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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 6 Documents
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Search results for , issue "Vol. 14 No. 2 (2025)" : 6 Documents clear
Exploration U-Net Architecture for Cervical Precancerous Lesions Segmentation Arum, Akhiar Wista; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Firdaus; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Ananda, Dea Agustria
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

The automatic analysis of images for the early detection of cervical cancer relies on the segmentation of cervical precancerous lesions. This paper investigates the incorporation of various CNN-based backbones into a U-Net model for improved segmentation accuracy. A set of twelve backbones was tested, including VGG16, VGG19, ResNet50, ResNext50, EfficientNetB7, InceptionResNetv2, DenseNet201, InceptionV3, MobileNet V2, SE-ResNet50, SE-ResNext50, and SE-Net154. Evaluation metrics were computed using Intersection over Union, pixel accuracy, and Dice coefficient. The findings demonstrate that U-Net with EfficientNetB7 backbone outperforms all other models with an IoU of 73.13%, pixel accuracy of 89.92%, and a Dice coefficient of 77.64%. These results were visually confirmed; segmentation outputs were examined, showing accurate delineation of lesion borders. The dominating performance of EfficientNetB7 was observed to be due to high feature extraction efficiency coupled with powerful spatial information representation. The study is, however, limited by a lack of clinical validation and expert evaluation from trained medical personnel. The results demonstrate the effectiveness of combining the U-Net architecture with advanced CNN backbones towards designing automated systems to analyze medical images.
Cervical Pre-cancer Classification Using MLP Based on Hybrid Features from GLCM, LBP, and MobileNetV2 Suhandono, Nugroho; Nurmaini, Siti
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

The early and accurate diagnosis of cervical intraepithelial neoplasia lesions (CIN), particularly in a resource-limited environment, is paramount in helping to control the rising epidemic of cervical cancer. This research offers a hybrid classification model that merge texture features like Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP), alongside semantic features from MobileNetV2. These features, after being extracted, are merged and supplied to a Multilayer Perceptron (MLP) for multiclass classification into Normal, CIN1, CIN2, or CIN3. The model was trained and evaluated using a 5-fold stratified cross-validation technique on an IARC dataset that contains 200 cases of colposcopy images. The experimental results illustrate that the model developed with a stratified k-fold cross-validation performed consistently well with high performance, average accuracy reported as 86.75% ± 2.62% and Cohen's kappa 0.7963 ± 0.0524 showed substantial to almost perfect in agreement across folds. The best performance was recorded for Fold 4 achieving 90.31% accuracy, while maintaining robust F1-scores across all classes. This hybrid approach offers a promising direction for developing efficient and accurate computer-aided diagnosis (CAD) systems for cervical lesion classification.
Deep Learning for ECG-Based Arrhythmia Classification Based on Time-Domain Features Sari, Ririn Purnama; Darmawahyuni, Annisa; Tutuko, Bambang; Firdaus; Rachmatullah, Muhammad Naufal; Sapitri, Ade Iriani; Islami, Anggun; Arum, Akhiar Wista
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Arrhythmia is a disturbance in the electrical activity of the heart that can affect the rhythm and duration of the heartbeat. Early detection of arrhythmia is crucial to prevent more serious complications. Electrocardiogram (ECG) is an effective non-invasive diagnostic tool in detecting arrhythmia, but manual detection by experts takes time. To overcome this limitation, this research develops an arrhythmia classification system by utilizing deep learning. This study involves a series of stages, starting from pre-processing, feature extraction, and arrhythmia classification models using convolutional neural networks (CNN) and long short-term memory (LSTM). The results showed that feature extraction successfully improved model efficiency and accuracy. Evaluation of model performance using accuracy, recall, precision, specificity, and F1-score metrics showed that the LSTM model achieved 95% accuracy, 96% recall, 96% precision, 99% specificity, and 96% F1-score, outperforming the CNN model which achieved 91% accuracy, 90% recall, 89% precision, 98% specificity, and 89% F1-score. Thus, these results indicate that the LSTM model is superior in arrhythmia classification.
Improving Low-Cost Single-Phase Inverter Performance using DRL-Based Control System: Experimental Validation Jambak, Muhammad Irfan; Sidik, Muhammad Abu Bakar; Dinata, Noer Fadzri Perdana
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

