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Bambang Sumantri
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Journal of Electrical and Intelligent Systems (JEIS)
Published by Politeknik Elektronika Negeri Surabaya
ISSN : -     EISSN : 31637663     DOI : -
Core Subject :
Journal of Electrical and Intelligent Systems (JEIS) is a peer-reviewed scientific journal that publishes original research articles and review papers in electrical engineering, electronics, and intelligent systems. The journal serves as an interdisciplinary platform for researchers, academics, and practitioners to disseminate innovative theories, methodologies, and applications related to modern electrical and intelligent technologies. JEIS covers a broad range of topics including control, automation, and robotics; electronics, VLSI, and embedded systems; signal, image, and data processing; artificial intelligence and edge computing; sensors and industrial electronics; power systems and smart grid technologies; power electronics and electric drives; renewable energy and energy storage; as well as electric vehicles and transportation electrification. Emphasis is placed on both theoretical developments and practical implementations that address current industrial, societal, and technological challenges. By promoting high-quality research and rigorous peer-review processes, JEIS aims to advance intelligent, sustainable, and efficient electrical and electronic systems while fostering collaboration between academia and industry at the national and international levels.
Arjuna Subject : -
Articles 5 Documents
 1 
Experimental Validation of Enhanced Artificial Rabbit Optimization (ARO)-Based MPPT Method for Photovoltaic Systems Under Partial Shading Conditions Moh. Zaenal Efendi; Ircham Badrus Rahmadani; Muhammad Rizani Rusli
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.33

Abstract

Partial shading often introduces multiple local maxima into the power–voltage (P-V) characteristics of photovoltaic (PV) systems, which makes conventional maximum power point tracking (MPPT) methods prone to converging to suboptimal operating points. To overcome this challenge, this study proposes an Enhanced Artificial Rabbit Optimization (ARO)-based MPPT method by incorporating a time-varying adaptive inertia-weight mechanism, enabling more accurate global maximum power point (GMPP) tracking under nonlinear irradiance conditions. The proposed method was experimentally validated on a laboratory-scale PV platform consisting of three series-connected PV modules, a SEPIC converter, and an STM32-based controller tested under four irradiance patterns. The GMPP reference values were determined through direct experimental P-V scanning. The experimental results indicate that the enhanced method consistently performed better than the conventional ARO baseline, achieving a maximum output power of 98.68 W with a tracking accuracy of 99.93%. Across all test cases, the extracted power and tracking accuracy improved by an average of 1.24% and 1.21%, respectively, without a noticeable increase in tracking time.
Development of a UWB-Based Trilateration System for Multi-Mobile Node Indoor Localization Hary Oktavianto; Haniif Mulya Wicaksana; Audra Annisa Zhafirah; Mohammad Syafrudin; Prima Kristalina; Bambang Sumantri
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.39

Abstract

Accurate indoor localization is essential for navigation and coordination in multi-agent systems, particularly in environments where Global Positioning System (GPS) signals are unavailable. While ultrawideband (UWB)-based trilateration has been widely studied, most existing works focus on single-node localization and do not explicitly address scalability in terms of computational cost and processing time. This study proposes a scalable multi-mobile node localization framework based on UWB trilateration, with a key contribution in demonstrating linear computational growth with respect to the number of mobile nodes. The system employs UWB DWM1000 modules and the Symmetrical Double-Sided Two-Way Ranging (SDS-TWR) method to estimate distances between mobile nodes and anchor nodes, followed by onboard trilateration for position estimation. Experimental validation is conducted using up to four simultaneous mobile nodes within a 10×10 m indoor environment. The results show that the proposed system maintains centimeter-level accuracy, with RMSE values of 10.08 cm, 11.46 cm, 12.25 cm, and 9.13 cm for nodes 1 to 4, respectively. More importantly, the processing time increases consistently from 55 ms (one node) to 115 ms (four nodes), exhibiting an approximately constant incremental cost of 20 ms per additional node, which confirms the linear scalability of the proposed approach. These findings highlight that the proposed system not only achieves reliable localization accuracy but also ensures predictable and efficient computational performance, making it suitable for real-time multi-node applications such as robot swarm coordination and collaborative autonomous systems.
Automatic Control of Oxygen Flow for Hypoxemia Therapy Based on Fuzzy Method Rika Rokhana; Santi Anggraini; Retno Sukmaningrum; Hary Oktavianto; Paulus Susetyo Wardana; Agrippina Waya Rahmaning; Moch. Rochmad; Kemalasari; Hendhi Hermawan Efendi; Zainal Arief
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.46

