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All Journal jurnal informasi teknologi dan sistem Global Science: Journal of Information Technology and Computer Science
Genrawan Hoendarto
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Computer Science & IT Control & Systems Engineering Library & Information Science

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Rancang Bangun Keamanan Rumah Berbasis IOT Dengan Sensor Pir Dan Kamera IOT Viktorius Ando Saputra; Genrawan Hoendarto; Ricky I. Ndaumanu
INTEKSIS Vol 12 No 2 (2025): November 2025
Publisher : LPPM Universitas Widya Dharma Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.17810181

Abstract

The rising threat of crimes, such as theft, necessitates a home security system capable of delivering rapid and accurate alerts. This research aims to develop an integrated and responsive Internet of Things (IoT)-based home security system. The method used involves designing a prototype that utilizes an ESP32-CAM module as the central controller, supported by a Passive Infrared (PIR) sensor for motion detection and an MC-38 magnetic sensor for door access detection. The research results show that the developed system successfully integrates all components. When a sensor detects suspicious activity, the system is capable of automatically capturing visual evidence and sending real-time notifications to the homeowner's mobile device via a WiFi network. In conclusion, this system offers a practical, efficient, and affordable security solution. The integration of multiple sensors with real-time visual notifications is proven to enhance vigilance and provide better protection against potential threats in residential environments.
A Hybrid Federated-Edge Learning Framework with Dynamic Model Pruning for Real-Time Anomaly Detection in Smart Manufacturing Networks Genrawan Hoendarto; Thommy Willay; Pavan Kumar
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 3 (2025): September: Global Science: Journal of Information Technology and Computer Scien
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i3.173

Abstract

The rapid advancement of intelligent systems has accelerated the adoption of data-driven solutions across diverse industries, creating an increasing need for models that are both efficient and privacy-preserving. While traditional centralized machine learning approaches offer strong predictive capabilities, they often struggle with challenges related to data privacy, network latency, and computational inefficiency-especially in distributed environments with heterogeneous devices. To address these limitations, recent research has explored hybrid learning frameworks that integrate federated learning, edge computing, and dynamic model optimization techniques. These hybrid approaches enable models to process and learn from data closer to the source while maintaining stringent privacy requirements by keeping raw data localized. Additionally, the incorporation of pruning strategies, adaptive model compression, or multimodal data fusion contributes to improved speed, scalability, and accuracy in real-time inference tasks. Such frameworks have demonstrated notable promise in settings characterized by high data volume, operational complexity, and the necessity for fast anomaly detection or decision-making. However, despite these advancements, several challenges remain, including synchronization delays across edge nodes, variability in hardware capabilities, and the need for more efficient aggregation algorithms. Future developments may involve leveraging next-generation pruning techniques, energy-aware edge scheduling, decentralized orchestration protocols, or the integration of digital twin technologies to further enhance performance. Overall, hybrid distributed learning frameworks represent an important evolution toward more intelligent, secure, and autonomous computational ecosystems capable of supporting the next wave of smart applications.
Co-Authors Pavan Kumar Ricky I. Ndaumanu Viktorius Ando Saputra Willay, Thommy