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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 78 Documents
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Smart Hybrida Power Hydroponics Based On The Internet Of Things Mumtaza, Habil; Syaifurrahman, Syaifurrahman; Hadary, Ferry
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 4 No. 1: June 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v4i1.94752

Abstract

The hydroponic system is a method of cultivating plants without using soil, but instead utilizing nutrient solutions as a planting medium. However, monitoring and controlling important parameters such as TDS, pH, temperature, and water level manually is considered inefficient and prone to errors. To overcome this, this study designed and implemented an intelligent hydroponic system based on the Internet of Things (IoT) that is able to monitor and control the quality of nutrient solutions automatically. This system uses an ESP-32 microcontroller connected to several sensors, namely a TDS sensor (SEN0244), pH sensor (SEN0161), temperature sensor (DS18B20), and ultrasonic sensor (A02YYUW). For control, actuators are used in the form of peristaltic pumps and solenoid valves. All data from the sensors is displayed in real-time via LCD and a smartphone application connected to the Firebase database. This system is also designed with a hybrid power source, namely utilizing PLN electricity as the main source and solar power as a backup through the Automatic Transfer Switch (ATS) system. The test results show that the system works well, as evidenced by the accuracy of the TDS sensor of 99.35%, the pH sensor of 97.16%, the temperature sensor of 96.23%, and the ultrasonic sensor of 97.02%. With automatic monitoring and control capabilities, this system is considered capable of increasing efficiency and effectiveness in sustainable hydroponic cultivation.
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
Implementation of MAC Address Filtering as a Security System for Wireless Network Protection Anugrah, Aditya Bayu
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 4 No. 1: June 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v4i1.103020

Abstract

Wireless networks are highly vulnerable to security threats, particularly unauthorized access. PT Borneo Cakrawala Media currently relies on the WPA2-PSK security mechanism, which has certain limitations. This study aims to implement MAC Address Filtering as an additional security mechanism for a wireless network. The research methodology includes the design, configuration, and testing of MAC Address Filtering using a Mikrotik RB750GR3 router and a Ruijie RG-720L access point. The tests were conducted on registered and unregistered device access, device addition and removal, false positive and false negative cases, MAC Address Spoofing handling, and network performance evaluation using Quality of Service (QoS) parameters based on the TIPHON standard. The results indicate that MAC Address Filtering is capable of accurately verifying registered devices and rejecting unauthorized devices, managing MAC Address addition and removal, and handling false positive and false negative cases with a 100% success rate without significantly degrading network performance. Furthermore, the MAC Address Spoofing tests demonstrate that the system can distinguish devices even when identical or similar MAC Addresses are used. It can be concluded that MAC Address Filtering is effective as an additional security mechanism for protecting wireless networks.
Detection of Alligator Crack Types on Asphalt Roads Using Digital Images Basri, Syafalah
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 4 No. 1: June 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v4i1.103235

Abstract

Alligator cracking, also known as fatigue cracking, is a series of interconnected cracks that occur on the surface of asphalt concrete layers as a result of fatigue failure caused by repeated vehicle loads. This type of cracking is classified into three levels, namely low, medium, and high severity. However, the detection of road cracking is still commonly performed manually, which becomes an obstacle in accurately identifying alligator cracking conditions. To address this issue, this study develops an algorithm using YOLOv11 implemented in an Android application to facilitate the identification of low, medium, and high alligator cracking. The proposed method includes dataset collection, data labeling, preprocessing, data augmentation, YOLOv11 model training, implementation of the TensorFlow Lite (tflite) model into the application, and real-time testing. The experimental results show that the developed model achieves a Precision of 83.4 percent, Recall of 83.3 percent, mAP50 of 83.1 percent, mAP50–90 of 59.3 percent, F1-Score of 83.3 percent, and an indirect testing accuracy above 90 percent. Based on these results, it can be concluded that the developed application is capable of accurately identifying alligator cracking in real time.
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.
Design and Construction of an Elderly Activity Monitoring System Based on ESP32-CAM and PIR Sensor with Real-Time Notifications via Firebase Application rismar, rismariansyah
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 4 No. 1: June 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v4i1.111129

Abstract

The increasing risk of falls among the elderly necessitates the development of reliable monitoring systems for timely intervention. This study presents the design and development of an Internet of Things (IoT)-based activity monitoring system utilizing an ESP32-CAM, a PIR HC-SR501 sensor, and an Active Photoelectric Single Infrared Beam sensor, with data managed via a Firebase Realtime Database. The system’s logic employs an AND condition, whereby the camera is activated only upon the simultaneous detection of human presence by the PIR sensor and an interruption of the infrared beam exceeding five seconds, indicating a potential fall or abnormal event. Performance evaluation was conducted through sensor detection tests and an analysis of communication quality, assessed by Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Packet Delivery Ratio (PDR), and packet loss metrics. The experimental results demonstrate that the PIR sensor consistently detects motion at distances of 1 to 4 meters with 100% accuracy, while the infrared beam sensor operates effectively up to a 4-meter range. All captured images and notifications were successfully transmitted to the Firebase database, achieving an average response time of 3.72 seconds, a PDR of 100%, and 0% packet loss. These findings confirm that the proposed system is a robust and reliable solution for monitoring elderly activities and delivering real-time notifications in indoor settings, thereby offering a viable tool for enhancing elderly safety and facilitating prompt family intervention.
Application of Long Distance Communication for Corn Seed Drying Control and Monitoring Sistem Hendra, Hendra
Telecommunications, Computers, and Electricals Engineering Journal (TELECTRICAL) Vol. 4 No. 1: June 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/telectrical.v4i1.111202

Abstract

The drying process of corn kernels, which primarily depends on solar energy, is significantly affected by unpredictable weather conditions, especially rainfall, leading to a decline in the quality of the harvested corn. This study aims to design and implement a long-range communication system for an Internet of Things (IoT)-based monitoring and control system for corn kernel drying. The developed system employs an ESP32 microcontroller as the transmitter to acquire data from the DHT11 temperature and humidity sensor and the rain sensor, as well as to control a DC motor through an L298N motor driver. Meanwhile, an ESP8266 microcontroller functions as the receiver, receiving data via the LoRa SX1278 communication module and transmitting it to the Thinger.io platform over the Internet. The system is capable of operating in both automatic mode based on sensor readings and manual mode through the Thinger.io dashboard. The communication performance parameters analyzed include the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and communication delay at various testing distances. The experimental results indicate that the LoRa communication system performed successfully at communication distances of 100 m, 200 m, and 250 m. At a distance of 100 m, the average RSSI, SNR, and communication delay were −80.8 dBm, 3.5 dB, and 1800 ms, respectively. At a distance of 200 m, the average RSSI, SNR, and communication delay were −89.3 dBm, −5.625 dB, and 3000 ms, respectively. Although signal quality deteriorated with increasing communication distance, the transmitted data were still received reliably, enabling the monitoring and control system to maintain real-time operation.

Page 8 of 8 | Total Record : 78

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