Article Per Year (5 Year)
p-Index From 2021 - 2026
1.8
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem Cerdas Jurnal E-Komtek Journal of Soft Computing Exploration Jurnal INFOTEL TECHNOLOGIC International Journal of Electrical Engineering and Intelligent Computing Journal of Soft Computing Exploration
Nugroho, Waluyo
Astra Polytechnic
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN Waluyo Nugroho Waluyo; R. Rizal Isnanto; Adian Fatchur Rochim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4626

Abstract

Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40 testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN. The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%.
SISTEM ANDON PRODUKSI MENGGUNAKAN LED MATRIKS BERBASIS MIKROKONTROLER ESP32, KOMUNIKASI LORA DAN DASBOR NODE-RED Arifianto, Mada Jimmy Fonda; Nugroho, Waluyo; Cahya, Khairunnisa; Hadi, Aswan
Technologic Vol 16 No 1 (2025): TECHNOLOGIC
Publisher : LPPM Politeknik Astra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52453/t.v16i1.466

Abstract

Kemampuan dalam mendeteksi dan menangani ketidaknormalan secara cepat merupakan elemen penting untuk meningkatkan produktivitas di lingkungan industri modern. Sistem andon berbasis mikrokontroler merupakan sebuah desain guna mempermudah mendeteksi ketidaknormalan dan meningkatkan komunikasi antar operator dan supervisor di lini produksi. Sistem ini menggunakan mikrokontroler ESP32 sebagai pengendali utama, dot matrix RGB LED sebagai tampilan visual status produksi dan modul komunikasi LoRa. Keuntungan teknologi LoRa yaitu dapat mengirimkan notifikasi secara real-time dan stabil dengan jangkauan jauh. Pendekatan yang digunakan dalam penelitian ini adalah metode prototipe, yang mencakup tahapan analisis kebutuhan, perancangan perangkat keras dan perangkat lunak. Tahap berikutnya yaitu pengembangan sistem, pengujian dan evaluasi. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi masalah dalam waktu kurang dari 2 detik dan menampilkan pesan dengan warna berbeda sesuai tingkat urgensi. Selain itu, integrasi Node-Red sebagai antarmuka visual dan InfluxDB untuk penyimpanan data historis memungkinkan pengelolaan dan analisis data yang lebih efektif.
Web based IoT monitoring system for ultrasonic water flow measurement using ESP32-S3 and cloud database Nugroho, Waluyo; Arifianto, Mada Jimmy Fonda; Afianto, Afianto; Wicaksono, Andreadie; Nursim, Nursim
Journal of Soft Computing Exploration Vol. 6 No. 4 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i4.625

Abstract

Efficient water management is crucial for ensuring sustainable resource utilization and reducing water losses in both industrial and domestic applications. This study presents the design and implementation of a smart water monitoring system based on an ultrasonic flow meter, which enables accurate, real-time measurement of water flow without physical contact with the medium. The proposed system integrates ultrasonic sensors with a microcontroller-based data acquisition unit and wireless communication to transmit flow rate, volume, and consumption data to a cloud-based monitoring platform. The system was tested in various flow conditions to evaluate accuracy, stability, and energy efficiency. Experimental results demonstrate that the ultrasonic flow meter achieved a measurement accuracy of ±1% compared to a reference turbine flow meter, while maintaining minimal power consumption. Furthermore, the integration of Internet of Things (IoT) capabilities allows remote monitoring, anomaly detection, and data logging for long-term analysis. The results indicate that this ultrasonic-based monitoring system provides a reliable and non-invasive solution for smart water management, offering potential applications in household metering, agricultural irrigation, and industrial fluid monitoring.
Smart Parking based on Car Detection using Deep Learning YOLOv8 Waluyo Nugroho; Afianto Afianto; Mada Jimmy Fonda Arifianto
International Journal Of Electrical Engineering and Inteligent Computing Vol 2 No 1 December (2024): International Journal Of Electrical Engineering And Intelligent Computin
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/ijeeic.v2i1.8692

