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
 20   21   22   23   24 
Comparison of Naive Bayes and Support Vector Machine (SVM) Algorithms Regarding The Popularity of Presidential Candidates In The Upcoming 2024 Presidential Election Fadli Nurrizky; Saruni Dwiasnati
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.459

Abstract

This study aims to compare the effectiveness of two classification algorithms, Naive Bayes and Support Vector Machine (SVM), in analyzing the popularity of presidential candidates for the 2024 Presidential Election (Pilpres). The popularity of presidential candidates plays a crucial role in campaign strategies and political decision-making in the modern political era. This research utilizes data from social media, encompassing public sentiment towards presidential candidates and related political issues. The research results indicate that SVM achieves an accuracy rate of 97%, while Naive Bayes achieves 95%, demonstrating the superiority of SVM in predicting the popularity of presidential candidates. In conclusion, the selection of the appropriate algorithm for analyzing complex political data has a significant impact, and the high accuracy rates of both algorithms provide valuable guidance for political decision-makers and campaign teams in preparation for the upcoming 2024 Pilpres.
A Hybrid of Fuzzy C-Means for the segmentation in CT scan and X-ray images for screening the COVID-19 patients Nitit WangNo
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.460

Abstract

In this paper, using CT scan and X-ray images, we present a hybrid approach, based on combining fuzzy C-means with k-means clustering, to evaluate and determine pneumonia infection caused by the coronavirus disease (COVID-19). To achieve this objective, we introduce a hybrid method that combines fuzzy C-means clustering with K-means clustering. This hybrid approach is designed to effectively segment object boundaries within medical images, enabling the precise identification of pneumonia-related features. In addition to our hybrid method, we compare its performance with two other segmentation approaches: the Expectation Maximization (EM) algorithm and 2D Entropy segmentation. Which, the method we propose uses a comparison between the performances of the based on a database of medical imaging test. Experimental results showed that the proposed approach outperforms, it was found that the hybrid fuzzy C-means algorithm segmentation images methods give better performance in terms of accuracy, precision, and F-measure, which is effective in boundaries segmentation. Comparative results of the accuracy and image quality index demonstrate the robustness of AI. It also helps to improve work efficiency with accurate analysis of COVID-19 infection on CT scan and X-rays. In addition, the approach helps radiologists make clinical decisions for diagnosis, follow-up, and prognosis.
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
Video Annomaly Classification Using Convolutional Neural Network Rachmatullah, Muhammad Naufal; Sutarno, Sutarno; Isnanto, Rahmat Fadli
Computer Engineering and Applications Journal Vol 13 No 1 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i1.468

Abstract

The use of surveillance videos is increasingly popular in city monitoring systems. Generally, the analysis process in surveillance videos still relies on conventional methods. This method requires professional personnel to constantly monitor and analyze videos to identify abnormal events. Consequently, the conventional approach is time-consuming, resource-intensive, and costly. Therefore, a system is needed to automatically detect video anomalies, reducing the massive human resource utilization for video monitoring. This research employs deep learning methods to classify anomalies in videos. The video anomaly detection process involves transforming the video into image format by extracting each frame present in the video. Subsequently, a Convolutional Neural Network (CNN) model is utilized to classify anomalous events within the video. Testing results using the CNN architectures DenseNet121 and EfficientNet V2 yielded performance accuracies of 99.89 and 98.24, respectively. The testing results indicate that the DenseNet121 architecture outperforms the EfficientNetV2 architecture in terms of performance.
Electrical Energy Monitoring and Analysis System at Home using IoT-based Prophet Algorithm Firdaus, Vipkas Al Hadid; Apriyani, Meyti Eka; Aprilia, Nurus Laily
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.v13i2.435

