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Hermanus, Davy Ronald
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering

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Web-Based Anomaly Detection for Smart Urban Living: Drone Photography and Videography Hermanus, Davy Ronald; Suhono Harso Supangkat; Fadhil Hidayat
Jurnal Sistem Cerdas Vol. 6 No. 2 (2023)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v6i2.330

Abstract

Smart cities aim to enhance the quality of life for urban dwellers through technological advancements. Machine Learning (ML) plays a crucial role in various domains of Smart X, including education, transportation, healthcare, environment, and living. However, integrating ML into daily life poses challenges. This paper presents a web-based ML application prototype that effectively augments the daily quality of life for communities. It specifically explores the advantages of web-based photography-videography-enabled drones for citizen needs and city inspections. The application utilizes ML to detect anomalies and identify normal objects, addressing the common challenge of distinguishing normalcy from abnormality. Examples include assessing the structural integrity of house components, analyzing medical images, and evaluating the quality of fruits or hydroponic plants. The study employs exploratory and experimental methods, utilizing teachable machine learning and the Python-based Streamlit application. Experimental results demonstrate that web-based photo and video analysis expedites the detection of normal and abnormal images and videos, surpassing the limitations of visual examination with the naked eye. This research contributes to advancing ML applications in smart living for urban communities.
Mini Drone-Based Precision Agriculture for Indonesian MSMEs: A Low-Cost AI-Assisted Monitoring System Hermanus, Davy Ronald; Supangkat, Suhono Harso; Hidayat, Fadhil
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025): August
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.555

Abstract

This research introduces a cost-effective drone-based agricultural monitoring system targeted at Indonesia’s smallholder farming enterprises (MSMEs). By leveraging mini drones (DJI Mini 2 SE) and lightweight AI models, farmers can segment land, detect vegetation health, and count crops using simple RGB video analysis. The system utilizes a mobile-to-YouTube private livestream pipeline and performs video processing offline using semantic segmentation (U-Net) and object detection (YOLOvX). The prototype system—tested on a 300m² vegetable plot—shows promising results with over 90% detection accuracy and effective land use visualization. The interface, built with Streamlit, provides real-time insights, affordability, and aligns with Smart City goals of accessibility and sustainability.
Co-Authors Fadhil Hidayat Hidayat, Fadhil Suhono Harso Supangkat