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 318 Documents
 21   22   23   24   25 
CLUSTER ANALYSIS OF OBESITY RISK LEVELS USING K-MEANS AND DBSCAN METHODS Geovani, Dite; Umari, Zainal; Ramadini, Suci
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.481

Abstract

Obesity is defined as excessive fat accumulation and abnormal accumulation of adipose tissue in the human body that poses health risks. The causes of obesity are multifactorial and include environmental and individual factors. Several factors that cause obesity include genetic, behavioral and environmental factors. Obesity causes various problems in various fields, including health, employment, demographics, economics and family. The problem of obesity has a significant impact on public health. Therefore, understanding and predicting the level of obesity risk is important in efforts to prevent and treat obesity risk. Data on eating habits, physical activity, and other factors associated with obesity levels in certain populations can provide an important basis for understanding obesity risk. This research clusters the risk of obesity to find hidden patterns in the data. The stages in this research consist of pre-processing, clustering, and analysis. The clustering methods used are K-means and DBSCAN. In clustering using the K-means method with a parameter value of k , results are obtained with the same pattern as clustering using the DBSCAN method with a parameter value of epsilon and a minimum sample . In clustering using the K-means method with a parameter value of k , Four clusters were formed which had different patterns. The clustering results obtained in this research can be used as an effort to prevent and treat the risk of obesity.
ECG Signal Denoising Using 1D Convolutional Neural Network Rifai, Ahmad; Rachmamtullah, Muhammad Naufal; Sari, Winda Kurnia
Computer Engineering and Applications Journal Vol 13 No 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i2.482

Abstract

Electrocardiogram (ECG) signals are crucial for monitoring cardiac activity and diagnosing various cardiovascular conditions. However, these signals are often contaminated by different types of noise, such as baseline wander, muscle artifacts, and power line interference, which can obscure critical information and hinder accurate diagnosis. This study used a 1-Dimensional Convolutional Neural Network (1D CNN) architecture with seven convolutional layers for denoising ECG signals. The model utilizes a fully convolutional autoencoder approach, comprising an encoder that transforms noisy input signals into compact feature representations and a decoder that reconstructs the cleaned signals. The proposed architecture was tested using the MIT-BIH Noise Stress Test Database, which includes ECG recordings with simulated noise conditions. Performance evaluation metrics such as Mean Squared Error (MSE), Signal-to-Noise Ratio (SNR), and Mean Absolute Deviation (MAD) were used to assess the model's effectiveness. Results showed a low MSE of 0.034, a high SNR of 15.8 dB, and a MAD of 0.754, indicating significant noise reduction and high-quality signal reconstruction. These findings demonstrate that the 1D CNN architecture effectively reduces various types of noise in ECG signals, thereby enhancing signal quality and facilitating more accurate analysis and diagnosis. The model's ability to maintain the integrity of crucial ECG features while removing noise suggests its potential utility in clinical applications for improving cardiovascular disease diagnosis
The Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network Fauzan, Ahmad; Handayani, Lestari; Insani, Fitri; Jasril, Jasril; Sanjaya, Suwanto
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.484

Abstract

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection.
Imbalanced Data NearMiss for Comparison of SVM and Naive Bayes Algorithms Gunawan, Wawan; Devianto, Yudo; Sari, Anggi Puspita
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.485

Abstract

The study aims to improve the diagnosis, management, and prevention of HIV/AIDS by using classification algorithms. The dataset used consists of 707,379 records and 89 columns. Data preprocessing includes removing irrelevant attributes, handling inconsistencies, and balancing the data using the NearMiss method, resulting in a balanced proportion of reactive and non-reactive HIV cases. Once the data is balanced, it is split into several ratios: 60:40, 70:30, 80:20, and 90:10. The classification models used in this study are Naive Bayes and SVM. The models are evaluated using the metrics Accuracy, Precision, Recall, and F1-Score. The results show that the SVM model achieves the highest accuracy of 82.6% with a 90:10 data split at a 6-fold value, and 82.2% with a 60:40 data split at a 5-fold value. On the other hand, Naive Bayes achieves the highest accuracy of 61.1% with a 60:40 data split.
MRI-Based Brain Tumor Instance Segmentation Using Mask R-CNN Nasrudin, Muhammad
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.490

