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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
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Enhanced Short-Term Residential Load Forecasting Using K-means Clustering and Iterative Residual LSTM Networks Sulaiman, Abdullahi; Isqeel, Abdullateef Ayodele; Issa, Abdulkabir Olatunji; Issa, Abdulrasheed Olayinka
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.v14i1.1168

Abstract

Accurate short-term load forecasting (STLF) is essential for optimizing energy management systems, ensuring operational efficiency, and balancing supply and demand in power grids. This study introduces a hybrid model, K-RNLSTM, which integrates K-means clustering with iterative Residual Long Short-Term Memory (LSTM) networks to improve prediction accuracy. The K-means clustering algorithm categorizes similar load patterns, allowing the model to handle seasonal and hourly variations more effectively. Iterative ResBlocks are incorporated within the LSTM framework to capture complex non-linear dependencies and improve the learning process without suffering from degradation. The model was evaluated using real-world residential electricity consumption data across four seasons: winter, spring, summer, and autumn. The K-RNLSTM model consistently outperformed traditional methods such as Extreme Learning Machines (ELM), Seasonal-Trend Loess (STL), Gated Recurrent Units (GRU), and standard LSTM in terms of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results demonstrated that K-RNLSTM achieved an average RMSE of 0.71, MAE of 0.43, and MAPE of 1.31%, surpassing benchmark models across all seasonal variations. Furthermore, the integration of ResBlocks significantly improved the model's ability to minimize large forecasting errors, particularly during peak demand periods. This research demonstrates the effectiveness of combining clustering techniques with deep learning models for short-term load forecasting, offering a robust solution for power system operators to optimize energy distribution and reduce operational costs.
TeleOTIVA: Advanced AI-Powered Automated Screening System for Early Detection of Precancerous Lesions Rachmamtullah, Muhammad Naufal; Nurmaini, Siti; Agustiansyah, Patiyus; Sanif, Rizal; Sastradinata, Irawan; Arum, Akhiar Wista; Firdaus, Firdaus; Darmawahyuni, Annisa; Tutuko, Bambang; Sapitri, Ade Iriani; Islami, Anggun
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.v14i1.1197

Abstract

In 2023, the Indonesian Ministry of Health launched the Rencana Aksi Nasional (RAN) to enhance the detection and management of cervical cancer in Indonesia. One of the main pillars in this movement is the implementation of early screening for precancerous lesions aimed at identifying and treating these lesions before they develop into cervical cancer. This effort includes improving public access to healthcare services, providing education and awareness about the importance of early detection, and utilizing the latest technology in screening procedures. It is hoped that, through these targeted and effective interventions, the incidence of cervical cancer can be significantly reduced. This research aims to facilitate the early detection screening process for cervical precancerous lesions, particularly in difficult areas for medical experts to reach. This study also seeks to assist obstetricians and gynecologists in detecting precancerous lesions automatically, quickly, and accurately. By developing an advanced technology-based screening system, it is hoped that early detection of precancerous lesions can be carried out more efficiently, thereby increasing the chances of timely treatment and reducing the incidence of cervical cancer across various regions in Indonesia. This system is designed to provide reliable and user-friendly diagnostic support as it is developed on a mobile platform that can be accessed anytime and anywhere. This research developed a system for early screening called Tele-OTIVA. The Tele-OTIVA application system is an advanced platform that uses artificial intelligence (AI) based approaches to provide optimal services in early detection of precancerous lesions. This application is designed for mobile, allowing users to access and use its advanced features anytime and anywhere. With the integration of AI technology, Tele-OTIVA can detect and analyze cervical precancerous lesions accurately and quickly to provide accurate and efficient screening results. The Tele-OTIVA application system is capable of providing satisfactory detection results. The performance of the proposed model achieves accuracy, sensitivity, and specificity levels above 90%. With this high performance, Tele-OTIVA ensures that the detection of precancerous lesions is carried out with high reliability and precision, instilling greater confidence in healthcare professionals and users during the screening and diagnosis process. The implementation of our application model offers numerous advantages over traditional methods. It significantly enhances efficiency by automating processes, reduces human error through rigorous error-checking mechanisms, and accelerates the processing of large datasets. These improvements streamline operations and ensure more reliable and rapid data analysis.
Development of a Littering Behavior Detection Using 3D Convolutional Neural Networks (3D CNN) Husni, Nyayu Latifah; Prihatini, Ekawati; Ulandari, Monica; Handayani, Ade Silvia
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.v14i1.1246

Abstract

Littering has become a significant problem that negatively impacts public health and environmental cleanliness. This research introduces an innovative solution using 3D Convolutional Neural Networks (3D CNN) technology to automatically detect littering behavior through real-time CCTV recordings. Two models were developed and tested. Model 1, which employs Conv3D, Batch Normalization, and Dropout, showed high training accuracy but exhibited fluctuations in validation accuracy, indicating potential overfitting. In contrast, Model 2, designed with a simpler structure without Batch Normalization and Dropout, achieved higher classification accuracy and efficiency. Both models significantly contribute to addressing littering in public areas, increasing awareness, and supporting environmental law enforcement. The integration of 3D CNN technology in detecting littering behavior demonstrates its potential to reduce pollution and promote environmentally responsible behavior.
Analyzing Co-Authorship Networks in Indonesian PTN-BH Institution Through Social Network Analysis Firdaus, Firdaus; Nurmaini, Siti; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Raflesia, Sarifah Putri; Lestarini, Dinda
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.v14i1.1265

