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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Electro Luceat ILKOM Jurnal Ilmiah JURNAL IT: Media Infromasi STMIK Handayani Makassar E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal Rekayasa elektrika
Matalangi, Matalangi
Universitas Handayani Makassar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Anomaly Detection for Security in Children's Play Areas Based on Image Using Multiple Lines Detection Method Wahyuningsih, Pujianti; Matalangi, Matalangi; Fadhil Sukiman, Muhammad Nur; Mahenra, Yusril
Jurnal Rekayasa Elektrika Vol 20, No 1 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i1.34836

Abstract

This study aims to build a device as a security system to detect anomalies of children moving in play areas based on the Multiple Line Detection (MLD) method in a streaming image. We developed this device to help parents monitor their children's activities when playing in dangerous areas of the home to protect children from kidnapping. In this study, the MLD method can detect the children's activities when playing in three zones: the safe zone with green lines in the image, the caution zone with yellow lines, and the danger zone with red lines. The hardware used to build the devices in this study consists of three components: a camera to stream the image activities of children, a Raspberry Pi to process the image using OpenCV, and a buzzer for early security systems. The results of this study show that when the device detected the children playing in the safe zone, the system commanded the buzzer to turn off. Furthermore, when the camera detects that the children are playing in the caution and danger zone, the device then commands the buzzer to turn on as an early warning security system for the parents.
Comparative Analysis of CNN, MobileNetV2 and EffecientNetBO in Smart Farming System for Chili Leaf Disease Detection Arda, Abdul Latief; Syamsu Alam; Matalangi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6709

Abstract

Chili leaf diseases greatly affect agricultural productivity, making early and accurate detection essential to support smart farming systems. This study presents a comparative analysis of three deep learning architectures—Convolutional Neural Network (CNN), MobileNetV2, and EfficientNetB0—for detecting chili leaf diseases using RGB images. The dataset consists of three main disease classes: Bacterial Spot, Curl Virus, and White Spot. Each model was trained and evaluated using accuracy, precision, recall, F1-score, macro AUC, and training time as performance metrics. Experimental results show that MobileNetV2 achieved the highest performance with 99% accuracy, 0.99 F1-score, and 0.99 macro AUC, although it required the longest training time of 115.12 seconds. CNN demonstrated competitive results with 96% accuracy and the shortest training time of 60 seconds, while EfficientNetB0 performed poorly with only 38% accuracy and an F1-score of 0.18. These findings highlight that model architecture, dataset characteristics, and training configuration significantly influence performance outcomes. This study contributes to the development of intelligent agricultural monitoring systems by identifying the most suitable deep learning architecture for real-time chili leaf disease detection in smart farming applications.
Co-Authors Abdul Jalil Abdul Latief Arda Basuki Rahmat Masdi Siduppa Billy Eden William Asrul Fadhil Sukiman, Muhammad Nur Guntur Guntur Kamaruddin Mahenra, Yusril Maulana, Asrul Syamsu Alam Wahyuningsih, Pujianti