Abstract

In the context of rapidly growing urbanization, the need for efficient parking management solutions is becoming increasingly urgent. This research develops and implements a car detection system based on YOLOv8 (You Only Look Once Version 8) for smart parking applications using Raspberry Pi and the Node-RED platform. This system is designed to optimize the use of parking spaces and increase parking management efficiency by utilizing YOLO's real-time object detection capabilities. Data processed by the Raspberry Pi is sent to the Node-RED platform for Internet of Things (IoT) via MQTT protocol. Node-RED functions as a management and visualization system, allowing users to monitor parking status in real-time through an intuitive graphical interface. With Node-RED, users can find out which parking lots are full and which areas are still available.
Automated Component Detection for Quality PCB Using YOLO Algorithm with IoT Real-Time Streaming on Raspberry Pi Waluyo Nugroho; Rifdah Zahabiyah; Mada Jimmy Fonda Arifiant; Afianto Afianto
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1313

Abstract

This paper presents the development of an automated component detection system for quality control in Printed Circuit Boards (PCBs) by integrating the YOLO object detection algorithm with Internet of Things (IoT) real-time streaming on a Raspberry Pi platform. The proposed system aims to address the challenges associated with traditional manual inspection methods, including time inefficiency, human error, and limited accuracy in detecting faulty components. The YOLO model, renowned for its high-speed and accurate object detection capabilities, was trained to identify various PCB components and deployed on a Raspberry Pi due to its affordability, portability, and low power consumption. To enable real-time remote monitoring and analysis, IoT capabilities were incorporated using the MQTT protocol, allowing seamless data transmission to remote servers or devices. The experimental results demonstrated the effectiveness of the proposed system, achiev-ing an average detection accuracy of 95%, making it a reliable solution for real-time quality assurance in PCB manufacturing. The novelty of this study lies in the innovative integration of the YOLO algorithm with IoT technology on a cost-efficient platform, providing a scalable and practical solution for automating PCB inspection processes. This approach not only enhances inspection efficiency but also reduces operational costs, offering significant value to the electronics manufacturing industry. Future work will focus on scaling thesystem for broader applications and improving the detection capabilities for more complex PCB designs.
Optimizing YOLOv8 architecture using particle swarm optimization for high-precision binary quality classification in industrial welding seams Waluyo Nugroho; Heru Suprapto; Muhammad Hidayat
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.84

Abstract

The structural integrity of heavy machinery fundamentally depends on precise welding quality. However, traditional manual inspections remain inconsistent, labor-intensive, and susceptible to human error. While You Only Look Once v8 (YOLOv8) architectures have become the standard for real-time object detection, their performance in accurately classifying micro defects like porosity or cracks is frequently hindered by suboptimal default hyperparameters. To overcome this limitation, this study proposes PSO YOLOv8, an intelligent framework integrating the Particle Swarm Optimization (PSO) algorithm to automatically tune YOLOv8 critical hyperparameters, specifically learning rate, batch size, and weight decay. The framework was evaluated using a specialized dataset of 2,600 high resolution welding seam images, strictly categorized into Normal and Defective classes. Utilizing validation Mean Average Precision (mAP) as the fitness function, PSO was configured to maximize accuracy over 50 iterations. Experimental results demonstrate a substantial performance enhancement. The PSO optimized model achieved an mAP@50 of 94.2%, a significant improvement over the 83.7% baseline. Furthermore, the optimized configuration attained a 96.5% Precision rate, effectively reducing false-positive detections by 38.4%. These findings validate that fusing metaheuristic algorithms with deep learning provides a robust, high precision tool for automated quality assurance in smart manufacturing.
Random Forest Regression for Energy Consumption Prediction on Raspberry Pi Edge Computing Mada Jimmy Fonda Arifianto; Waluyo Nugroho; Afianto
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.632

Abstract

Efficient energy management in smart homes is critical for cost reduction and sustainability, yet conventional cloud-based monitoring systems often face challenges related to network latency, bandwidth consumption, and data privacy. This study proposes an Edge Computing architecture to predict electrical energy consumption locally using a Raspberry Pi, thereby eliminating the dependency on continuous cloud processing. The system integrates a PZEM-004T sensor to acquire real-time voltage, current, and power data, while the core intelligence is built upon the Random Forest Regression (RFR) algorithm trained and deployed directly on the Raspberry Pi to forecast short-term energy load based on historical usage patterns and Internet of Things (IoT). Experimental results demonstrate that the proposed edge system achieves high prediction accuracy with an R2 score of 0.94 and a Mean Absolute Percentage Error (MAPE) of 4.25%, and a Root Mean Square Error (RMSE) of 12.80 Watts using a model configuration of 100 estimators, confirming that Raspberry Pi based edge computing is a viable, low latency, and privacy preserving solution for intelligent energy management
Automated Waste Classification for Sustainable Cities Using YOLO Based CNN Integrated IoT Waluyo Nugroho; Adnan Alfattah; Mada Jimmy Fonda Arifianto; Aswan Hadi
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.582

