Article Per Year (5 Year)
p-Index From 2021 - 2026
3.815
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sains dan Teknologi Jurnal Buana Informatika Jurnal Ilmiah Kursor Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu
Program Studi Teknik Informatika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Udayana
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

Published : 35 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 35 Documents
Search

 1   2   3   4 
Implementation of Face Recognition for Attendance Recording in Online Learning Darmawan, I Dewa Made Bayu Atmaja
Jurnal Ilmu Komputer Vol 16 No 2 (2023): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2023.v16.i02.p06

Abstract

The utilization of facial recognition technology has become increasingly imperative within the realm of online learning. The current study introduces a novel system that utilizes face recognition technology to record attendance in online learning environments. The attendance system necessitates students to activate an attendance button, whereby their attendance is subsequently documented through facial recognition technology. The system recognizes students as present solely based on facial recognition. The system stores the duration of online learning activities in a database. Implementing machine learning methodologies, specifically face detection algorithms, improves precision and efficacy in administering student attendance in online education. The system utilizes Haar cascades in OpenCV to detect faces, extract features such as eyes, nose, and mouth, and classify them using LBPH. Through extensive experiments, an accuracy rate of 93.55% was achieved. The study demonstrates the effectiveness of the combined approach, showcasing the potential of Haar cascades and LBPH in face recognition tasks. The present study makes a valuable contribution to the domains of computer vision and educational technology by offering a pragmatic remedy for attendance tracking in virtual learning settings.
Utilizing Machine Learning Techniques for Learning Analytics: A Case Study of Moodle LMS Activity Log Analysis Darmawan, I Dewa Made Bayu Atmaja
Jurnal Ilmu Komputer Vol 17 No 1 (2024): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2024.v17.i01.p05

Abstract

Learning analytics collects data, analyzes, and interprets the learning process that has taken place. The output of this method can be used to improve the quality of teaching or learning. Moodle is a popular learning management system (LMS) used for online learning. Various learning activities carried out by students are recorded in the activity log. This paper shows the potential of using machine learning methods to analyze activity logs taken from Moodle LMS. The sample used in this study refers to implementing the Digital Society course, which students from different fields of science attend. This paper describes using supervised and unsupervised learning on activity log data taken from the Moodle LMS. The variables used as datasets include the frequency of activity reading pdf material, scores, videos, forums, quizzes, and graduation status. The supervised learning model that was built succeeded in obtaining an accuracy of 100% in the application of logistic regression and Naïve Bayes Classification. Unsupervised learning clustered all the data and showed the cluster related to the frequency of online learning activities and students' assessment success status.
SISTEM INSTALASI AIR RUMAH TERKOMPUTERISASI BERBASIS MIKROKONTROLER DENGAN PERINTAH SMS Darmawan, I Dewa Made Bayu Atmaja; Mogi, I Komang Ari; Santiyasa, I Wayan
JST (Jurnal Sains dan Teknologi) Vol. 6 No. 1 (2017)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (937.851 KB) | DOI: 10.23887/jstundiksha.v6i1.9388

Abstract

Penelitian ini berangkat dari permasalahan yang dialami masyarakat Jimbaran dalam mengelola instalasi air karena permasalahan distribusi air yang sering bermasalah. Masyarakat pada kawasan tersebut pada umumnya memiliki dua buah tandon air, tandon tertanam dan tandon atas. Suplai air didapatkan dari perusahaan air minum daerah yang sering kali mengalami permasalahan dalam distribusinya. Pihak ketiga penyedia air bersih digunakan sebagai alternatif untuk memenuhi kebutuhan air bersih. Permasalahan tersebut mengharuskan pemilik rumah harus mengetahui kondisi tandon air bawah tanah dan atas sebelum memutuskan untuk membeli air bersih di pihak ketiga. Hal tersebut relatif sulit dilakukan dengan pengamatan langsung. Sistem mikrokontroler digunakan sebagai solusi untuk permasalahan tersebut. Sistem yang dibangun memungkinkan pengguna dalam mengontrol instalasi air. Fitur yang dibangun meliputi, notifikasi kapasitas air di setiap tandon air, mengontrol secara otomatis pompa air untuk mengisi tandon atas, mengontrol secara otomatis keran PDAM. Fitur tersebut didukung dengan layanan SMS. Sehingga pemilik rumah dapat mengontrol dan monitoring instalasi air rumah menggunakan layanan SMS.
Klasifikasi Berita Berdasarkan Kategori Menggunakan Convolutional Neural Network dengan IndoBERT Jonathan Federico Tantoro; I Dewa Made Bayu Atmaja Darmawan
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 4 (2025): JNATIA Vol. 3, No. 4, Agustus 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i04.p20

