Rachmat Muwardi

@mercubuana.ac.id

Department of Electrical Engineering, Faculty of Engineering
Universitas Mercu Buana

Rachmat Muwardi


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https://researchid.co/rachmat.muwardi
15

Scopus Publications

200

Scholar Citations

9

Scholar h-index

8

Scholar i10-index

Scopus Publications

  • Adaptive Meta-Loss Networks: Learning Task-Agnostic Loss Functions via Evolutionary Optimization
    Mirna Yunita, Xiabi Liu, Zhaoyang Hai, Rachmat Muwardi
    Computers Materials and Continua, 2026
    Designing appropriate loss functions is critical to the success of supervised learning models. However, most conventional losses are fixed and manually designed, making them suboptimal for diverse and dynamic learning scenarios. In this work, we propose an Adaptive Meta-Loss Network (Adaptive-MLN) that learns to generate task-agnostic loss functions tailored to evolving classification problems. Unlike traditional methods that rely on static objectives, Adaptive-MLN treats the loss function itself as a trainable component, parameterized by a shallow neural network. To enable flexible, gradient-free optimization, we introduce a hybrid evolutionary approach that combines Genetic Algorithms (GA) for global exploration and Evolution Strategies (ES) for local refinement. This co-evolutionary process dynamically adjusts the loss landscape, improving model generalization without relying on analytic gradients or handcrafted heuristics. Experimental evaluations on synthetic tasks and the CIFAR-10 and MNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy, convergence, and adaptability.
  • Improving Convolutional Neural Network Performance Using Alpha-Based Adaptive Pooling for Image Classification
    Nahdi Saubari, Kunfeng Wang, Rachmat Muwardi, Andri Pranolo
    Computers Materials and Continua, 2026
    This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark datasets, MNIST and CIFAR-10, representing grayscale and color image classification scenarios, respectively. Experiments are conducted across three CNN families with different depths LeNet-5, a deeper custom-built CNN, and ResNet-18 to assess robustness under varying representational capacity. Under the best α setting with a 4 × 4 pooling configuration, Adaptive Pooling exhibits architecture-dependent behavior. On LeNet-5, Adaptive Pooling achieves 87.2% on MNIST and 30.1% on CIFAR-10, compared with 97.8% (max/average pooling) on MNIST and 60.1% (max pooling)/53.9% (average pooling) on CIFAR-10. In contrast, on the deeper custom CNN, Adaptive Pooling becomes competitive, reaching 99.7% on MNIST and 86.1% on CIFAR-10, which is comparable to 99.6%–99.7% on MNIST and 84.5%–86.2% on CIFAR-10 achieved by conventional pooling. On ResNet-18, Adaptive Pooling attains 99.1% on MNIST, while CIFAR-10 performance decreases to 37.2% relative to the default global average pooling baseline (99.7% on MNIST and 89.0% on CIFAR-10), suggesting that performance also depends on where the pooling replacement is applied. Overall, these findings indicate that α-controlled Adaptive Pooling provides a lightweight and configurable pooling strategy that can improve stability and achieve competitive accuracy in deeper CNNs, although it should be treated as a complementary mechanism rather than a universal replacement across all architectures.
  • YOLO-LIO: A Real-Time Enhanced Detection and Integrated Traffic Monitoring System for Road Vehicles
    Rachmat Muwardi, Haiyang Zhang, Hongmin Gao, Mirna Yunita, Rizky Rahmatullah, Ahmad Musyafa, Galang Persada Nurani Hakim, Dedik Romahadi
    Algorithms, 2026
    Traffic violations and road accidents remain significant challenges in developing safe and efficient transportation systems. Despite technological advancements, improving vehicle detection accuracy and enabling real-time traffic management remain critical research priorities. This study proposes YOLO-LIO, an enhanced vehicle detection framework designed to address these challenges by improving small-object detection and optimizing real-time deployment. The system introduces multi-scale detection, virtual zone filtering, and efficient preprocessing techniques, including grayscale transformation, Laplacian variance calculation, and median filtering to reduce computational complexity while maintaining high performance. YOLO-LIO was rigorously evaluated on five datasets, GRAM Road-Traffic Monitoring (99.55% accuracy), MAVD-Traffic (99.02%), UA-DETRAC (65.14%), KITTI (94.21%), and an Author Dataset (99.45%), consistently demonstrating superior detection capabilities across diverse traffic scenarios. Additional system features include vehicle counting using a dual-line detection strategy within a virtual zone and speed detection based on frame displacement and camera calibration. These enhancements enable the system to monitor traffic flow and vehicle speeds with high accuracy. YOLO-LIO was successfully deployed on Jetson Nano, a compact, energy-efficient hardware platform, proving its suitability for real-time, low-power embedded applications. The proposed system offers an accurate, scalable, and computationally efficient solution, advancing intelligent transportation systems and improving traffic safety management.
  • Design of Student Attendance System Based on Face Recognition and IoT Communication
    Yuliza, Rachmat Muwardi, Sakinah Puspa Anggraeni, Mirna Yunita, Felix Tan, Ainul Yaqin
    International Conference on Radar Antenna Microwave Electronics and Telecommunications Icramet, 2025
    In response to the increasing demand for efficient, accurate, and contactless attendance systems in educational environments, this study proposes a real-time student attendance system based on face recognition using the YOLOv3 algorithm. The system is deployed on a microprocessor platform and uses MQTT as the primary IoT communication protocol to enable low-latency, reliable, and lightweight two-way communication with a local server. Operating within a closed local host network, the system ensures data security and stability without reliance on external internet connectivity. The system’s performance was evaluated using the WIDER FACE, WIDER PERSON, and a custom dataset collected from Indonesian elementary school students. The results demonstrate that YOLOv3 achieved a detection accuracy of 88.5% (Easy), 86.2% (Medium), and 75.4% (Hard) with a stable processing speed of 25-30 FPS, offering an ideal balance between speed and accuracy for real-time school applications. Comparative evaluations show that MQTT outperformed HTTP and WebSocket protocols, achieving the lowest latency ($\mathbf{7 5 ~ m s}$), highest throughput ($\mathbf{8. 5 ~ m s g} / \mathbf{s}$), smallest payload size (1.5 KB), and highest success rate (99.6%). These findings confirm that YOLOv3 combined with MQTT provides a reliable, efficient, and scalable solution for schoolbased face recognition attendance systems.
  • Design Of Iot-Integrated Real-Time Vehicle Detection System With Mqtt Communication And Web-Based Visualization
    Akhmad Wahyu Dani, Rachmat Muwardi, Suisbiyanto Prasetya, Mirna Yunita, Clarissa Yoselia, Ana Handayana
    International Conference on Radar Antenna Microwave Electronics and Telecommunications Icramet, 2025
    The rapid growth of vehicles in urban areas, particularly in Jakarta, has intensified challenges related to traffic congestion, road safety, and law enforcement. To address these issues, this study proposes a real-time vehicle detection system integrating the YOLOv3 algorithm with an IoT-based communication framework. The system employs a Virtual Zone approach to optimize detection efficiency, combined with preprocessing techniques such as median filtering and grayscale conversion to improve image clarity. Vehicle detection results are transmitted using the lightweight MQTT protocol and visualized on a web-based monitoring dashboard. Experimental evaluations were conducted using three datasets MAVD, GRAM-RTM, and a custom Author Dataset covering diverse traffic conditions. Results show that YOLOv3 achieves accuracies consistently above 97% while maintaining high computational efficiency, making it suitable for deployment on edge devices such as the Jetson Nano. Furthermore, MQTT demonstrated superior communication performance compared to HTTP and WebSocket, with lower latency, higher throughput, and minimal packet loss, achieving a success rate of 99.5%. These findings highlight the practicality and scalability of the proposed system for intelligent traffic monitoring applications, providing a lightweight and real-time solution that supports the advancement of systems such as Electronic Traffic Law Enforcement (E-Tilang).
  • Phase Laser Ranging Radar Based on IoT Communication
    Ketty Siti Salamah, Trie Maya Kadarina, Puput Dani Prasetyo Adi, Rachmat Muwardi, Rizky Rahmatullah, Mirna Yunita