Abstract

Sustainable waste management is a vital component of smart city development, directly impacting environmental quality and recycling efficiency. This study presents an IoT-enabled waste classification system that utilizes a Convolutional Neural Network (CNN) for accurate, real-time identification of organic and non-organic waste. The model, implemented using the YOLO architecture, was trained on a diverse dataset of waste images captured under various environmental conditions to ensure robustness in practical scenarios. Classification results are automatically stored in a MySQL database and visualized via an Internet of Things (IoT) based Node-RED dashboard, enabling municipal operators to monitor waste categories and quantities remotely. Field evaluations demonstrate that the system achieves an accuracy of 94%, precision of 94.5%, recall of 93.2%, and an F1-score of 93.85%, indicating high detection reliability and consistent performance, even in challenging urban environments. By integrating CNN-based deep learning with IoT visualization tools, this approach offers a scalable and efficient solution that supports sustainable waste management initiatives within smart city frameworks.
Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection Waluyo Nugroho; Rifdah Zahabiyah; Afianto; Mada Jimmy Fonda Arifianto
Jurnal E-Komtek Vol 8 No 2 (2024)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v8i2.2187

Abstract

The use of Personal Protective Equipment (PPE) is a critical step in ensuring worker safety in various sectors, including industry, construction, and health. However, violations in using PPE often occur, which can increase the risk of work accidents. This study aims to develop a deep learning-based PPE detection system using the YOLOv8 algorithm. This method was chosen because of its superior ability to detect objects in real time with high accuracy. The training data consists of various images of workers in different work environments, label to recognize types of PPE such as helmets, masks, and safety vests. The developed system was tested on a test dataset to evaluate model performance based on metrics such as confusion matrix, inference speed, and detection error rate. The experimental results show that the YOLOv8 model can detect PPE with an accuracy level of up to 95%. The implementation of this system is expected to be an effective solution in increasing compliance with the use of PPE and preventing work accidents.
Design and Implementation of an IoT-Enabled Deep Learning Vision System for Automated Dimensional Measurement in Smart Manufacturing Waluyo Nugroho; Afianto; Agus Ponco
Jurnal E-Komtek Vol 9 No 2 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/e-komtek.v9i2.2855

Abstract

The rapid advancement of Industry 4.0 has brought the convergence of Internet of Things (IoT), computer vision, and deep learning to enhance automation and precision in manufacturing. This paper presents the design and implementation of an IoT-enabled deep learning vision system for automated dimensional measurement, integrated with programmable logic controller (PLC) control and real-time monitoring. The system employs a Raspberry Pi 5 as an edge computing unit, Logitech C270 camera for visual data acquisition, and an Omron CP2E PLC for process control. A YOLOv5 deep learning model is trained to detect and measure object dimensions with sub-millimeter accuracy. The Node-RED platform is utilized for dashboard visualization and communication, interfaced through Omron FINS protocol, with MySQL as the database for data logging. Experimental results show a high detection accuracy of 98.6% and an average measurement error of less than 0.5 mm, demonstrating the system’s effectiveness for smart manufacturing applications.
Co-Authors Adian Fatchur Rochim Adnan Alfattah Afianto Afianto Afianto Afianto Afianto Afianto Afianto, Afianto Agus Ponco Arifianto, Mada Jimmy Fonda Aswan Hadi Cahya, Khairunnisa Hadi, Aswan Heru Suprapto Mada Jimmy Arifianto Mada Jimmy Fonda Arifiant Mada Jimmy Fonda Arifianto Mada Jimmy Fonda Arifianto Mada Jimmy Fonda Arifianto Muhammad Hidayat Nursim, Nursim R Rizal Isnanto Rifdah Zahabiyah Wicaksono, Andreadie Zahabiyah, Rifdah