Abstract

Electrical energy is one of the necessities of human life, especially in modern society in urban areas. With a monitoring device for electrical energy consumption using IoT technology, the results of the development show that the monitoring system works well, but the results show that current and voltage measurements are still less accurate. Therefore, in this study, an Electrical Energy Analysis and Monitoring System were developed using the IoT-Based Prophet Algorithm. Data collection was obtained from electrical energy using the PZEM-004T module sensor device used at home and the energy data obtained were stored in a MySQL database. This PZEM data retrieval will appear in real time on the Monitoring Website. The dataset was processed by implementing the Prophet Algorithm, evaluating the model and visualizing the prediction results on the analysis website. Testing using Mean Absolute Percentage Error (MAPE). For design, this system uses energy data and data retrieval time as parameters in the monitoring system for the use of electrical energy at home. Analysis of data taken from electrical energy monitoring was predicted by the model created by the Prophet Algorithm and tested with MAPE to see how accurate the predicted value is in the Prophet Algorithm model. Predictions in this study get an error value of less than 10%, namely 6.87%, which means it is very accurate in predicting the prophet algorithm at home.
An Improved Myocardial Infarction Detection using Convolutional Neural Network and Graph Neural Network Algorithm Abisoye, Opeyemi Aderiike
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.v13i2.438

Abstract

Myocardial infarction (MI) is a crucial health problem and its mortality rate is higher than that of cancer. It is the damage and death of heart muscle from the sudden blockage of a coronary artery by a blood clot. Although lots of researches have been carried out with impressive performance record for detection of MI, however, existing approaches for MI detection can be improved upon for better performance. A vital piece of medical technology that aids in the diagnosis of a number of heart-related disorders in patients is an electrocardiogram (ECG). To find significant episodes in long-term ECG data, an automated diagnostic method is needed. Cardiologists face a very difficult problem when trying to quickly examine long-term ECG records. To pinpoint critical occurrences, a computer-based diagnosing tool is necessary. In this study we employ Convolutional Neural Network (CNN) algorithm with Graph Neural Network (GNN) to select best features and make appropriate classifications. The result of the study gave f1 score of 99.58%, precision of 99.5% and an accuracy of 99.72%. Our proposed model have shown a significant improvement in the detection of MI, this will aid in effectively addressing the challenge of performance drawback in this domain of research.
Application of Machine Learning in Clustering Maize Producing Regions in Indonesia Eliyani, Eliyani; Dwiasnati, Saruni; Arif, Sutan Mohammad; Avrizal, Reza; Fatimah, Nona
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.v13i2.455

Abstract

Maize is considered an important commodity with promising market prospects. Given the importance of maize, there is a need to increase maize production to meet people's needs and maintain price stability. This study aims to group maize production in Indonesia by region, with the hope of finding areas that have the potential to become maize production centers to reduce dependence on imports. The data used in this research was obtained from the Central Statistics Agency, covering information from 34 provinces during the 2017-2021 period. This analysis uses the K-Means method with the Python programming language. The number of groups is determined using the Elbow Method. The results of this research show that there are three categories of maize production regions: regions with low maize production (below average), regions with medium maize production, and regions with high maize production. A total of 25 provinces are in the low production category, eight provinces are in the medium category, and only East Java is in the high production category.
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, Mohammad Reza; Herteno, Rudy
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.v13i2.469

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
Augmented Reality in STEM Using Personalized Learning to Promote Student’s Understanding Mukhlis, Rizki; Erlangga, Erlangga; Wihardi, Yaya; Raflesia, Sarifah Putri
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.v13i2.473

Abstract

The current curriculum highlights the premise of self-directed learning performed by students. Additionally, technological uses in educational settings prove to be a challenging task in a sense of implementing them in learning media and materials used in the classroom. This study aims at investigating the utilization of augmented reality (AR) in STEM (Science, Mathematics, Engineering, and Technology) using personalized learning. This study employed pre-experimental research design, specifically adopting One-Group Pretest-Posttest Design. The findings highlight that students’ pretest scores on average reached 51,6 and significantly improved to 82,67 in their posttest, whereas students’ gain score reached 0,64 which is considered as moderate. Their perspectives towards the use of augmented reality with personalized learning were significantly positive with the percentage of 82,1%. It is evident that the use of augmented reality with personalized learning is a viable option when it comes to affecting the learning outcomes.

Page 22 of 32 | Total Record : 318

 20   21   22   23   24 

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