    International Conference on Computer and Communication Engineering Iccce, 2025
    This paper introduces and implements an Internet of Things (IoT) system that seamlessly integrates UDP protocol communication with multi-scale phase laser radar technology. At its core, the system employs an FPGA to tackle the challenges of high-speed laser signal acquisition and realtime processing. To overcome the bottleneck associated with rapid laser ranging, an innovative multi-scale phase ranging algorithm based on dual-frequency modulation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(150 \text{MHz}$</tex> and 0.5 MHz) has been devised, achieving a ranging precision of 0.01 meters over a distance of 0 to 300 meters, with efficient processing realized through FPGA-based hardware filters and phase-locked loops (PLL). The system leverages the low-latency nature of the UDP protocol to establish a robust transmission architecture comprising a high-speed Ethernet MAC, UDP communication core, data buffer FIFO, and interface logic, thereby ensuring stable communication between the FPGA and the host computer. To further expand its application scenarios, a host computer platform has been developed in Python. Experimental results demonstrate that the system excels in target detection, real-time monitoring, and high-precision ranging, while consuming a modest amount of hardware resources-only 20 DSP units, 2132 LUTs, 2408 registers, and 7 block memories. This work offers a novel solution for the integration of precise LiDAR imaging with the Internet of Things, and future research will focus on incorporating intelligent algorithms to further enhance system robustness in complex scenarios.
  • Multinode LoRa-MQTT of Design Architecture and Analyze Performance for Dual Protocol Network IoT
    Rizky Rahmatullah, Hongmin Gao, Ryan Prasetya Utama, Puput Dani Prasetyo Adi, Jannat Mubashir, Rachmat Muwardi, Widar Dwi Gustian, Hanifah Dwiyanti, Yuliza -
    International Journal of Advanced Computer Science and Applications, 2025
    LoRaWAN networks and large places do not support Wi-Fi for multiple points. An architecture that offers dual networks to alter their supporting networks is needed for IoT device installation. The novelty in this research is that designing an architecture for multimode LoRa-MQTT with a mechanism for testing LoRa data transmission with different delays and Wireshark for testing Wi-Fi network QoS on MQTT is necessary. This hour-long LoRa network experiment shows that the End-Node can only receive one data at a time. One data set will be received if several data sets are obtained due to conflict. The second experiment showed data barely reached 70%. The signal strength or RSSI, and the node that sent the data initially decide the data received from a given node, some seconds apart, towards tested QOS with excellent packet loss, 21 ms delay, 50,616 bytes/s throughput, and 0.1426 jitter. Avoid data conflicts and loss by utilizing fewer nodes or adding end nodes in this experiment. The network service is excellent. According to this study, LoRa and MQTT can work well together. This approach could solve Internet of Things communication concerns, especially in large places that are LoRaWAN-inaccessible and Wi-Fi networks are limited.
  • HorGait: Advancing Gait Recognition With Efficient High-Order Spatial Interactions in LiDAR Point Clouds
    Jiaxing Hao, Yanxi Wang, Zhigang Chang, Hongmin Gao, Zihao Cheng, Chen Wu, Xin Zhao, Peiye Fang, Rachmat Muwardi
    IEEE Access, 2025
    Gait recognition is a remote biometric technology that utilizes the dynamic characteristics of human movement to identify individuals even under various extreme lighting conditions. Due to the limitation in spatial perception capability inherent in 2D gait representations, LiDAR can directly capture 3D gait features and represent them as point clouds, reducing environmental and lighting interference in recognition while significantly advancing privacy protection. For complex 3D representations, shallow networks fail to achieve accurate recognition, making vision Transformers the foremost prevalent method. However, the prevalence of dumb patches has limited the widespread use of Transformer architecture in gait recognition. This paper proposes a method named HorGait, which utilizes a hybrid model with a Transformer architecture for gait recognition on the planar projection of 3D point clouds from