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All Journal ComEngApp : Computer Engineering and Applications Journal Annual Research Seminar Jurnal Abdimas Maduma Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Muhammad Fachrurrozi
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Public Health Social Sciences

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Journal : comengapp computer engineering and applications journal

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.465 KB) | DOI: 10.18495/comengapp.v11i1.398

Abstract

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 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.
Co-Authors Ade Iriani Sapitri Al Farissi Anggun Islami Anna Dwi Marjusalinah Annisa Darmawahyuni Arum, Akhiar Wista Arum Bambang Tutuko Bayu Wijaya Putra Ermatita - Firdaus Firdaus Firdaus Firdaus Hadipurnawan Satria Irvan Fahreza Kanda Januar Miraswan Masayu Nadila Maharani Mira Afrina Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Novi Yusliani Osvari Arsalan Primanita, Anggina Raihan Mufid Setiadi Redo Jufarda Rizki Kurniati Siti Nurmaini Suci Dwi Lestari