Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Majalah Ilmiah Teknologi Elektro Jurnal Informatika: Jurnal Pengembangan IT
Nugraha, I Putu Elba Duta
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Rancang Bangun Modul Praktikum Programmable Logic Controller Berbasis Outseal PLC Langit, Sekar; Rahardjo, Pratolo; Nugraha, I Putu Elba Duta
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P18

Abstract

In this research, the design of PLC trainer tools and its accessories was carried out to support the making of practicum modules for Programmable Logic Controller + Lab courses by utilizing the Outseal PLC Mega V3 Standard PP board. There are three experimental designs for the practicum module of the Programmable Logic Controller + Lab course, such as running light, water level control, and application of oil to the machine. In general, the design of this practicum module is divided into 3 stages, including system design, hardware design, and software design. Keyword – PLC Trainer; Practicum; Outseal PLC Mega V3 Standard
Rancang Bangun Trainer Modul Praktikum Programmable Logic Controller Berbasis Outseal PLC Mega V.3 Standar PP Gea, Susi Susanti; Rahardjo, Pratolo; Nugraha, I Putu Elba Duta
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P20

Abstract

The aim of this research is to design and build a learning trainer using the Outseal Mega V.3 Standard PP PLC board to support the practical in the Programmable Logic Controller + Lab course. The method used in this research is divided into three stages, such as including system design, hardware design, and software design. Testing the practical module of Programmable Logic Controller + Lab consists of three experiments, such as basic operation of Outseal PLC, logic gates, and bell quiz. Based on the result, it can be concluded that the 3 test experiments can be declared successful because the standard Outseal Mega V.3 PLC can be controlled to receive, send, and store data.
Sistem Presensi Otomatis Menggunakan Pengenalan Wajah Berbasis Deep Learning dan Real-Time Database Nugraha, I Putu Elba Duta; Sukadarmika, Gede
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8792

Abstract

The attendance system is a crucial component in the operations of any organization. However, most existing attendance systems still require significant time or manual intervention from users. This study aims to develop a deep learning-based face recognition application with a real-time database to record attendance automatically. This approach is expected to make the attendance process more accurate, faster, and more convenient compared to traditional attendance methods. The study employs a quantitative method through primary data analysis from laboratory testing using dummy data. This testing aims to measure the accuracy of the face recognition system in automatically recording attendance. A face recognition application prototype has been successfully developed with real-time database integration using the Python programming language. The test results show that the application can recognize all faces in the database with a very high accuracy level. The system performance metrics indicate an accuracy of 99.1%, precision of 98.7%, recall of 98.7%, and F1-score of 98.7%. Additionally, the model has been implemented on an NVIDIA Jetson Nano mini-processor, demonstrating efficient operation on low-power hardware and real-time face recognition with optimal processing speed.
Co-Authors Gea, Susi Susanti Gede Sukadarmika Langit, Sekar Pratolo Rahardjo