LiDAR. Specifically, it employs a hybrid model structure called LHM Block to achieve input adaptation, long-range, and high-order spatial interaction of the Transformer architecture. Additionally, it uses large convolutional kernel CNNs to segment the input representation, replacing attention windows to reduce dumb patches. Extensive experiments demonstrated that HorGait achieved a Rank-1 accuracy of 82.54% on the SUSTech1K dataset, surpassing the state-of-the-art Transformer architecture method by 8.33%. This confirms the hybrid model’s ability to execute the complete Transformer process and excel in point cloud planar projection. The outstanding performance of HorGait offers new insights for the future application of the Transformer architecture in gait recognition.
  • Design of New Traffic System YOLO-LIO: Light-Traffic Intercept and Observation
    Rachmat Muwardi, Haiyang Zhang, Hongmin Gao, Mirna Yunita, Yanxi Wang, Yuliza
    Proceeding 2024 International Conference on Radar Antenna Microwave Electronics and Telecommunications Icramet 2024, 2024
    Vehicle detection is an area of active development aimed at enhancing driving safety and ensuring compliance with traffic regulations. Despite ongoing efforts, accidents and traffic violations continue to pose significant challenges, leading to disruptions in driving. In response to these issues, the author aims to develop a more efficient traffic management system to improve driver organization and driving behavior. To achieve this, the author proposes using YOLO-LIO as the neural network of choice for the Traffic System. The effectiveness of YOLO-LIO was evaluated using three datasets: the Montevideo Audio and Video Dataset (MAVD), the GARM Road-Traffic Monitoring dataset (GRAM-RTM), and a custom dataset created by the author. The results highlight the superior performance of YOLO-LIO in vehicle detection tasks, achieving accuracy rates of $\\mathbf{9 9. 0 2 \\%}$ on the MAVD dataset, $\\mathbf{9 9. 5 5 \\%}$ on the GRAM-RTM dataset, and $\\mathbf{9 9. 3 2 \\%}$ on the custom dataset. This demonstrates the model’s high effectiveness across various datasets. Additionally, the author conducted experiments incorporating OCR technology with the YOLO-LIO algorithm in the Traffic System. The system achieved an accuracy of $\\mathbf{8 0. 2 1 \\%}$ in vehicle number detection, demonstrating its effectiveness. This result reflects the overall performance of the entire system process, from data input to the final detection output, ensuring a comprehensive and accurate detection mechanism. Compared to other algorithms such as YOLOv3 + OCR, YOLOv4, and Faster R-CNN, YOLO-LIO + OCR, they have exhibited significantly better performance. These promising results highlight the potential of YOLO-LIO in creating a robust Traffic System that can significantly enhance road safety and traffic regulation compliance.
  • Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family
    Sinergi Indonesia, 2023
  • Kalman Filter for tracking a noisy cosinusoidal signal with constant amplitude
    Prima Wijaya Kusuma, Mohamed Hadi Habaebi, Galang P. N. Hakim, Rachmat Muwardi, Md. Rafiqul Islam
    Proceedings of the 9th International Conference on Computer and Communication Engineering Iccce 2023, 2023
  • Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted
    Galang Persada Nurani Hakim, Rachmat Muwardi, Mirna Yunita, Diah Septiyana
    Indonesian Journal of Electrical Engineering and Computer Science, 2022
  • Delivery of Data Digital High Frequency Radio Wave Using Advanced Encryption Standard Security Mechanism
    Setiyo Budiyanto, Lukman Medriavin Silalahi, Freddy Artadima Silaban, Rachmat Muwardi, Hongmin Gao
    Proceedings 2021 International Seminar on Intelligent Technology and Its Application Intelligent Systems for the New Normal Era Isitia 2021, 2021
  • The automatic and manual railroad door systems based on IoT
    Setiyo Budiyanto, Freddy Artadima Silaban, Lukman Medriavin Silalahi, Triyanto Pangaribowo, Muhammad Hafizd Ibnu Hajar, Alvin Sepbrian, Rachmat Muwardi, Gao Hongmin
    Indonesian Journal of Electrical Engineering and Computer Science, 2021
  • Research and Design of Fast Special Human Face Recognition System
    Rachmat Muwardi, Huangyao Qin, Hongmin Gao, Harun Usman Ghifarsyam, Muhammad Hafizd Ibnu Hajar, Mirna Yunita
    2020 2nd International Conference on Broadband Communications Wireless Sensors and Powering Bcwsp 2020, 2020

