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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jupiter Bulletin of Electrical Engineering and Informatics Rekursif: Jurnal Informatika Jurnal Meteorologi Klimatologi dan Geofisika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Buletin Konstitusi Prosiding (SIAR) Seminar Ilmiah Arsitektur
Muhammad Fachrurrozi
Sriwijaya University
Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Social Sciences

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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
Identification of Indonesian Authors Using Deep Neural Networks Firdaus; Fahreza, Irvan; Nurmaini, Siti; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Lestari, Suci Dwi; Fachrurrozi, Muhammad; Afrina, Mira; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 1 (2022)
Publisher : Universitas Sriwijaya

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Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.
Classification of Atrial Fibrillation In ECG Signal Using Deep Learning Fachrurrozi, Muhammad; Rachmatullah, Muhammad Naufal; Setiadi, Raihan Mufid
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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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; 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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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 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.
Co-Authors Abdiansah, Abdiansah Archibald Hutahaean, Jerrel Adriel Armansyah, Risky Bambang Tutuko Bayu Wijaya Putra Darmawahyuni, Annisa Dwiyono, Aswin Dyah Widi Astuti Fahreza, Irvan Firdaus Firdaus Firdaus Fishwaranta, Alexandra Islami, Anggun Kade Wida, Dewa Ayu Maharani, Masayu Nadila Mira Afrina Muhammad Ikhsan Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad, Fadzli Nazuli, Muhammad Furqan Novi Yusliani Pratama, Jimiria Pratama, Reksi Hendra Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Setiadi, Raihan Mufid Sitepu, Delvi Nur Ropiq Siti Nurmaini Suci Dwi Lestari Yesi Novaria Kunang