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ZIGBEE-BASED DATA ACQUISITION SYSTEM FOR ORNAMENTAL PLANTS Elisa, Nurul; Putra, Leonardus Sandy Ade; Marpaung, Jannus
Journal of Electrical Engineering, Energy, and Information Technology (J3EIT) Vol. 14 No. 1: April 2026
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/j3eit.v14i1.111311

Abstract

Ornamental plants require adequate soil moisture to grow optimally. However, manual irrigation is often performed irregularly, resulting in either insufficient or excessive water supply. This study aims to design and implement a ZigBee-based data acquisition system for monitoring ornamental plants and controlling irrigation automatically. The system employs a soil moisture sensor and a DS18B20 temperature sensor, an Arduino Nano as the transmitter controller, an XBee Series 2 module for ZigBee communication, an ESP32 as the receiver controller and Internet gateway, and the Blynk application for real-time monitoring. The system was tested on two potted ornamental plants, while ZigBee communication performance was evaluated at distances ranging from 20 to 120 meters using the Received Signal Strength Indicator (RSSI), packet delay, and Packet Loss as performance parameters. The results show that the system is capable of monitoring temperature and soil moisture, displaying real-time data on the Blynk application, and automatically activating irrigation based on the predefined soil moisture threshold. ZigBee communication was successfully maintained up to 120 meters, with RSSI values ranging from 75.14% to 43.08%, packet delay ranging from 456 to 4663 ms, and a maximum Packet Loss of 43.6%. These results indicate that the proposed system is effective for automatic monitoring and irrigation of ornamental plants, although the communication performance decreases as the transmission distance increases.
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.