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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Indonesian Journal of Electrical Engineering and Computer Science Sriwijaya Journal of Informatics and Applications Jurnal Mandiri IT
Muhammad Naufal Rachmatullah
Universitas Sriwijaya
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Other

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Pemanfaatan aplikasi daring untuk peningkatan pemasaran songket dan purun perajin Burai Sri Indra Maiyanti; Anita Desiani; Sugandi Yahdin; Erwin Erwin; Desty Rodiah; Muhammad Naufal Rachmatullah; Dite Geovanni; Muhammad Akmal Shidqi; Muhammad Gibran Al-Filambany
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 5, No 2 (2022): Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v5i2.14356

Abstract

Delineation of electrocardiogram morphologies by using discrete wavelet transforms Annisa Darmawahyuni; Siti Nurmaini; Hanif Habibie Supriansyah; Muhammad Irham Rizki Fauzi; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Bambang Tutuko
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 1: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i1.pp159-167

Abstract

The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. This paper proposes a discrete wavelet transform (DWT), non-stationary signal analysis for noise removal, and onset-offset of PQRST feature extraction. A well-known database from Physionet: QT database (QTDB) is used to validate the DWT function for detecting the onset and offset of P-wave, QRS-complex, and T-wave localization. From the results, P-peak detection gets the highest result that achieves 2.19 and 13.62 milliseconds of mean error and standard deviation, respectively. In contrast, Toff has obtained the highest error value due to differences in the T-wave morphology. It can be affected by inverted or biphasic T-waves and others.
Paper Clustering Data Bibliografi menggunakan Algoritma DBSCAN dengan Author Matching Classifier Berbasis Deep Neural Network Ricy Firnando; Siti Nurmaini; Sukemi Sukemi; Firdaus Firdaus; Muhammad Naufal Rachmatullah
Jurnal Sistem Informasi Vol 14, No 2 (2022)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/jsi.v14i2.19089

Abstract

Ambiguitas nama penulis atau author name ambiguity sering kali menjadi masalah yang dapat mempengaruhi kualitas layanan database bibliografi. Untuk mengatasi masalah ambiguitas nama penulis maka diciptakanlah disambiguasi nama penulis atau author name disambiguation. Metode yang digunakan dalam author name disambiguation umumnya menangani masalah ambiguitas nama penulis dengan pendekatan author matching, classification, dan clustering. Beberapa penelitian menggabungkan beberapa pendekatan seperti menggunakan author matching classifier berbasis algoritma random forest untuk pairwise classification dan algoritma DBSCAN sebagai algoritma clustering namun masih belum mendapatkan hasil atau performa yang optimal. Pada penelitian ini dibangun sebuah model author matching classifier berbasis deep neural network yang kemudian diimplementasikan dalam algoritma clustering DBSCAN. Berdasarkan percobaan yang dilakukan menggunakan dataset The Giles, model author matching cassifier berbasis deep neural network yang kami usulkan dapat menghasilkan performa sebesar 95.99% untuk pairwise classification dan 97.23% untuk clustering.
Securing Text File on Audio Files using Least Significant Bit (LSB) and Blowfish Ahmad Rizky Fauzan; Al Farissi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 3, No 2 (2022)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v3i2.42

Abstract

Along with the development of technology, communication can be done in various ways, one of which is digital messages. But often the messages sent do not reach their destination and are obtained by irresponsible parties. This happens because of the lack of security in the file. For this reason, security is needed so that messages cannot be stolen or seen by other parties. There are various ways to secure messages, including Steganography and Cryptography techniques. This study uses a combination of the Least Significant Bit method and the Blowfish algorithm to secure secret messages in audio files. This research will measure encryption and decryption time, analysis of message file size changes after encryption and decryption, and PSNR value of audio files. The result of encryption using blowfish is a change in the size of the message file caused by the size of the message file is less than the block cipher size, so additional bytes are given so that the message size matches the block cipher size. The speed of the encryption and decryption process using the blowfish algorithm results in an average time for encryption of 547.98ms while the average time for decryption is 538.19ms. The longest time for the encryption process is 557.30ms and the fastest is 534.50ms, while the longest time for the decryption process is 548.74ms and the fastest is 531.46ms. Hiding messages in audio files using LSB produces PSNR values above 30dB.
CLASSIFICATION OF ATRIAL FIBRILLATION IN ECG SIGNAL USING DEEP LEARNING Raihan Mufid Setiadi; Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah
Sriwijaya Journal of Informatics and Applications Vol 4, No 1 (2023)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/sjia.v4i1.53

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Classification of Atrial Fibrillation In ECG Signal Using Deep Learning Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah; Raihan Mufid Setiadi
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.439

Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1-Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus Firdaus; Muhammad Fachrurrozi; Siti Nurmaini; Bambang Tutuko; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ade Iriani Sapitri; Anggun Islami; Masayu Nadila Maharani; Bayu Wijaya Putra
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.457

Abstract

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 Short-Term 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.
Segmentation of Skin Lesions Using Convolutional Neural Networks Firdaus Firdaus; Muhammad Fachrurrozi; Muhammad Naufal Rachmatullah; Dewi Chayanti; Annisa Darmawahyuni; Anggun Islami; Ade Iriani Sapitri; Bambang Tutuko
Computer Engineering and Applications Journal Vol 12 No 1 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i1.466

Abstract

Skin lesions play a crucial role as the initial clinical symptoms of diseases such as chickenpox and melanoma. By employing digital image processing techniques for skin cancer detection, it becomes feasible to diagnose these conditions without the need for physical contact with the skin. However, the automatic analysis of dermoscopy images, which exhibit characteristics like residue (hair and ruler markers), indistinct borders, varying contrast, and variations in shape and color, poses significant challenges. To overcome these difficulties, effective hair removal through segmentation has been explored extensively in the literature. In this study, we present a skin lesion segmentation system developed using the Convolutional Neural Networks (CNNs) method with the U-Net architecture. The model was constructed and evaluated using the HAM10000 Dataset. The results achieved by the best-performing model were outstanding, with a Pixel Accuracy, Intersection over Union (IoU), and F1 Score of 95.89%, 90.37%, and 92.54%, respectively
Video Based Fish Species Detection Using Faster Region Convolution Neural Network Muhammad Naufal Rachmatullah; Akhtiar W Arum
Computer Engineering and Applications Journal Vol 12 No 2 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i2.467

Abstract

Fish recognition and classification represent significant challenges in marine biology and agriculture, promising fields for advancing research. Despite advancements in real-time data collection, underwater fish recognition and classification still require improvement due to challenges such as variations in fish size and shape, image quality issues, and environmental changes. Feature learning approaches, particularly utilizing convolutional neural networks (CNNs), have shown promise in addressing these challenges. This study focuses on video-based fish species classification, employing a feature learning-based extraction method through CNNs. The process involves two main stages: detection and classification. To address the detection and classification in video a Faster Region Convolutional Neural Network (RCNN) with transfer learning techniques are applied, achieving a mean average precision of 84% for detection and classification tasks. These techniques offer promising avenues for enhancing fish recognition and classification in diverse environments
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

Abstract

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.
Co-Authors Abdurahman Ade Iriani Sapitri Ade Iriani Sapitri Ahmad Rifai Ahmad Rizky Fauzan Akhiar Wista Arum Akhtiar W Arum Al Farissi Al-Filambany, Muhammad Gibran Ananda, Dea Agustria Andre Herviant Juliano Anggun Islami Anggun Islami Anita Desiani Annisa Darmawahyuni Annisa Darmawahyuni Armansyah, Risky Arnaldo, Muhammad Arum, Akhiar Wista Bambang Tutuko Bambang Tutuko Bambang Tutuko Bayu Wijaya Putra Darmawahyuni, Annisa Darmawahyuni, Annisa Desty Rodiah Dewi Chayanti Dian Palupi Rini Dian Palupi Rini Dinda Lestarini Dite Geovanni Erwin Erwin Erwin, Erwin Fadel Muhammad, Fadel Fahreza, Irvan Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Geovanni, Dite Hadipurnawan Satria Hanif Habibie Supriansyah Irvan Fahreza Islami, Anggun Kurniawan, Anggy Tias M. Fachrurrozi . Maharani, Masayu Nadila Masayu Nadila Maharani Mira Afrina Muhammad Akmal Shidqi Muhammad Arnaldo Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Gibran Al-Filambany Muhammad Irham Rizki Fauzi Muhammad Taufik Roseno, Muhammad Taufik Novi Yusliani Patiyus Agustiansyah PATIYUS AGUSTIANSYAH, PATIYUS Putri Mirani Rahmat Fadli Isnanto Raihan Mufid Setiadi Raihan Mufid Setiadi Renny Amalia Pratiwi Reza Firsandaya Malik Ricy Firnando Ricy Firnando Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Ririn Purnama Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Setiadi, Raihan Mufid Shidqi, Muhammad Akmal Siti Nurmaini Sri Indra Maiyanti Sri Indra Maiyanti Suci Dwi Lestari Suci Dwi Lestari Sugandi Yahdin Sukemi Sukemi Sukemi Sutarno Sutarno Sutarno Syaputra, Hadi Tio Artha Nugraha Varindo Ockta Keneddi Putra Yesi Novaria Kunang