RECENT SCHOLAR PUBLICATIONS

  • Improving Convolutional Neural Network Performance Using Alpha-Based Adaptive Pooling for Image Classification.
    N Saubari, K Wang, R Muwardi, A Pranolo
    Computers, Materials & Continua 87 (3), 1 , 2026
    2026
  • Tunnel-Like Road Foliage Path Loss Prediction for V2V Communications for LoRa LPWAN 433 MHz and 920 MHz bands
    GP Hakim, MH Habaebi, A Firdausi, R Muwardi, M Alaydrus
    Engineering Research Express , 2026
    2026
  • YOLO-LIO: A Real-Time Enhanced Detection and Integrated Traffic Monitoring System for Road Vehicles
    R Muwardi, H Zhang, H Gao, M Yunita, R Rahmatullah, A Musyafa, ...
    Algorithms 19 (1), 42 , 2026
    2026
    Citations: 2
  • Adaptive Meta-Loss Networks: Learning Task-Agnostic Loss Functions via Evolutionary Optimization
    M Yunita, X Liu, Z Hai, R Muwardi
    Computers, Materials, & Continua 87 (2) , 2026
    2026
  • Design Of Iot-Integrated Real-Time Vehicle Detection System With Mqtt Communication And Web-Based Visualization
    AW Dani, R Muwardi, S Prasetya, M Yunita, C Yoselia, A Handayana
    2025 International Conference on Radar, Antenna, Microwave, Electronics, and … , 2025
    2025
  • Design of Student Attendance System Based on Face Recognition and IoT Communication
    R Muwardi, SP Anggraeni, M Yunita, F Tan, A Yaqin
    2025 International Conference on Radar, Antenna, Microwave, Electronics, and … , 2025
    2025
  • Phase Laser Ranging Radar Based on IoT Communication
    KS Salamah, TM Kadarina, PDP Adi, R Muwardi, R Rahmatullah, ...
    2025 10th International Conference on Computer and Communication Engineering … , 2025
    2025
  • Design and Implementation of a Real-Time Monitoring System for a 150 kV Substation with Multi-Platform Notification and Visualization: English
    EAY Kartika, R Muwardi, R Rahmatullah, M Yunita, Y Yuliza, AW Dani
    Internet of Things and Artificial Intelligence Journal 5 (2), 409-418 , 2025
    2025
  • HorGait: Advancing Gait Recognition With Efficient High-Order Spatial Interactions in LiDAR Point Clouds
    J Hao, Y Wang, Z Chang, H Gao, Z Cheng, C Wu, X Zhao, P Fang, ...
    IEEE Access , 2025
    2025
    Citations: 2
  • Multinode LoRa-MQTT of Design Architecture and Analyze Performance for Dual Protocol Network IoT.
    R Rahmatullah, H Gao, RP Utama, PDP Adi, J Mubashir, R Muwardi, ...
    International Journal of Advanced Computer Science & Applications 16 (1) , 2025
    2025
    Citations: 6
  • Design of new traffic system YOLO-LIO: light-traffic intercept and observation
    R Muwardi, H Zhang, H Gao, M Yunita, Y Wang
    2024 International Conference on Radar, Antenna, Microwave, Electronics, and … , 2024
    2024
    Citations: 6
  • Optimization of YOLOv4-tiny algorithm for vehicle detection and vehicle count detection embedded system
    R Muwardi, IP Nugroho, KS Salamah, M Yunita, R Rahmatullah, ...
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 10 (3), 639-648 , 2024
    2024
    Citations: 10
  • Horgait: A hybrid model for accurate gait recognition in lidar point cloud planar projections
    J Hao, Y Wang, Z Chang, H Gao, Z Cheng, C Wu, X Zhao, P Fang, ...
    arXiv preprint arXiv:2410.08454 , 2024
