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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 5 Documents
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Search results for , issue "Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal" : 5 Documents clear
Drift-Resilient IoT Energy Monitoring for Low-Cost Voltage and Current Sensors Sulaiman, Abdullahi; Ayodele Isqeel, Abdullateef; Issa , Abdulkabir Olatunji; Issa, Abdulrasheed Yinka; Agbolade, Onasanya Mobolaji
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i1.1325

Abstract

Low-cost voltage and current sensors such as the ZMPT101B and ACS712 are widely used in IoT-based energy monitoring due to their affordability and ease of integration. However, their outputs suffer from drift caused by thermal variation, material degradation, and electromagnetic interference, leading to cumulative errors that compromise load monitoring, forecasting, and anomaly detection. This work presents a drift-resilient framework that integrates lightweight filtering and regression-based calibration into a unified pipeline deployable on ESP32-class devices. Moving average and adaptive Kalman filters suppress noise and track drift trends, regression models align sensor outputs with reference standards, and spectrogram-based analysis detects transient drift events for adaptive correction. Experiments under realistic conditions show substantial improvements: voltage RMSE decreased by over 90% (3.45V to 0.30V), current RMSE by 92% (0.065A to 0.005A), and MAPE to below 0.5%. Signal-to-noise ratio improved by approximately 21dB, confirming significant restoration of measurement fidelity. Compared with data-intensive deep learning or AutoML frameworks, the proposed method offers a scalable, interpretable, and resource-efficient solution for long-term IoT energy monitoring. By bridging drift mitigation strategies with the practical constraints of low-cost sensors, this framework enhances the reliability of smart grid and IoT-based infrastructures.
Application of Additive Manufacturing Technology in Custom Surgery and Orthopedic Implants through 3D Bioprinting: Rapid Review Karmilah; Della Afrilliani Sutaryo; Raina Azhari Nariswari; Salma Fajrian Agustin; Riva Nurizkiah; Tanti Intan Nurhayati; Nunung Siti Sukaesih
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v14i1.1326

Abstract

Musculoskeletal disorders, which are the leading cause of global disability, require better implant reconstruction solutions, given the limitations of conventional implants in terms of anatomical fit, stability, and stress shielding risk. The objective of this rapid review is to summarize the latest evidence on the application of Additive Manufacturing (AM), 3D Printing, and 3D Bioprinting technologies in the manufacture of custom orthopedic implants. The method used was a Rapid Review with the PRISMA framework, which involved searching 3,291 articles in the PubMed and ScienceDirect databases and filtering them down to 14 selected articles. The results show that the integration of 3D imaging, 3D printing, and Artificial Intelligence (AI) significantly improves visual-spatial understanding in orthopedic education, as well as improves implant placement accuracy (e.g., in THA), reduces operating time, blood loss, and radiation exposure through the use of AI-based 3D preoperative planning, custom models, and 3D-printed surgical guides. However, challenges remain in terms of cost, preoperative production time, and lack of long-term follow-up data. In conclusion, 3D and AI technologies have revolutionized orthopedic practice by improving accuracy, efficiency, and personalization of therapy, requiring large-scale research and long-term evaluation for sustainable clinical implementation.
Implementation of Color Matching in Ball Image Processing Using OpenCV Awah Rizqi Tanzil Haq Azami
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal
Publisher : Universitas Sriwijaya

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

Abstract

This research focuses on object detection through color matching, enabling machines to detect objects based on specific colors using OpenCV, a Python library widely used in computer vision projects. As a form of Machine Learning and Artificial Intelligence, this method allows machines to automatically learn and classify objects by simulating the human visual system. The study aims to enable a machine to detect and locate a ball through digital image processing using a webcam. The research method includes digital image processing, implementation on a Raspberry Pi, and testing on a robot, where logic is applied to guide the robot toward the ball by detecting its color. The outcome is an object detection system that identifies the ball’s position in two dimensions based on its specific color. In this case, the RGB code (164, 122, 0) and a minimum ball size of 10 radians were successfully implemented on the robot. However, the system has limitations under certain conditions. Future improvements will involve integrating TensorFlow for dataset processing and OpenCV for real-time object detection to achieve more accurate results. Keywords: Artificial Intelligence, Color Matching, Computer Vision, OpenCV, Digital Image Processing.
Air Quality Monitoring System based on the TI MSP430 Microcontroller Family Kommey, Benjamin; Tamakloe, Elvis; Ato-Sam, Nathaniel; Agyekum, Kwame Agyeman-Prempeh
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i1.1330

Abstract

Air quality monitoring devices are generally sensor circuits coupled with signal processing devices, where the output signal provides intelligible information to users. In this project report, a low-cost portable air quality monitoring device based on the TI MSP430G2553 microcontroller is described and designed. The device can monitor the air quality in one’s immediate environment and hence it gives individuals an idea of how clean or polluted the air in their surroundings is. A design is presented which applies basic gas sensing techniques and analog-to-digital conversion (ADC) principles to achieve the needed functionality. The device is built with off-the-shelf components, which are easy to comprehend and assemble. The device can detect the presence of ammonia (NH3), nitrogen oxides (NOx), benzene (C6H6), Carbon dioxide (CO2), smoke, and other hazardous gases and it is powered by a dc supply voltage ranging between +7V and +12V.
Detection of Ventricular Septal Defect in Pediatric Cardiac Ultrasound Videos Using Parasternal View and Faster R-CNN Nasrudin, Muhammad; Shindi Shella May Wara; Amri Muhaimin; Nur Indah Nirmalasari; Mega Rizkya Arfiana
Computer Engineering and Applications Journal Vol. 15 No. 1 (2026): Computer Enginering and Applications Journal
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v15i1.1334

Abstract

Congenital heart disease (CHD), particularly ventricular septal defect (VSD), remains a major contributor to pediatric morbidity, while echocardiographic diagnosis is highly dependent on operator expertise and image quality. This study examines the feasibility of an object-detection-based intelligent imaging framework for localizing VSD in pediatric cardiac ultrasound videos acquired from the parasternal long-axis view. Rather than proposing a novel detection algorithm, this work adopts a system-oriented approach by evaluating the Faster R-CNN framework under practical clinical constraints, including limited annotated data and heterogeneous ultrasound characteristics. Three convolutional neural network backbones such as ResNet50, ResNet101, and Inception-ResNet V2 are comparatively analyzed within a unified detection pipeline. Experimental results indicate that the ResNet101-based model achieves the highest localization performance at an intersection-over-union threshold of 0.5, while ResNet50 provides more consistent precision across stricter localization thresholds. Although false-positive detections are observed in acoustically challenging frames, the proposed framework maintains real-time feasibility at approximately 7–8 frames per second. The findings offer practical insights into accuracy–efficiency trade-offs and backbone selection for the development of clinically aware intelligent echocardiography systems, supporting the application of information and communication technology in pediatric cardiac imaging.

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