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All Journal Jurnal Teknik Elektro dan Komputer TRIAC EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Computer Architecture and Signal Processing Intelligent Systems and Robotics
Hayadi Hamuda
UNIVERSITAS PAMULANG
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology

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Implementasi Simulasi Smart Antenna WiMAX 802.16D/E Berbasis Algoritma FIFO Hayadi Hamuda
EEICT (Electric, Electronic, Instrumentation, Control, Telecommunication) Vol 9, No 1 (2026)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/eeict.v9i1.23080

Abstract

Teknologi broadband nirkabel yang banyak digunakan menawarkan jangkauan luas dan berfungsi secara efisien Algoritma First-In, First-Out (FIFO pemrosesan dan pengambilan data. Hal ini berarti bahwa data yang diterima pertama kali adalah yang diproses terlebih dahulu pada dasarnya, data diproses sesuai urutan kedatangannya. Pada penelitian ini dirancangn sebuah traffic control jaringan untuk mengatur aliran data, dengan mengintegrasikan empat skenario setiap standar, serta memanfaatkan perangkat lunak simulasi OPNET 14.5 di wilayah studi yang ditentukan. Posisi geografis pengguna individu ditentukan berdasarkan kepadatan penduduk di setiap kecamatan dengan simulasi, analisis parameter Kualitas Layanan (QoS) pada penundaan, jitter, throughput, dan kehilangan paket dilakukan sesuai dengan standar TIPHON (Telecommunications and Internet Protocol Harmonisation Over Network), fokus pada suara, video, dan HTTP sebagai entitas yang dievaluasi untuk layanan video menunjukkan latensi yang kurang optimal dan kehilangan paket yang signifikan, di atas 25% sekitar 150 ms secara konsisten, disertai dengan kehilangan paket yang sangat kecil beroperasi secara efisien berkat kinerja latensi yang unggul
Medicine Delivery Robot Using Arduino Based on Android Control Hayadi Hamuda; Anjar Setiawan
Jurnal Teknik Elektro dan Komputer TRIAC Vol 13, No 1 (2026): Mei 2026
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v13i1.33827

Abstract

Pharmacies are experiencing increasing pressure in delivering healthcare services due to rising demand driven by the spread of infectious diseases. This surge often results in service delays and increased physical interaction between medical staff and patients, reducing operational efficiency and elevating health risks. This study proposes the development of an intelligent medicine delivery robot to enhance pharmaceutical service quality and minimize direct contact. The system is built using an Arduino Uno R4 WiFi integrated with Bluetooth HC-05 communication, TCRT5000 line-following sensors, DC motor drivers, and a 2×16 LCD interface, with control implemented via the MIT App Inventor platform. Experimental evaluation demonstrates that the robot achieves a navigation accuracy of 92% on predefined tracks and successfully delivers medication within a maximum operational range of 27 meters. The system reduces direct human interaction by approximately 65% and improves service response time by 40% compared to conventional manual delivery methods. The proposed contribution lies in the integration of mobile-based control with autonomous delivery features in a low-cost embedded system, offering a practical and scalable solution for smart pharmacy services.
Low Power Microcontroller Based System Design Employing Efficient DSP Algorithms for Smart Cyber Physical Embedded Monitoring Hayadi Hamuda; Novia Permata Atmadja; Rahmadi Asri
Computer Architecture and Signal Processing Vol. 1 No. 1 (2026): March: Computer Architecture and Signal Processing
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/casp.v1i1.33

