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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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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.
Co-Authors Adian Fatchur Rochim Afianto Afianto Afianto Afianto Afianto, Afianto Arifianto, Mada Jimmy Fonda Cahya, Khairunnisa Hadi, Aswan Heru Suprapto Mada Jimmy Fonda Arifiant Mada Jimmy Fonda Arifianto Muhammad Hidayat Nursim, Nursim R Rizal Isnanto Wicaksono, Andreadie Zahabiyah, Rifdah