This paper presents the improvement of a low-cost, single-phase pure sine wave inverter controlled by a deep reinforcement learning (DRL) agent. The study addresses the challenge of lacking performance of low-cost inverter, which is primarily due to the stability requirements of conventional control strategies. A DRL- based control approach is proposed to enhance voltage and frequency stability while reducing the need for extensive manual tuning. The system is validated through both simulation and experimental verification in a microgrid islanded configuration. The results demonstrate that the DRL-based inverter effectively maintains 220 VRMS at 50 Hz, achieving a stable root mean square voltage of 219.8 V, and a total harmonic distortion (THD) below 8%. The use of DRL making it an attractive solution for renewable energy systems, off-grid applications, and rural electrification. This study highlights the feasibility of DRL in power electronics and suggests that further optimization of training generalization and computational efficiency could enhance real-time and grid-tied deployment. The findings contribute to the advancement of intelligent inverter control, offering an alternative for next-generation microgrid and distributed energy systems.
Implementation of Weightless Neural Network in Embedded Face Recognition for Eye and Nose Pattern Mobile Identification Zarkasi, Ahmad; Exaudi, Kemahyanto; Sazaki, Yoppy; Romadhona, Londa Arrahmando
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

The pattern of the human face is a form of self-identity and also a form of originality for each individual. The development of facial recognition technology impacts its application in various computing devices, both in computer vision and on single-chip processors. One of the continuously developed implementations is in the form of robot vision by identifying facial features. This research aims to develop a facial recognition system focusing on the identification of the eye and nose areas. This research utilizes the Weightless Neural Network (WNN) method with the Immediate Scan technique. The combination of methods allows for rapid and accurate pattern recognition, even when the face changes position. The detection process is carried out using the Haar Cascade Classifier algorithm, which functions to recognize faces and divides the area into nine different zones to ensure accurate identification. The hardware implementation was carried out on a Raspberry Pi for face detection and facial pattern recognition, as well as the data processor for the robot vision sensor and actuator on the microcontroller. The results of the robot's movement testing have worked well according to the calculation of GPS data values to determine the robot's last position. Then, in the face pattern recognition process, it shows that the proposed method can achieve a maximum accuracy level of up to 98.87% in testing with the internal data set, while testing under different conditions experiences a slight decrease in accuracy to 91.38%. The highest similarity percentage to the faces of other individuals reached 75.69%, indicating that this method is quite adaptive to various facial variations. The execution time of the identification process ranges from 11 ms to 17 ms, depending on the amount of data compared during the scanning. This research is expected to serve as a foundation for further development in robotics systems and embedded system-based facial recognition.
Implementation of Feature Selection for Optimizing Voice Detection Based on Gender using Random Forest Abdurahman; Vindriani, Marsella; Prasetyo, Aditya Putra Perdana; Sukemi; Buchari, M. Ali; Sembiring, Sarmayanta; Firnando, Ricy; Isnanto, Rahmat Fadli; Exaudi, Kemahyanto; Dudifa, Aldi; Riyuda, Rafki Sahasika
Computer Engineering and Applications Journal (ComEngApp) Vol. 14 No. 2 (2025)
Publisher : Universitas Sriwijaya

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Abstract

Gender-based voice detection is one of the machine learning applications that has various benefits in technology and services, such as virtual assistants, human-machine interaction systems, and voice data analysis. However, the use of too many features, including irrelevant features, can cause a decrease in accuracy and model performance. This research aims to optimize voice-based gender detection by applying a feature selection method to select significant features based on their correlation value to the target. Experimental results show that by using only the significant features selected through correlation analysis, the accuracy of the model is significantly improved compared to using all available features. This research confirms the importance of feature optimization to support the development of more efficient and accurate gender-based speech detection models.

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