Abstract

The integration of Digital Signal Processing (DSP) algorithms in low power microcontroller based embedded systems has emerged as a promising solution to optimize energy efficiency without compromising signal accuracy and performance. This study focuses on the design and optimization of DSP algorithms specifically for microcontrollers, aimed at achieving real-time, reliable monitoring for applications such as healthcare, environmental sensing, and IoT devices. The research highlights the system's ability to handle complex signal processing tasks while maintaining low power consumption, ensuring long-term, continuous operation in remote or battery-powered environments. The system employs various techniques, including advanced power management strategies such as dynamic voltage scaling (DVS) and adaptive voltage scaling (AVS), along with lightweight AI algorithms and model pruning, to minimize energy use. The results show significant reductions in power consumption compared to traditional systems, particularly during continuous monitoring tasks. Despite this, the optimized DSP algorithms maintain or even enhance signal accuracy, ensuring that critical monitoring data remains reliable. Furthermore, the system demonstrates robust performance and reliability over extended periods, making it suitable for long-term deployment in critical applications such as wearable medical devices and industrial sensors. This research provides a foundation for the development of future low power embedded systems, emphasizing the importance of DSP-aware optimization in achieving energy-efficient and high-performance monitoring. Future improvements may include advanced AI-driven power optimization techniques, enhanced scalability, and cross-domain interoperability, ensuring that these systems can be effectively deployed across diverse applications, from healthcare to environmental monitoring.
A Hybrid NeuralSymbolic Approach for Human Robot Interaction Enhancement Using Multimodal Sensor Fusion and Context Aware Behavioral Adaptation Techniques Setyawan Wibisono; Hayadi Hamuda; Encik Yoega Renaldi
Intelligent Systems and Robotics Vol. 1 No. 1 (2026): February: Intelligent Systems and Robotics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/isr.v1i1.35

Abstract

Human–Robot Interaction (HRI) systems increasingly rely on data-driven approaches to interpret multimodal sensory inputs and support natural interaction. However, purely neural-based HRI models often suffer from limited interpretability and insufficient context-aware decision-making, which can reduce user trust and adaptability in dynamic interaction scenarios. To address these limitations, this study proposes a hybrid neural–symbolic HRI framework that integrates multimodal neural perception with explicit symbolic reasoning for adaptive and interpretable robot behavior. The proposed system combines deep neural networks for processing visual, speech, and gesture inputs with a rule-based symbolic reasoning layer that models interaction context, user states, and behavioral constraints. A loosely coupled integration strategy enables neural outputs to be transformed into symbolic representations, allowing logical inference to guide action selection while preserving perceptual accuracy. The framework was evaluated through controlled HRI experiments comparing a neural-only baseline with the proposed hybrid configuration across multiple interaction scenarios. Experimental results demonstrate that the hybrid neural–symbolic system significantly improves interaction accuracy, contextual responsiveness, and user satisfaction, while achieving substantial gains in interpretability. These findings indicate that symbolic reasoning effectively complements neural perception by enhancing transparency and context-aware adaptation without compromising performance. The study concludes that hybrid neural–symbolic architectures provide a promising foundation for developing trustworthy, adaptive, and human-centered HRI systems.
Development of an Intelligent Embedded Cyber Physical System Integrating Edge AI and Low Power Sensor Networks for Adaptive Environmental Monitoring and Robotic Control Hayadi Hamuda; Sarah Anjani; Lailatun Adzimah
Intelligent Systems and Robotics Vol. 1 No. 1 (2026): February: Intelligent Systems and Robotics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/isr.v1i1.40

Abstract

Recent advancements in environmental monitoring and robotic control demand systems that are capable of real-time responsiveness, energy efficiency, and reliable operation in dynamic and resource-constrained environments. Conventional cloud-centric cyber-physical system (CPS) architectures often suffer from high latency, continuous connectivity dependency, and increased energy consumption, limiting their suitability for time-critical monitoring and adaptive control applications. To address these challenges, this study proposes an intelligent embedded cyber-physical system integrating Edge AI, low-power sensor networks, and adaptive robotic control for environmental monitoring. The proposed architecture relocates data processing and decision-making closer to the data source, enabling real-time inference, reduced communication overhead, and enhanced system autonomy. The research adopts a design-oriented experimental methodology involving system architecture design, lightweight Edge AI model development, prototype implementation, and performance evaluation under realistic operating conditions. Experimental results demonstrate that the proposed edge-based CPS significantly reduces end-to-end latency and energy consumption while maintaining acceptable inference accuracy compared to cloud-based processing. Furthermore, the system achieves improved communication efficiency and higher operational reliability, particularly under intermittent network connectivity. The findings highlight that embedding intelligence at the edge enables closed-loop sensing, decision-making, and actuation, which is essential for adaptive robotic control in environmental monitoring scenarios. This study contributes a system-level perspective on Edge AI–enabled CPS design and provides empirical evidence supporting the transition from cloud-centric architectures toward distributed, energy-aware, and resilient cyber-physical systems for real-time monitoring and control applications.
Co-Authors Anjar Setiawan Encik Yoega Renaldi Lailatun Adzimah Novia Permata Atmadja Rahmadi Asri Sarah Anjani Setyawan Wibisono