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Leonardus Sandy Ade Putra
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INDONESIA
Telecommunications, Computers, and Electricals Engineering Journal
Published by Universitas Tanjungpura
ISSN : -     EISSN : 30260744     DOI : https://dx.doi.org/10.26418/telectrical.v1i2
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering
Signal Processing; Communication Networks; Artificial Intelligence, Computer Technology; Power Systems; Image Processing
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 5 Documents
 1 
Search results for , issue "vol. 3 no. 3: february 2026" : 5 Documents clear
Implementation of Automatic Feeding and Monitoring of Pet Development Based on Telecommunication Networks and Android Valioenza, Difa
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.100661

Abstract

Increasingly busy daily activities make it difficult for pet owners to feed their pets on time and monitor their development. This can cause discomfort for owners and potential health problems for pets. How can feeding be automated and anabul (fur babies) development be monitored so that pet owners can more easily and efficiently care for their pets amidst their busy schedules? This study aims to design and develop an automatic feeding device and anabul development monitoring system based on telecommunication networks and Android. The designed device is capable of monitoring anabul development by periodically measuring their weight. Data obtained over a week show that the anabul's weight increased well from Monday to Sunday. Additionally, the device's food level reading functions well over the course of a week, ensuring that feeding occurs according to the set schedule.
A Compact Dual-Band Cross-Notched T-Shaped Antenna for High-Efficiency 2.4/5.8 GHz Wi-Fi Energy Harvesting in Self-Powered IoT Devices Ansah, John Nyamekye
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.101650

Abstract

This paper presents a dual-band cross-notched T-shaped monopole antenna developed for efficient ambient RF energy harvesting in the 2.4 GHz and 5.8 GHz Wi-Fi bands. The antenna occupies a compact size of 40 40 1.6 mm³, making it suitable for space-constrained IoT and biomedical devices. The proposed antenna demonstrates excellent impedance matching, achieving S11 values of -23.225 dB at 2.4 GHz and -29.096 dB at 5.8 GHz, with corresponding VSWR values of 1.1483 and 1.0727, respectively. It achieves wide impedance bandwidths of 0.71 GHz (2.07–2.78) GHz and 1.78 GHz (5.07–6.85) GHz, yielding fractional bandwidths of 29.28% and 29.87%, ensuring stable operation across both Wi-Fi bands. The antenna measures gains of 2.10 dBi and 4.29 dBi, with directivities of 2.52 dBi and 4.58 dBi, supported by high radiation efficiencies of 90.78% and 93.43% at the two resonant frequencies. The cross-notched T-shape configuration contributes significantly to achieving compactness and enhanced electromagnetic performance, while strategically positioned rectangular notches at the upper-left and lower-right corners are instrumental in achieving dual-band operation. The results demonstrate the antenna’s strong suitability for RF energy harvesting, where high efficiency and wide impedance bandwidth are essential for maximising RF captured power. In summary, the proposed antenna provides a balanced combination of compact size, high radiation efficiency and broad bandwidth, establishing it as a robust candidate for the next-generation battery-less IoT and low-power medical systems.
Implementation of the YOLOv11 Algorithm on Guppy Ornamental Fish Based on Android Suhaimi, Rendy
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.102129

Abstract

This study implements the YOLOv11 algorithm to detect five types of ornamental guppy fish through an Android-based application. The research background arises from the difficulty of manually distinguishing guppy varieties due to their complex color variations and patterns. The methodology includes dataset collection, labeling using Roboflow, image preprocessing and augmentation, training the YOLOv11n model, conversion to TensorFlow Lite, as well as real-time implementation and testing within the application. The training results demonstrate strong performance, achieving a Precision of 85.90%, Recall of 90.70%, mAP50 of 90.60%, mAP50–95 of 66.40%, and an F1-Score of 88.3%. Indirect testing on 250 test images produced per-class accuracy ranging from 94% to 98%. Direct real-time testing indicates that distance and fish orientation significantly influence confidence scores: at 10 cm the confidence reached 76.6% (straight) and 71.6% (turned), at 15 cm it reached 83% (straight) and 75.4% (turned), and at 20 cm it reached 72.8% (straight) and 47.4% (turned). The optimal performance was obtained at a distance of 15 cm. The application is capable of detecting objects within 1–3 seconds, making it suitable for real-time guppy identification. This study can be further improved by expanding the dataset, optimizing the model, and adding additional application features. Keywords: YOLOv11, Guppy Fish Identification, Object Detection, Image Processing, Android Application
Analysis of the Characteristics of Digital Image Authenticity Forensic Methods Using Error Level Analysis, Noise Analysis, and Clone Detection Adianto, Hafiz; Putra, Leonardus Sandy Ade
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.111086

Abstract

Digital image manipulation has become increasingly difficult to identify through visual inspection, creating a need for digital forensic methods capable of objectively verifying image authenticity. This study aims to analyze the characteristics and compare the performance of Error Level Analysis (ELA), Noise Analysis, and Clone Detection in detecting image splicing and copy-move forgery. The study used 500 digital images, consisting of 250 original images and 250 manipulated images, which were analyzed using the three forensic methods through a Python-based application and evaluated based on their detection success rates. The results show that ELA achieved detection rates of 70.40% for image splicing and 64.00% for copy-move forgery, while Noise Analysis achieved 39.20% and 28.00%, respectively, and Clone Detection achieved 46.40% and 81.60%. These findings indicate that ELA is more effective for detecting compression-based manipulation, Clone Detection performs better in identifying copy-move forgery, whereas Noise Analysis serves as a complementary method for analyzing inconsistencies in noise patterns.
Application of Rice Plant Image Processing for Disease Identification and Classification Using YOLOv11 Arisqi, Wais
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 3 No. 3: February 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v3i3.111124

Abstract

Rice is one of the main food commodities in Indonesia and plays an important role in meeting the food needs of the population. However, rice productivity is often reduced due to various leaf diseases, such as Bacterial Leaf Blight, Blast, and Brown Spot. This study aims to develop an artificial intelligence-based rice leaf disease detection system using the YOLOv11 algorithm implemented on an Android application. The method used in this study includes collecting a rice leaf image dataset consisting of three disease classes, namely Bacterial Leaf Blight, Blast, and Brown Spot. The dataset was then annotated and used to train the YOLOv11 model. After the training process was completed, the model was evaluated using Precision, Recall, mAP50, mAP50-95, and F1-Score metrics. The trained model was subsequently converted into TensorFlow Lite format to enable real-time detection on Android devices. The experimental results showed that the model achieved a Precision of 75.4%, Recall of 71.4%, mAP50 of 77.4%, mAP50-95 of 42.1%, and an F1-Score of 73%. Based on class-wise testing using 100 test images for each disease class, the model obtained accuracies of 88% for Bacterial Leaf Blight, 74% for Blast, and 65% for Brown Spot. The implementation of the model on the Android application successfully displayed detection results in the form of bounding boxes, disease labels, and confidence scores in real time. The YOLOv11 algorithm is capable of detecting rice leaf diseases with satisfactory performance and can be effectively implemented on Android devices using TensorFlow Lite.

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