Abstract

This study involved an examination of bibliographic information from Indonesia. Our approach centered on utilizing social network analysis to explore the co-authorship relationships among Indonesian authors, focused on the co-authorship network within the context of authors affiliated with Indonesian state universities known as "PTN-BH," which specialize in higher education and legal studies. To conduct our analysis, we gathered publication data from the Scopus database, spanning a time frame from 1948 to 2020. The primary methodology entailed constructing a graph composed of nodes and edges, representing the co-authorship connections among these authors. By employing the Louvain method, we were able to identify prominent communities within this graph. We carried out a comprehensive analysis at both macro and micro levels, involving measurement techniques tailored to these perspectives. Through this approach, we revealed and examined the collaboration patterns among authors associated with PTN-BH institutions, as illuminated by the co-authorship network analysis.
A Survey of Hand Gesture Dialogue Modeling For Map Navigation Pang, Yee Yong; Ismail, Nor Azman
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Human trends to use hand gesture in communication. The development of ubiquitous computer causes the possibility of human to interact with computer natural and intuitive. In human-computer interaction, emerge of hand gesture interaction fusion with other input modality greatly increase the effectiveness in multimodal interaction performance. It is necessary to design a hand gesture dialogue based on the different situation because human have different behavior depend on the environment. In this paper, a brief description of hand gesture and related study is presented. The aim of this paper is to design an intuitive hand gesture dialogue for map navigation. Some discussion also included at the end of this paper.
Using Power-Law Degree Distribution to Accelerate PageRank Jin, Zhaoyan; Wu, Quanyuan
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.
Monitoring and Resource Management in P2P Grid-Based Web Services Laouni, Djafri; Rachida, Mekki
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Grid computing has recently emerged as a response to the growing demand for resources (processing power, storage, etc.) exhibited by scientific applications. However, as grid sizes increase, the need for self-organization and dynamic reconfigurations is becoming more and more important. Since such properties are exhibited by P2P systems, the convergence of grid computing and P2P computing seems natural. However, using P2P systems (usually running on the Internet) on a grid infrastructure (generally available as a federation of SAN-based clusters interconnected by high-bandwidth WANs) may raise the issue of the adequacy of the P2P communication mechanisms. Among the interesting properties of P2P systems is the  volatility of  peers  which  causes  the  need  for  integration  of  a  service fault tolerance. And service Load balancing,   As a solution, we proposed a mechanism of fault tolerance and model of Load balancing  adapted to a grid P2P model, named SGRTE (Monitoring and Resource Management, Fault Tolerances and Load Balancing).DOI: 10.18495/comengapp.12.071084
Lightweight Solar Vehicle Impact Analysis Using ABAQUS/EXPLICIT Passarella, Rossi; Taha, Zahari
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This paper described the Abaqus/Explicit 6.7 simulation work performed to study the frontal crash impact condition for an in-house designed and produced lightweight solar vehicle main structural body. The structural body was fabricated from aluminum hollow pipes welded together. The analysis is needed to safeguard the safety of the vehicle driver. The dynamic response of the vehicle structure when subjected to frontal impact condition was simulated, according to NASA best practice for crash test methodology. The simulated speed used was based on the NHTSA standard. Comparison of the analysis with the standard Head Injury Criteria (HIC) and Chest Injury Criteria (CIC) revealed that the driver of the designed vehicle would not be risk because the acceleration resultant was found to be lower than 20 G.  The analysis also proved that structural component was able to protect the driver during any frontal collision incident. However, to ensure the safety of the driver, safety precautions such as the use of seatbelt and helmet as well as driving below the speed limit are recommended.DOI: 10.18495/comengapp.12.085096
Circularly Polarized Slotted Microstrip Patch Antenna with Finite Ground Plane Rawat, Sanyog; Sharma, K K
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

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Abstract

In this paper a new geometry of circularly polarized patch antenna is proposed with improved bandwidth. The radiation performance of proposed patch antenna is investigated using IE3D simulation software and its performance is compared with that of conventional rectangular patch antenna. The simulated return loss, axial ratio and impedance with frequency for the proposed antenna are reported in this paper. It is shown that by selecting suitable ground-plane dimensions, air gap and location of the slots, the impedance bandwidth can be enhanced upto 10.15% as compared to conventional rectangular patch (4.24%) with an axial ratio bandwidth of 4.05%.DOI: 10.18495/comengapp.12.097106
Combined Classifier for Face Recognition using Legendre Moments Sridhar, D; Krishna, I.V. Murali
Computer Engineering and Applications Journal (ComEngApp) Vol. 1 No. 2 (2012)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.DOI: 10.18495/comengapp.12.107118

Page 24 of 32 | Total Record : 318

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