Abstract

Brain tumor segmentation is a crucial step in medical image analysis for the accurate diagnosis and treatment of patients. Traditional methods for tumor segmentation often require extensive manual effort and are prone to variability. In this study, we propose an automated approach for brain tumor segmentation using Mask R-CNN, a state-of-the-art deep learning model for instance segmentation. Our method leverages MRI images to identify and delineate brain tumors with high precision. We trained the Mask R-CNN model on a dataset of annotated MRI images and evaluated its performance using the mean Average Precision (mAP) metric. The results demonstrate that our model achieves a high mAP of 90.3%, indicating its effectiveness in accurately segmenting brain tumors. This automated approach not only reduces the manual effort required for tumor segmentation but also provides consistent and reliable results, potentially improving clinical outcomes.
Fake News Detection Using Optimized Convolutional Neural Network and Bidirectional Long Short-Term Memory Sari, Winda Kurnia; Azhar, Iman Saladin B.; Yamani, Zaqqi; Florensia, Yesinta
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.492

Abstract

The spread of fake news in the digital age threatens the integrity of online information, influences public opinion, and creates confusion. This study developed and tested a fake news detection model using an enhanced CNN-BiLSTM architecture with GloVe word embedding techniques. The WELFake dataset comprising 72,000 samples was used, with training and testing data ratios of 90:10, 80:20, and 70:30. Preprocessing involved GloVe 100-dimensional word embedding, tokenization, and stopword removal. The CNN-BiLSTM model was optimized with hyperparameter tuning, achieving an accuracy of 96%. A larger training data ratio demonstrated better performance. Results indicate the effectiveness of this model in distinguishing fake news from real news. This study shows that the CNN-BiLSTM architecture with GloVe embedding can achieve high accuracy in fake news detection, with recommendations for further research to explore preprocessing techniques and alternative model architectures for further improvement.
The Eye and Nose Identification Chip Controller-Based on Robot Vision Using Weightless Neural Network Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Fitriyanto, Megi
Computer Engineering and Applications Journal Vol 13 No 03 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i03.615

Abstract

Increasingly advanced image analysis in computer vision, allowing computers to interpret, identify, and analyze pictures with accuracy comparable to humans. The availability of data sources in decimal, hexadecimal, or binary forms enables researchers to take the initiative in applying their study findings. Decimal formats are typically used on traditional computers like desktops and minicomputers, whereas hexadecimal and binary formats were utilized on single-chip controllers. Weightless Neural Network is a method that can be implemented in a single chip controller. The aim of this research is to develop a facial recognition system, for eye and mouth identification, that works in a single chip controller or also called a microcontroller. The suggested method is a Weightless Neural Network with Immediate Scan approach for processing and identifying eye and nose patterns. The data will be handled in many memory locations that are specifically designed to handle massive volumes of data. The data is made up of primary face data sheets and face input data. The data sets utilized are (x,y) pixels, and frame sizes range from 90x90 pixels to 110x110 pixels. Each face shot will be processed by selecting the region of the eyes and nose and saving it as an image file. The eye and nose will identify the face frame. Next, the photos will be converted to binary format. A magazine matrix will be used to transmit binary data from a minicomputer to a microcontroller via serial connection. Based on a known pattern, the resultant similarity accuracy is 83,08% for the eye and 84,09% for the sternum. In contrast, the similarity percentage for an eye ranges from 70% to 85% for an undefined pattern.
Anxiety Detection for Autism Children through Vital Signs Monitoring using a Socially Assistive Robot Prihatini, Ekawati; Damsi, Faisal; Husni, Nyayu Latifah; Muslimin, Selamat; Marniati, Yessi; Ramadhan, M. Daffa
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.v14i1.493