    2024
    Citations: 3
  • Design of Equipment for Detecting and Ensuring Reliability of The Substation
    H Ihsan, R Muwardi, M Yunita, Y Yuliza, AW Dani
    Internet of Things and Artificial Intelligence Journal 4 (3), 452-463 , 2024
    2024
    Citations: 5
  • Smart Cupboards at Community Learning Activity Center Wiyata Utama
    IUV Simanjuntak, LM Silalahi, KS Salamah, F Supegina, R Muwardi
    ABDIMAS: Jurnal Pengabdian Masyarakat 7 (2), 749-755 , 2024
    2024
    Citations: 4
  • Human object detection for real-time camera using mobilenet-SSD
    R Muwardi, JMR Permana, H Gao, M Yunita
    Journal of Integrated and Advanced Engineering (JIAE) 3 (2), 141-150 , 2023
    2023
    Citations: 16
  • Kalman Filter for tracking a noisy cosinusoidal signal with constant amplitude
    PW Kusuma, MH Habaebi, GPN Hakim, R Muwardi, MR Islam
    2023 9th International Conference on Computer and Communication Engineering … , 2023
    2023
    Citations: 1
  • Design human object detection yolov4tiny algorithm on ARM cortex-a72 and a53
    R Muwardi, A Faizin, PDP Adi, R Rahmatullah, Y Wang, M Yunita, ...
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9 (4), 1168-1178 , 2023
    2023
    Citations: 4
  • Fast human recognition system on real-time camera
    RM Yuliza, M Rhozaly, MY Lenni, GE Yehezkiel
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9 (4), 895-903 , 2023
    2023
    Citations: 11
  • Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family
    M Yunita, R Muwardi, Z Iklima
    SINERGI 27 (1), 23-30 , 2023
    2023
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Design Sistem Keamanan Pintu Menggunakan Face Detection
    R Muwardi, RR Adisaputro
    Jurnal Teknologi Elektro 12, 113-119 , 2021
    2021
    Citations: 31
  • Network Security Monitoring System Via Notification Alert
    R Muwardi, H Gao, HU Ghifarsyam, M Yunita, A Arrizki, J Andika
    Journal of Integrated and Advanced Engineering (JIAE) 1 (2), 113-122 , 2021
    2021
    Citations: 24
  • Human object detection for real-time camera using mobilenet-SSD
    R Muwardi, JMR Permana, H Gao, M Yunita
    Journal of Integrated and Advanced Engineering (JIAE) 3 (2), 141-150 , 2023
    2023
    Citations: 16
  • The automatic and manual railroad door systems based on IoT
    S Budiyanto, FA Silaban, LM Silalahi, T Pangaribowo, MHI Hajar, ...
    Indonesian Journal of Electrical Engineering and Computer Science 21 (3 … , 2021
    2021
    Citations: 13
  • Research and Design of Fast Special Human Face Recognition System
    R Muwardi, H Qin, H Gao, HU Ghifarsyam, MHI Hajar, M Yunita
    2020 2nd International Conference on Broadband Communications, Wireless … , 2020
    2020
    Citations: 12
  • Fast human recognition system on real-time camera
    RM Yuliza, M Rhozaly, MY Lenni, GE Yehezkiel
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9 (4), 895-903 , 2023
    2023
    Citations: 11
  • Delivery of data digital high frequency radio wave using advanced encryption standard security mechanism
    S Budiyanto, LM Silalahi, FA Silaban, R Muwardi, H Gao
    2021 International Seminar on Intelligent Technology and Its Applications … , 2021
