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Journal of Information Technology and Computer Engineering
Published by Universitas Andalas
ISSN : 25991663     EISSN : -     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
Journal of Information Technology and Computer Engineering (JITCE) is a scholarly periodical. JITCE will publish research papers, technical papers, conceptual papers, and case study reports. This journal is organized by Computer System Department at Universitas Andalas, Padang, West Sumatra, Indonesia.
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Articles 186 Documents
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Development of a Multi-Task Learning CNN Model for Pneumonia Detection and Pathogen Classification Based on Medical Images Harahap, Aris Munandar; Samosir, Khairunnisa
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Pneumonia is one of the leading causes of death from respiratory tract infections worldwide. Early detection and identification of the causative pathogen are crucial for determining appropriate treatment. This study aims to develop a Convolutional Neural Network (CNN) model based on Multi-Task Learning (MTL) to simultaneously detect pneumonia and classify the type of pathogen through chest X-ray images. The model architecture uses a shared convolutional layer for feature extraction, which then branches into two classification paths. The model was trained using a dataset of X-ray images labeled with disease status and pathogen type, with two loss functions optimized simultaneously. Based on the training process and model architecture design, the estimated accuracy achieved is approximately 92% for pneumonia detection and 89% for pathogen type classification. These results indicate that the CNN-MTL approach is effective and efficient in simultaneously addressing two clinical tasks. The proposed model has the potential to be applied as a clinical decision support system, particularly in healthcare facilities with limited resources.
- Development of an Automated Water Turbidity Monitoring and CO₂ Injection System for Aquascape Environments: - Rio, Muhammad -; Kasoep, Werman; Yolanda, Desta
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

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Abstract

The purpose of this research is to monitor water turbidity and automatically inject CO₂ into the aquascape, enabling aquascape enthusiasts to identify potential issues related to water quality and plant health when the system is left unattended. Key concerns include water turbidity, lighting conditions, and the distribution of CO₂ in the water. Therefore, a real-time monitoring and automated CO₂ injection system is required. The results of this study show that the system is capable of monitoring water turbidity with an average error of 2.77% and regulating CO₂ distribution based on the predefined schedule. At 13:00 WIB, the lighting is activated, and the solenoid valve opens. At 21:00 WIB, the lighting is turned off, and the solenoid valve closes. If the water becomes turbid, the buzzer automatically activates, and a notification is sent to the user via Telegram.
Leveraging Microcontroller-Based approach on Automatic pH Control, Monitoring, and Feeding System for Catfish Aquaculture. Azmi, Ahmad Nurul; Hersyah, Mohammad Hafiz
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 1 (2025)
Publisher : Universitas Andalas

