cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Siti Nurmaini
Contact Email
comengappjournal@unsri.ac.id
Phone
+6285268048092
Journal Mail Official
comengappjournal@unsri.ac.id
Editorial Address
Jurusan Sistem Komputer, Fakultas Ilmu Komputer, Universtas Sriwijaya, KampusUnsri Bukit Besar, Palembang
Location
Kab. ogan ilir,
Sumatera selatan
INDONESIA
ComEngApp : Computer Engineering and Applications Journal
Published by Universitas Sriwijaya
ISSN : 22524274     EISSN : 22525459     DOI : 10.18495
Core Subject : Science, Engineering,
Computer Science & IT Engineering
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 12 No 3 (2023)" : 6 Documents clear
Littering Activities Monitoring using Image Processing Nyayu Latifah Husni; Ade Silvia Handayani
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.v12i3.427

Abstract

Littering is a human behavior that become a habit since childhood. Even though there are rules that prohibit this behavior, the community still continues to do so. In order to limit this bad behavior, a device that can monitor and provide notifications is needed. In this research, a device is offered that can identify human activities in real time using webcam-based image processing. Then, it is processed by machine learning using the Recurrent Neural Network (RNN). The monitoring device produced in this research works by comparing the captured image data with a dataset. The captured image data will then be extracted features and form several coordinate points on the human body, then the system will classify these human activities into the category of normal activities or littering activities. This device will provide an output in the form of a warning every time the activity of littering is detected.
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.
Nonparametric Regression Analysis of BE4DBE2 Relationship with n and z Variables using Naive Bayes and SVM Classification on Nuclear Data Ruben Cornelius Siagian
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.v12i3.443

Abstract

This research article describes several analyses of nuclear data using various statistical methods. The first analysis uses linear regression to investigate the relationship between the independent variables (n and z) and the response variable (BE4DBE2). The second analysis uses a nonparametric regression model to overcome the assumptions of normality and linearity in the data. The third analysis uses the Naive Bayes method to classify nuclear data based on variables n and z. The fourth analysis uses a decision tree to classify nuclear data based on the same variables. Finally, the article describes an SVM analysis and a K-means analysis to classify and group nuclide data. The article presents clear and organized descriptions of each analysis, including visual representations of the results. The findings of each analysis are discussed, providing valuable insights into the relationships between the variables and the response variable. The article demonstrates the usefulness of statistical methods in analyzing nuclear data.
Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Alif Almuqsit; Osvari Arsalan
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.v12i3.444

Abstract

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.
The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images Cahyo Pambudi Darmo; Lucky Indra Kesuma; Dite Geovani
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.v12i3.452

Abstract

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.
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.

Page 1 of 1 | Total Record : 6

 1 

Filter by Year

2023 2023


Reset
Filter By Issues
All Issue Vol. 14 No. 2 (2025) Vol 14 No 1 (2025) Vol. 14 No. 1 (2025) Vol 13 No 03 (2024) Vol. 13 No. 3 (2024) Vol. 13 No. 2 (2024) Vol 13 No 2 (2024) Vol 13 No 1 (2024) Vol. 13 No. 1 (2024) Vol 12 No 3 (2023) Vol. 12 No. 3 (2023) Vol. 12 No. 2 (2023) Vol 12 No 2 (2023) Vol. 12 No. 1 (2023) Vol 12 No 1 (2023) Vol 11 No 3 (2022) Vol. 11 No. 3 (2022) Vol. 11 No. 2 (2022) Vol 11 No 2 (2022) Vol 11 No 1 (2022) Vol. 11 No. 1 (2022) Vol 10 No 3 (2021) Vol 10 No 2 (2021) Vol 10 No 1 (2021) Vol 9 No 3 (2020) Vol 9 No 2 (2020) Vol 9 No 1 (2020) Vol 8 No 3 (2019) Vol 8 No 2 (2019) Vol 8 No 1 (2019) Vol 7 No 3 (2018) Vol 7 No 2 (2018) Vol 7 No 1 (2018) Vol 6 No 3 (2017) Vol 6 No 2 (2017) Vol. 6 No. 2 (2017) Vol. 6 No. 1 (2017) Vol 6 No 1 (2017) Vol. 5 No. 3 (2016) Vol 5 No 3 (2016) Vol 5 No 2 (2016) Vol 5 No 1 (2016) Vol 4 No 3 (2015) Vol 4 No 2 (2015) Vol 4 No 1 (2015) Vol 3 No 3 (2014) Vol 3 No 2 (2014) Vol 3 No 1 (2014) Vol 2 No 3 (2013) Vol. 2 No. 3 (2013) Vol. 2 No. 2 (2013) Vol 2 No 2 (2013) Vol. 2 No. 1 (2013) Vol 2 No 1 (2013) Vol 1 No 2 (2012) Vol. 1 No. 2 (2012) Vol 1 No 1 (2012) More Issue