    2021
    Citations: 11
  • Optimization of YOLOv4-tiny algorithm for vehicle detection and vehicle count detection embedded system
    R Muwardi, IP Nugroho, KS Salamah, M Yunita, R Rahmatullah, ...
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 10 (3), 639-648 , 2024
    2024
    Citations: 10
  • Modification of Control Oil Feeding with PLC Using Simulation Visual Basic and Neural Network Analysis
    Y Yuliza, R Muwardi, DW Pratama, MH Santoso, M Yunita
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 8, 38-50 , 2022
    2022
    Citations: 9
  • Optimize image processing algorithm on arm cortex-a72 and a53
    R Muwardi, M Yunita, H Ghifarsyam, H Juliyanto
    Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika 8 (3), 399-409 , 2022
    2022
    Citations: 8
  • Multinode LoRa-MQTT of Design Architecture and Analyze Performance for Dual Protocol Network IoT.
    R Rahmatullah, H Gao, RP Utama, PDP Adi, J Mubashir, R Muwardi, ...
    International Journal of Advanced Computer Science & Applications 16 (1) , 2025
    2025
    Citations: 6
  • Design of new traffic system YOLO-LIO: light-traffic intercept and observation
    R Muwardi, H Zhang, H Gao, M Yunita, Y Wang
    2024 International Conference on Radar, Antenna, Microwave, Electronics, and … , 2024
    2024
    Citations: 6
  • Design of Equipment for Detecting and Ensuring Reliability of The Substation
    H Ihsan, R Muwardi, M Yunita, Y Yuliza, AW Dani
    Internet of Things and Artificial Intelligence Journal 4 (3), 452-463 , 2024
    2024
    Citations: 5
  • Implementation of Bayesian inference MCMC algorithm in phylogenetic analysis of Dipterocarpaceae family
    M Yunita, R Muwardi, Z Iklima
    SINERGI 27 (1), 23-30 , 2023
    2023
    Citations: 5
  • Fuzzy Mamdani performance water chiller control optimization using fuzzy adaptive neuro fuzzy inference system assisted
    GPN Hakim, R Muwardi, M Yunita, D Septiyana
    Indonesian Journal of Electrical Engineering and Computer Science 28 (3 … , 2022
    2022
    Citations: 5
  • Smart Cupboards at Community Learning Activity Center Wiyata Utama
    IUV Simanjuntak, LM Silalahi, KS Salamah, F Supegina, R Muwardi
    ABDIMAS: Jurnal Pengabdian Masyarakat 7 (2), 749-755 , 2024
    2024
    Citations: 4
  • Design human object detection yolov4tiny algorithm on ARM cortex-a72 and a53
    R Muwardi, A Faizin, PDP Adi, R Rahmatullah, Y Wang, M Yunita, ...
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 9 (4), 1168-1178 , 2023
    2023
    Citations: 4
  • Monitoring chicken livestock process using Vento Application at a farm
    F Andika, R Muwardi, M Yunita, MA Purba
    Journal of Integrated and Advanced Engineering (JIAE) 2 (2), 77-88 , 2022
    2022
    Citations: 4
  • Horgait: A hybrid model for accurate gait recognition in lidar point cloud planar projections
    J Hao, Y Wang, Z Chang, H Gao, Z Cheng, C Wu, X Zhao, P Fang, ...
    arXiv preprint arXiv:2410.08454 , 2024
    2024
    Citations: 3
  • Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta
    Y Yuliza, N Sari, R Muwardi, L Lenni, Y Rahmawati
    Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 8 (4), 610 , 2022
    2022
    Citations: 3