Abstract

The advancement of technology information has led to a significant increased the volume of digital news, that makes needs for automatic news classification. This study aims to design a model capable of caterogizing Indonesian language news articles into six predefined categories, such as News, Money, Bola, Health, Tekno, and Tren. To achieve this goal, the method used combines IndoBERT as the embedding technique with Convolutional Neural Network (CNN) as the classification algorithm. The dataset consists of 3.000 news articles collected from Kompas.com and is divided into training data and testing data using four different data split ratios: 60:40, 70:30 80:20, and 90:10 . The evaluation results show that the best performance was achieved using the 80:20 ratio, where the model reached an accuracy of 91%, along with high precision, recall, and F-1 Score These result prove that the combination of IndoBERT and CNN is effective for the automatic classification of Indonesian new texts.
Model Penerjemah Bahasa Isyarat SIBI Statis Berbasis Convolutional Neural Network Ni Made Wipra Ranum Ratnayu; I Dewa Made Bayu Atmaja Darmawan; I Putu Gede Hendra Suputra
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 4 No. 1 (2025): JNATIA Vol. 4, No. 1, November 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v04.i01.p21

Abstract

This study addresses the communication gap between deaf and hearing communities by developing an optimal sign language recognition system for Indonesian Sign Language System (SIBI) static gestures. A comprehensive comparative analysis was conducted on four VGG architecture variants (VGG-11, VGG-13, VGG-16, and VGG-19) using a dataset across 10 SIBI word classes. The research employed systematic methodology including data extraction from video sources, preprocessing with augmentation techniques, model training over 25 epochs, and comprehensive evaluation using accuracy, precision, recall, and F1-score metrics. Results demonstrate that VGG-16 achieves superior performance with 83.4% accuracy, 85.2% precision, 83.4% recall, and 82.9% F1-score, establishing optimal balance between model complexity and generalization capability. The study reveals diminishing returns phenomenon in VGG-19 despite increased architectural complexity. Computational efficiency analysis shows VGG-11 provides highest efficiency score (10.46 GFLOPs) while VGG-16 maintains optimal accuracy-efficiency trade-off. These findings provide crucial insights for developing effective assistive technology solutions that bridge communication barriers for the Indonesian deaf community.
Co-Authors Alim Ikegami Anak Agung Gde Ramananda Kartikeya Pattraksha Anak Agung Istri Ngurah Eka Karyawati Ari Mogi, I Komang Arvanchrist Charlie Wijaya Brata, I Putu Bayu Wira Budhiono, Sofia Shieldy Christian, Yuriko Dharmajaya, Gede Putra Elia, Benny I Gede Alanda Indra Kusuma I Gede Angga Narotama I Gede Arta Wibawa I Gede Erwin Winata Pratama I Gusti Agung Gede Arya Kadyanan I Gusti Ayu Nyoman Tri Wahyuni Mahayani I Ketut Gede Suhartana I Ketut Gede Suhartana I Komang Ari Mogi I Komang Ari Mogi, I Komang Ari I Komang Tryana Mertayasa I Made Anditya Mahesastra I Made Widiartha I Nyoman Arlan Kusuma Ardika I Putu Bayu Wira Brata I Putu Gede Hendra Suputra I WAYAN SANTIYASA Ida Bagus Made Mahendra Ikegami, Alim Jonathan Federico Tantoro Luh Arida Ayu Rahning Putri Luh Gede Astuti Made Rusdinda Hartani Maha, Ni Made Krisna Mahesastra, I Made Anditya Marselinus Putu Harry Setyawan Mertayasa, I Komang Tryana Muhammad Arief Budiman Ni Luh Putu Dianti Parasmitha Sari Ni Made Wipra Ranum Ratnayu Noak, Devin Reness Novita Dewi, Putu Mira Prianta, I Made Yoga Putra, Angga Pramana Raharja, Made Agung Satya, I Dewa Gede Rama Setyawan, Marselinus Putu Harry Sri Devi, Ni Komang Wiantari, Ni Wayan Yuriko Christian