Article Per Year (5 Year)
p-Index From 2021 - 2026
3.493
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 : 38 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 38 Documents
Search

 1   2   3   4 
Analisis Sentimen dan Pemodelan Topik Ulasan Aplikasi Noice Menggunakan XGBoost dan LDA Ikegami, Alim; Darmawan, I Dewa Made Bayu Atmaja
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In recent years, audio content has seen a rise in consumption. The COVID-19 pandemic also contributes to the rise in consumption. In the survey that was conducted in 2021, more than 40% of people in France, Germany, and Spain have been listening to more audio content since the first restriction on COVID-19 came into place [1]. One of the rising startups in Indonesia that offers audio content with their original and exclusive content is Noice. To maintain their quality of service, it’s important to look into the reviews that were written for their application. To analyze the reviews, sentiment analysis and topic modeling can be used to extract the sentiment polarity and the topics that are discussed on each sentiment polarity [3]. In this study, XGBoost and Latent Dirichlet Allocation are used to analyze the reviews that were written in Google Play Store. The result of the sentiment analysis yielded accuracy, precision, recall, and F1-score of 87,5%, 84,8%, 79,4%, and 81,6%. While the topic modeling managed to extract 16 and 6 topics respectively for positive and negative reviews.
Pemodelan Topik Teks Berita Menggunakan DistilBERT Mahesastra, I Made Anditya; Darmawan, I Dewa Made Bayu Atmaja
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Online newspapers are content that can be a source of information or entertainment for the audience. There are so many online newspapers on the internet and also from various publishers. This condition causes the available news to be very varied and with different structures. If in certain cases we want to group these online newspapers efficiently, then a technique will be needed that will be able to group these online newspapers efficiently into several groups so that the available online newspapers can be more structured to be enjoyed according to the needs and tastes of the readers. The technique that can be applied is topic modeling. In the case of modeling the topic of Indonesian online newspapers, currently LDA is one of the most widely applied algorithms. So, this study aims to determine whether the performance of using the DistilBERT model will be better or not when compared to commonly used algorithms such as LDA for topic modeling tasks in Indonesian online newspapers.
Pemodelan Topik Pada Ulasan Hotel Menggunakan Metode BERTopic Dengan Prosedur c-TF-IDF Mertayasa, I Komang Tryana; Darmawan, I Dewa Made Bayu Atmaja
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

User review data on travel guidance services can be useful textual data for other users. By knowing what topics are discussed in user reviews in hotel products, travel guidance service providers can group these reviews based on the topics discussed. In grouping textual data into several topics, the use of topic modeling methods can be done. In this study, the author uses the BERTopic method in modeling topics on user review data related to hotel products on one of the TripAdvisor travel guidance services. This study uses secondary data in the form of hotel reviews on the TripAdvisor site. Topic modeling with BERTopic begins with document embedding, dimensionality reduction (UMAP), clustering (HDBSCAN), and c-TF-IDF. Topic modeling using the BERTopic method resulted in 78 topics with a topic coherence value of 0.07287 and a topic diversity of 0.496154. The lower the number of topics to be generated, the value of topic coherence and topic diversity decreases
Klasifikasi Kesegaran Daging Sapi Menggunakan Deep Learning Arsitektur VGG16 Dengan Augmentasi Citra Elia, Benny; Darmawan, I Dewa Made Bayu Atmaja
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Beef is one of the best foods to eat. However, to identify beef manually has a weakness, namely the limitations of human visual abilities. Therefore it is necessary to have an indicator of the level of freshness of the meat. This study uses image augmentation to change or modify images and use Convolutional Neural Network (CNN) with VGG16 architecture to perform image classification. The accuracy obtained with epoch 15 and the distribution of data by 80% of training data and 20% of validation data is the highest accuracy obtained, which is 97.14% and when tested with validation data it gets an accuracy of 98.68%. Overall, the model that has been used can classify the freshness of beef well.
Identifikasi Citra Daun Tanaman Herbal menggunakan Convolutional Neural Network Setyawan, Marselinus Putu Harry; Darmawan, I Dewa Made Bayu Atmaja
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia is one of the countries with the highest number of herbal plant species in the world. However, it is not linear with people's knowledge about herbal plants and their health benefits. Leaves are one of the characteristics of plants that can be used to identify plant species because each plant has leaves and is easier to distinguish than tree bark. This research uses the Local Binary Pattern (LBP) method to obtain the texture features of leaf herbal plants, and the Convolutional Neural Network (CNN) to perform the classification. The highest accuracy was obtained with an epoch value of 25 and a batch size of 32. This combination resulted in a model with an accuracy of 95%, and when tested with validation data it produced an accuracy of 84%. Overall, the model that was built was able to identify the types of herbal plants very well.
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.
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 WAYAN SANTIYASA Ida Bagus Made Mahendra Ikegami, Alim 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 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