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Abstract

Fisheries cultivation is beginning to experience improvement and enthusiasm within the community. Especially the cultivation of catfish, which is gaining quite a lot of enthusiasts. This is caused by catfish that are easy to cultivate, have high demand within the community, and are rich in protein. The increase in catfish enthusiasts is based on the period from 2018 to 2019, during which the growth of catfish has increased by 27.76%. The increase in catfish production reached 1,005,530 tonnes in 2018 and 1,224,360 tonnes in 2019.Along with the advancements in technology for cultivating feed catfish, which meet the energy and growth needs, there should be sufficient resources to achieve a maximum harvest. However, lately, many cultivators have failed. This is due to several problems, including the intensity of irregular feeding and unstable catfish pool water levels, which resulted in the growth of catfish not yet fully implemented by cultivators. Consequently, many catfish cultivators suffered losses. Technology will help improve the quality of catfish feed. The basic principle of catfish cultivation is how the system can maintain a suitable water quality for catfish so that the risk of death does not occur because too Low or too high pH can have a harmful effect on catfish. pH itself is the degree of acidity used to declare the level of acidity or pH that a solution has. The components used in the design of this tool include pH sensors to read acid levels in catfish pool water, selenoids for controlling the flow of pH-adjusted liquid, load cell sensors for calculating the weight of the feed, and a servomotor to open the fish feed valve
Low-complexity Automatic Modulation Classification of Higher-order QAM Based on Square Modulus Extraction Bello, Olalekan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Modulation classification plays a key role in decoding cognitive radio, signal identification, menace assessment, spectrum senses, and management, efficient use of available spectrum and increase in the speed of data transfer. Quadrature Amplitude Modulation (QAM) has become an important modulation scheme used in most civilian and military applications. However, algorithms developed so far for these purposes have been limited in classifying higher-order QAM and are also extremely complex. Applications which need to take real-time critical decision based upon modulation types information require that an automatic modulation classification (AMC) algorithm is necessarily simple both in cost and in implementation. This paper, therefore, proposes a novel low-complexity feature-based (FB) method based on evaluating the square modulus of the baseband demodulated received signal, as the only discriminating feature, to classify QAM of any modulation order. Results show, in the presence of combined effects of the carrier phase deviations, timing offset, multipath interference and AWGN, that all QAM modulation types up to 2048-QAM achieve 100% classification accuracy at lower than 10 dB of SNR. The classification algorithm is thus robust in accurately classifying any QAM modulation type even in the presence of combined effects of the common distortions on the received modulated signal.
Begal-Detector: A Real-Time Street Crime Detection Framework Combining Human Activity Recognition and Object Detection on Raspberry Pi Prastyo, Pulung Hendro; Agung, Ismi Batari; Ikram, Ahmad Fadahil; Pratama, Muhammad Herlan; Mandasari, Nia
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Currently, street crime remains a serious challenge in Indonesia, while conventional CCTV systems still function passively as recorders. One of the most concerning types of crime is robbery with violence, commonly known in Indonesia as begal, which remains among the most frequently reported cases. This study proposes the Begal-Detector, a YOLOv8-based system that integrates Human Activity Recognition (HAR) and Object Detection to identify suspicious activities in real time on edge devices. The experiments were conducted on Raspberry Pi 4, Raspberry Pi 5, and Raspberry Pi 5 with Hailo AI Kit, with variations in distance, camera angle, and lighting conditions. The test dataset consisted of 72 video samples, including both street crime and non-street crime scenarios, recorded using the EZVIZ H8C Outdoor CCTV camera. Experimental results show that the Begal-Detector performs very well, achieving a 100% detection accuracy at a distance of 2 meters, 94% at 3 meters, and 94% at a 45° camera angle. Under low-light conditions supported by infrared light, the system maintained an accuracy of up to 79%, making it feasible for real-world deployment. In terms of hardware performance, the Raspberry Pi 5 with Hailo AI Kit provided the most optimal results, achieving an average of 52.71 FPS with a stable temperature of 63 °C, significantly outperforming the Raspberry Pi 4 and Raspberry Pi 5 without an accelerator, both of which failed to operate the system in real time. The findings confirm that utilizing Raspberry Pi 5 with Hailo AI Kit is an effective solution to ensure that the Begal-Detector operates quickly, stably, and reliably.
Food Classification and Monitoring System in Refrigerators Using YOLO Algorithm Husna, Laellatul; Ferdian, Rian; Purbolingga, Yoan
JITCE (Journal of Information Technology and Computer Engineering) Vol. 9 No. 2 (2025): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

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Abstract

Monitoring food freshness in refrigerators remains a challenge for many users, often leading to food spoilage and waste due to the absence of an automatic monitoring system. This study proposes a computer vision–based food monitoring system that leverages the YOLOv5 algorithm to automatically detect and categorize food items through camera input and deliver real-time notifications to users via a connected application. Experimental results demonstrate that YOLOv5 achieves an average accuracy of over 90% across various distances and object positions. Despite challenges related to limited datasets and lighting variations inside the refrigerator, the system offers a practical and innovative solution to reduce food spoilage, minimize household food waste, and support more efficient food storage management.

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