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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal E-Komtek Jurnal INFOTEL TECHNOLOGIC
Nugroho, Waluyo
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering 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%.
Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection Nugroho, Waluyo; Rifdah Zahabiyah; Afianto; Mada Jimmy Fonda Arifianto
Jurnal E-Komtek (Elektro-Komputer-Teknik) 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.
Automated Component Detection for Quality PCB Using YOLO Algorithm with IoT Real-Time Streaming on Raspberry Pi Nugroho, Waluyo; Zahabiyah, Rifdah; Arifiant, Mada Jimmy Fonda; 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.
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.
Co-Authors Adian Fatchur Rochim Afianto Afianto, Afianto Arifiant, Mada Jimmy Fonda Arifianto, Mada Jimmy Fonda Cahya, Khairunnisa Hadi, Aswan Mada Jimmy Fonda Arifianto R Rizal Isnanto Rifdah Zahabiyah Zahabiyah, Rifdah