Abstract

Hypoxemia is a serious condition that requires oxygen transfusion. Indiscriminate oxygen administration is a poor strategy that can increase organ damage and even death. This paper describes a system for automatically controlling airflow of an oxygen tubes to a patient based on blood oxygen saturation and respiratory rate measurements. The MAX30102 sensor is used to measure oxygen saturation levels, and the MAX9814 module is used to determine respiratory rate. Both sensor outputs are processed by an STM32F411 microcontroller, and then sent wirelessly to an Arduino Uno microcontroller, which implements the fuzzy logic controller to control oxygen flow. The fuzzy output is used to activate a motor servo that controls the oxygen tube valve opening. The valve opening width (in degrees) is divided into 5 categories. Communication between the microcontroller and the valve actuator uses a 433MHz wireless RF module. The device test results revealed an MAE of 0.40% for oxygen saturation measurements compared to standard hospital measuring instruments and an MAE of 0.47% for respiratory rate measurements compared to manual measurements. Overall system testing produced a valve opening with an MAE of 0.56% compared to simulation results using MATLAB.
Performance Analysis of a Voice-Integrated Overtaking Assistance Application based on LiDAR and Fuzzy Logic Dafit Ody Endriantono; Dedid Cahya Happyanto; Niam Tamami
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.58

Abstract

This research aims to enhance driver situational awareness during high-risk overtaking maneuvers by developing Navienta (Navigation Intelligent Assistant), a localized AI-powered navigation assistant. Conventional driver assistance systems often suffer from high latency and cloud dependencies that compromise real-time safety. To address these challenges, we implemented a localized edge-computing architecture utilizing a TF-350 LiDAR sensor and an Intel NUC as a processing hub, specifically designed to facilitate a high-speed, voice-driven interface. The system utilizes an Extended Kalman Filter (EKF) and a Mamdani Fuzzy Inference System (FIS) as the computational core to transform complex environmental dynamics into instantaneous voice instructions, ensuring low-latency feedback for the driver. The scientific contribution of this work lies in the synergistic integration of kinematic smoothing and fuzzy decision-making within a fully localized, high-concurrency architecture, eliminating cloud-dependency for safety-critical maneuvers. Experimental results confirm the system's precision with an average relative distance error of 0.22% and a consistent 50 ms end-to-end latency via a high-concurrency Golang backend. Experimental trials demonstrated that the localized Sherpa-ONNX engine achieved a 95.1% command recognition rate, which directly contributed to a significant 38.8% reduction in driver reaction time (from 1.8s to 1.1s). By maintaining operational integrity without external API dependencies, the Navienta framework provides a robust, cross-platform solution for modern intelligent transportation systems, offering a reliable approach for localized, safety-critical driver assistance.
Motion Consistent Object Detection: A Velocity Constrained Filtering Framework for Traffic Perception Gusty Anugrah; Dedid Cahya Happyanto; Eru Puspita
Journal of Electrical and Intelligent Systems Vol. 1 No. 1 (2026): April
Publisher : Politeknik Elektronika Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.68129/jeis.v1i1.59

Abstract

Conventional object detection systems in Intelligent Transportation Systems (ITS) commonly operate in a frame-wise manner, leading to temporally inconsistent predictions under dynamic conditions. This paper proposes a motion-consistent object detection framework that integrates velocity-constrained filtering to enforce physical plausibility across consecutive frames. The main contribution of this work lies in explicitly formulating detection validation as a deterministic velocity-bounded constraint problem, enabling direct integration of motion dynamics into the perception pipeline rather than treating motion as auxiliary information. The system employs a lightweight YOLO11s-based detector for visual perception and GPS-based motion estimation to provide real-world velocity constraints. Experiments are conducted on a traffic infrastructure dataset comprising 52,135 images across 33 classes and evaluated on an Intel NUC 13 Pro edge computing platform. Results demonstrate that the proposed method improves temporal stability compared to conventional frame-wise detection while maintaining strong detection performance, achieving an mAP@0.5 of 0.945 at 20 FPS. The findings indicate that incorporating explicit motion constraints enhances detection reliability without introducing significant computational overhead. However, performance may degrade under low-speed GPS noise and challenging visual conditions such as occlusion, highlighting limitations for future improvement.

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