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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 10 Documents
 1 
Search results for , issue "vol. 15 no. 3 (2026)" : 10 Documents clear
Particle Swarm Optimisation of Static VAR Compensator Deployment for Voltage Support in Transmission Grid Olalekan Ogunbiyi; Andee Olasubomi Olayinkascott; Abdulazeez Mosopeoluwa Ekundayo
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1339

Abstract

Voltage stability is a persistent operational challenge in power systems, particularly in developing grids such as Nigeria’s, where rapid load growth and infrastructural limitations frequently push networks close to their stability margins. This study investigates the improvement of voltage stability in a representative transmission network through the optimal placement and sizing of Static VAR Compensators using Particle Swarm Optimisation. The IEEE 30-bus test system was adopted as a benchmark and assessed under normal (100%), light (80%), heavy (120%), and stress (140%) loading conditions. Under uncompensated operation, several buses violated the statutory voltage limits of 0.95–1.05 p.u., with overvoltages reaching 1.071 p.u. during light loading and undervoltage dropping to 0.927 p.u. under stress conditions, indicating elevated risks of voltage instability and possible voltage collapse. The PSO algorithm identified optimal buses for SVC deployment, with reactive power ratings ranging from about –49.6 MVAR (reactive absorption under light load) to +68.0 MVAR (reactive injection under stress load). After compensation, all bus voltages were restored within permissible limits, with critical buses showing average improvements of 3–5%. For instance, the most depressed voltage improved from 0.927 p.u. to 0.963 p.u., while peak overvoltage reduced to approximately 1.021–1.051 p.u. These results confirm the effectiveness of PSO-optimised SVC placement for enhancing voltage stability and operational security under varying load conditions.
Comparison of CRF and BiLSTM Models for Disease Named Entity Recognition on the NCBI Disease Dataset Agus Siswanto; Bambang Tutuko; Firdaus; Jasmir
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1352

Abstract

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) aimed at identifying and classifying named entities in text. In the biomedical domain, disease entity recognition is crucial for supporting automated medical information extraction. This study compares two NER model approaches, Conditional Random Field (CRF) and Bidirectional Long Short-Term Memory (BiLSTM), for disease entity recognition using the NCBI Disease dataset. The CRF model utilizes handcrafted linguistic features including prefixes, suffixes, POS tags, and word shapes, while BiLSTM employs GloVe 200-dimensional embeddings and automatically learns features from sequential context. BiLSTM experiments were conducted using 5 different seeds for robust statistical validation, reporting mean F1-score, standard deviation, and 95% confidence interval. Comprehensive analysis includes confusion matrix, error analysis, learning curve, and performance comparison per entity length. Results show that CRF achieves an F1-score of 78.49% (P=82.37%, R=74.95%), while BiLSTM achieves 76.54% ± 1.32% (95% CI ±1.15%). CRF outperforms BiLSTM by 1.95 percentage points under baseline conditions, although BiLSTM demonstrates notably fewer false positives (FP=124 vs FP=153) and a standard deviation of 1.32% across 5 experimental seeds
Vision Transformer-Based View Classification for Pediatric Cardiac Septal Defect Anggun Islami; Ria Nova; Ermatita Ermatita; ade Iriani Sapitri; Rahmatulloh Rahmatulloh
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1356

Abstract

Congenital Heart Disease (CHDs), particularly Cardiac Septal Defects (CSDs), remains a major cause of morbidity in children. This study proposes a deep learning–based approach using Vision Transformer (ViT) to automatically classify view echocardiographic images as a fundamental step toward improving diagnostic accuracy in pediatric CSDs. A total of 158 videos for the four-chamber view (4CV), five-chamber view (5CV), and subcostal (SC) views; 161 videos for the parasternal long-axis (PLAX) view; and 165 videos for the parasternal short-axis (PSAX) view were used in this study, and converted into 48,000 image frames. Four ViT variants (ViT-B/16, ViT-B/32, ViT-L/16, and ViT-L/32) were evaluated under identical experimental settings using 100 training epochs, the AdamW optimizer, and consistent input configurations. Model performance was assessed using accuracy, precision, and recall metrics. Experimental results showed that ViT-L/32 achieved the best performance with an accuracy of 92.20%, accompanied by superior precision and recall compared to other models. The proposed ViT-based framework provided an effective and reliable solution for automatic echocardiographic view classification, supporting early and accurate diagnosis of CSD
Decision Tree-Based Weather Factor Analysis for Bandar Lampung Flood Event Classification Ridho Sholehurrohman; Reza Habibi
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1374

