOTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The TeleOTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, TeleOTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.

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

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.

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