Abstract

Socially Assistive Robot (SAR) to detect anxiety levels in children with Autism Spectrum Disorder (ASD), a condition often accompanied by difficulties in recognising and expressing emotions, including anxiety. Early recognition of anxiety in children with Autism Spectrum Disorder (ASD) is crucial as it can affect their behaviour and social interactions. This SAR monitors vital signs namely blood pressure, heart rate and body temperature. This study involved children with Autism Spectrum Disorder (ASD) with two conditions, namely Asperger Syndrome and Classical Autism who interacted with a Socially Assistive Robot (SAR) equipped with a tensimeter (MPS20N0040D sensor) for blood pressure, MAX30100 sensor for heart rate, and MLX90614 sensor to measure body temperature. Results show that the Socially Assistive Robot (SAR) is able to measure vital signs with high accuracy and provide an indication of anxiety levels effectively, as vital signs correlate with anxiety levels. These findings demonstrate the potential of the Socially Assistive Robot (SAR) as a reliable tool in anxiety monitoring in children with ASD, with important implications for the development of future therapeutic interventions
Emotion Classification in Indonesian Text Using IndoBERT Rizky, Aditya Saiful; Hidayat, Erwin Yudi
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.v14i1.494

Abstract

Mental health issues have become a challenge that affects many individuals around the world. A 2018 WHO report noted an increase in deaths by suicide, with a frequency of one case every 40 seconds. The Ipsos Global 2023 survey showed that 44% of respondents in 31 countries are concerned about mental health, while 30% identified stress as a major issue. In Indonesia, the mental health situation is also a serious concern. The 2022 I-NAMHS survey found that 34.9% of adolescents face mental health problems, but only 2.6% of them utilize counseling services. Emotion detection in text is challenging due to the absence of facial expressions or voice modulation. This study aims to classify emotions in Indonesian text using the IndoBERT model. The dataset used consists of 5079 tweets with five emotion labels: Angry, Fear, Joy, Love, and Sad. Parameter variations include the composition of training, validation, and test data split (80:10:10, 75:15:15, and 60:20:20), as well as the combination of learning rate (1e-2 to 1e-7) and batch size (8, 16, and 32). The model was trained for 25 epochs with the application of early stop and patience for 5 epochs. The experimental results showed that the composition of data split 80:10:10, learning rate 1e-6, and batch size 8 resulted in optimal classification. Although some experiments showed indications of overfitting, this research has important implications in the early detection of emotions and can help in mental health treatment efforts.
Deep Neural Networks for Intelligent Voice Authentication Systems in Large-Scale Electronic Voting Olaniyi, Olayemi Mikail; Nuhu, Bello Kotangora; Okunade, Oluwasogo Adekunle; Ezeanya, Uchenna Christiana; Eke, Chimdiebube Emmanuel
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.v14i1.1164

Abstract

The authentication of eligible voters is an area of concern that needs further exploration of the prospects of electronic voting systems. The integration of voice authentication in electronic voting systems for varying numbers of disabled and prospective voters should be secure, scalable, and suitable in both federal and state elections. Machine learning (ML) is an evolving field of computing that presents prospects in electronic voting. Applying ML algorithms to electronic voting provides optimal solutions to a wide range of biometric authentication challenges. This paper presents the design of an effective voice classification algorithm from a narrower perspective that can be used in developing prototype electronic voting systems in large-scale voting scenarios, particularly for disabled voters. Applying the knowledge of deep neural networks, a three hidden layer network using a feed-forward architecture is designed for classifying voice data acquired from prospective voters. The proposed design is tested on two different datasets and is adapted to handle small and vast amounts of voters’ voice information. Results indicated average training and average validation accuracies of 92% and 97% respectively for both deep learning models for inclusivity and accountability of disabled voters in secure electronic voting systems.

Page 23 of 32 | Total Record : 318

 21   22   23   24   25 

Filter by Year

2012 2025


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