Abstract

Flood events in Bandar Lampung City, Indonesia, are often triggered by extreme weather factors such as high rainfall, high humidity, and fluctuating air temperatures. This study applies the Decision Tree model as an analytical tool to examine the influence of these weather factors on flood occurrences using a dataset of 50 historical records containing information on rainfall, air temperature, humidity, and wind direction. The methodology includes data exploration, preprocessing, a single train–test split (80:20), and model evaluation using a confusion matrix. The exploratory analysis revealed that daily and weekly rainfall are the dominant factors contributing to flood events. The Decision Tree model, trained using default parameters, achieved a prediction accuracy of 90% on the test data. The visualization of the decision tree and feature importance analysis reinforce the finding that rainfall is the primary predictor of flood events. As a preliminary proof of concept, this study demonstrates that the Decision Tree model provides an interpretable approach for flood prediction. However, given the limited dataset size, further validation using larger datasets is recommended before operational implementation in early warning systems for Bandar Lampung City.
Implementation of IoT-Based Weather Prediction System Using Machine Learning for Irrigation Optimization in Peatland Oil Palm Plantations Dinda Rintic Rose; Sachio Aji; Zinniarethie Andari Kostiene; Yasyir Masy’al; M. Rizky Taufik Nur Hidayat; Aditya Putra Perdana Prasetyo
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1375

Abstract

Oil palm plantations on tropical peatlands require adaptive irrigation management due to unstable environmental conditions. This study proposes an IoT-based environmental monitoring and rainfall prediction system for irrigation optimization in oil palm plantations located in Palembang, South Sumatra, Indonesia. The proposed system integrates multiple environmental sensors with an ESP32 microcontroller for real-time monitoring and wireless data transmission. Historical environmental data were processed using the Long Short-Term Memory (LSTM) algorithm for short-term rainfall prediction under multiple forecasting scenarios. Experimental results demonstrated that the proposed model achieved stable learning performance and acceptable rainfall prediction accuracy. Overall, the proposed system shows potential for supporting smart irrigation management in peatland plantation areas.
Improving Cervical Precancerous Image Segmentation Using Pix2Pix Marsella Vindriani
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1383

Abstract

Cervical cancer remains a major health concern worldwide, particularly in developing countries where access to early screening and diagnosis is limited. Visual Inspection with Acetic Acid (VIA) is widely used for cervical cancer screening. However, its interpretation is highly dependent on medical expertise and image quality. Recent advances in deep learning have demonstrated promising performance in medical image segmentation, but their effectiveness is often constrained by limited dataset availability. This study proposes a Pix2Pix-based augmentation approach to improve cervical precancerous image segmentation performance. Cervical precancerous images obtained from RSUP Dr. Mohammad Hoesin Palembang and the International Agency for Research on Cancer (IARC) were annotated into three classes: cervical area, Columnar Area (CA), and lesion area. Two Pix2Pix augmentation configurations were evaluated using image resolutions of 256×256 and 640×640 pixels. The quality of generated synthetic images was assessed using Fréchet Inception Distance (FID), while segmentation performance was evaluated using YOLOv11-seg with Intersection over Union (IoU), Dice Coefficient, Pixel Accuracy, and mean Average Precision (mAP). Experimental results showed that the 640×640 Pix2Pix model achieved a substantially lower FID score of 25.6 compared to 266.0 for the 256×256 configuration, indicating superior image realism. Therefore, only the synthetic images generated by the 640×640 model were incorporated into segmentation training. The augmented dataset improved lesion segmentation performance, achieving increases of 16.4% in mAP50, 12.0% in IoU, and 14.7% in Dice Coefficient compared to the baseline model. These findings demonstrate that high-resolution Pix2Pix augmentation effectively enhances dataset diversity and improves the segmentation performance of YOLOv11-seg for cervical precancerous image analysis.
Simulation-Based Evaluation of Reliability and Data Continuity in IoT-Based Environmental Quality Monitoring Under Traffic and Failure Scenarios Andicho Haryus Wirasapta; Merisa Rahma Mawaddah; Muhammad Damas Fatih; Tiara Deta Pamungkas; Octo Gabriel Lumban Tobing; Zeeshan Hamid Malik
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1409

Abstract

Data continuity problems in environmental monitoring systems due to congestion in communication traffic, interference from wireless communications, and failure of components present a great problem in maintaining accurate and reliable data continuity and environment assessment results. This paper proposes the development of a simulation framework for evaluating the reliability and data continuity of the distributed IoT-based environmental quality monitoring system with different traffic intensities, wireless communication interference levels, and component failures. The reliability performance measures include: data delivery ratio (DDR), packet loss ratio (PLR), transmission delays, system availability, time in interruption state and data continuity ratio (DCR). Based on simulation analysis, the proposed distributed IoT environmental monitoring system has DDR and DCR of 0.971 when the network is under baseline operations. Under high traffic intensity, DDR was reduced to 0.734 while under high traffic and high interference conditions, DDR is reduced to 0.420 while PLR was 0.580. Under gateway component failure, DDR was reduced to 0.409 while under high traffic, high interference, and component failure condition, DCR was 0.487 or a 49.8% reduction from baseline. The recovery techniques have helped significantly increase the availability of the system when the environment is degraded. This indicates that the main factors that determine the reliability of IoT-enabled environmental monitoring systems are gateway resilience and communication efficiency.
Architectural Optimization Of A Lightweight Kubernetes Microservices Framework For The Toba Herbal Information System Under Extreme Resource Constraints Pandapotan Siagian
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

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

Abstract

This study designs, implements, and evaluates a lightweight, decoupled virtualization architecture utilizing K3s Kubernetes and Docker containers tailored for the Toba Herbal Information (THI). The system architecture isolates the web application logic, stateful relational databases, and high-velocity user connection sessions into three independent, resource-constrained pod tiers. Operating under a strict single-node hardware envelope of 4 CPU cores and 2 GB RAM, the framework enforces rigorous memory quotas and integrates an in-memory Redis caching buffer. Performance resiliency was rigorously evaluated via Apache JMeter against a baseline monolithic virtual machine (VM) utilizing a peak load of 1,000 concurrent healthcare users, complemented by an intentional Chaos Engineering failure injection. Empirical benchmarks demonstrate that the proposed 3-pod K3s framework successfully accommodated peak traffic with a stable 95th percentile (P_95) latency under 340ms and zero Out-Of-Memory (OOM) disruptions, safely capping RAM saturation at 87%. Conversely, the monolithic setup suffered a catastrophic kernel freeze at 400 concurrent users. Following a forced application pod termination, the K3s control plane achieved an exceptional Mean Time to Recover (MTTR) of 3.8 seconds with 100% transactional data integrity. This paper establishes a repeatable, cost-effective architectural blueprint proving that defensive microservices orchestration can successfully overcome physical hardware barriers to deliver enterprise-grade digital health platforms in resource-constrained rural regions.
Complex-Valued Representation of Relevance and Semantic Relations for Claim-Evidence Pairs with PSO Boundary Calibration Fadilah Nur Imani; Arsyelina Husni Johan
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1421

Abstract

This study constructs f(c,e), a complex-valued function in a polar form as a measure of relevance and semantic orientation between claims and evidence, where the modulus component represents semantic relevance and the angle component represents semantic orientation, which reflects the direction of the relationship between claims and evidence. The function is proven to have four mathematical properties: well-definedness, boundedness, continuity, and symmetry. Particle Swarm Optimization (PSO) is used to determine the angular boundaries that partition the semantic orientation space into support, not enough information, and refute relation zones. This approach ensures that the resulting zone boundaries reflect the characteristics of the empirical data distribution. The angular boundary calibration experiment was conducted using two datasets, namely SNLI and FEVER, and with two embedding models, namely all-MiniLM-L6-v2 and mxbai-embed-large-v1. The PSO results were evaluated using the F1-macro objective function. The obtained F1-macro ranged from 0.4105 to 0.59888, with the combination of SNLI and mxbai-embed-large-v1 producing the highest value. In this experiment, zone separation was descriptively clearer for SNLI than for FEVER, with SNLI exhibiting wider NEI zones and less overlap among relation distributions.
DDoS Detection in Smart Home IoT Networks: An Experimental Comparison of MLP and AE-MLP with Correlation-Based Feature Selection Nurul Afifah; Septiani Kusuma Ningrum; RA Siti Safa Marefa; Jihan Badiatus Shaliha; Hikmah Maharani; Ilma Sari; Andi Daniella
Computer Engineering and Applications Journal Vol. 15 No. 3 (2026)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i3.1422

Abstract

As the use of the Internet of Things (IoT) increases, the number of smart homes is rapidly increasing. But the increase in the number of connected devices increases the attack surface, making them vulnerable to Distributed Denial-of-Service (DDoS) attacks. The Autoencoder-Multilayer Perceptron (AE-MLP) is a hybrid deep learning model for DDoS attack detection in the smart home context. The Multilayer Perceptron (MLP) is proposed as the baseline model. The dataset is collected from a testbed of several IoT devices deployed in smart homes. Flow based features were extracted from network traffic in PCAP format using the CICFlowMeter. Data cleaning, feature selection with the correlation matrix and Min-Max normalization were the steps of data preparation process. We tried different data partition, optimizer, learning rate and epoch settings. Results showed that the performance of both models was greatly improved with feature selection. Before feature selection, the accuracy and F1-Score of the AE-MLP are 99.2% and 99.3% whereas the accuracy and F1-Score of the MLP are 99.5% and approximately 68%. The results indicate that feature selection plays a significant role in reducing noise and enhancing the performance of DDoS detection in smart home environments.

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