Article Per Year (5 Year)
p-Index From 2021 - 2026
3.143
P-Index
This Author published in this journals
All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Ekuitas: Jurnal Pendidikan Ekonomi JUTIK : Jurnal Teknologi Informasi dan Komputer WIDYABHAKTI Jurnal Ilmiah Populer IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Sains dan Teknologi Ekuitas Jurnal Pnedidikan Ekonomi Majalah Ilmiah Teknologi Elektro KOMET
I Gusti Agung Indrawan
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Classification Of Organic And Inorganic Waste Using Resnet50 Qinantha, I Kadek Mahesa Chandra; Indrawan, I Gusti Agung; Putra, I Putu Satria Udyana; Aristamy, I Gusti Ayu Agung Mas; Willdahlia, Ayu Gede
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.267

Abstract

Waste generation, particularly from organic and inorganic sources, has become a growing environmental issue, especially in culturally unique regions like Bali where traditional offerings contribute to organic waste volumes. Despite regulations such as Gianyar Regency Regulation No. 76 of 2023 mandating source-level separation, on-ground implementation remains inconsistent due to low public awareness and operational limitations. This study addresses the challenge by developing an automated image-based classification system using the ResNet50 deep learning architecture to distinguish between organic and inorganic waste. A total of 200 images were collected 100 per class using smartphone cameras, and the dataset was expanded to 1,400 images through geometric data augmentation techniques such as rotation, flipping, and zooming. Images were resized to 224x224 pixels and evaluated using K-Fold Cross Validation to ensure model stability. The model was trained using transfer learning and tested under two conditions with and without augmentation while optimizing hyperparameters such as learning rates (0.0001 and 0.00001) and optimizers (Adam and SGD). The results demonstrate that augmentation significantly enhanced model performance, with the augmented model achieving an average accuracy of 99.25%, precision of 99.32%, recall of 99.25%, and F1-score of 99.25%, compared to 89.88% accuracy in the non-augmented model. These findings confirm that ResNet50, when combined with geometric augmentation and proper preprocessing, offers a robust, accurate, and scalable solution for waste classification tasks. This research contributes to the advancement of AI-driven environmental technologies and offers a potential framework for smart waste management systems, with future directions including real-time deployment, multi-class classification, and expansion to more diverse and real-world datasets.
Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies Udayana, I Putu Agus Eka Darma; I Gusti Agung Indrawan; Putra, I Putu Dwi Guna Ambara
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1999

Abstract

Sentiment analysis is the process of classifying a text dataset as positive, negative or neutral. Youtube is one of the popular media used to provide responses to a problem. In the Jokowi era, infrastructure development was carried out massively and evenly, one of which was in Bali Province, namely the construction of the Mengwi-Gilimanuk Toll Road. The construction of the Mengwi-Gilimanuk Toll Road consumed a lot of people's agricultural land, which resulted in various pro and con responses from the community. From these problems, sentiment analysis is carried out to get community reviews related to the object being analyzed by utilizing algorithms to be able to classify opinions, in the construction of this system the naïve bayes algorithm is used with testing methods namely accuracy, precision, and recall. From the sentiment analysis conducted by utilizing 18 video links on YouTube with 701 comments, it produces positive sentiment as much as 50.64%, negative sentiment as much as 7.70% and neutral sentiment as much as 39.23%.
Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays Udayana, I Putu Agus Eka Darma; Indrawan, I Gusti Agung; Prawira, I Made Karang Satria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4345

Abstract

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
Video Company Profile Sebagai Media Promosi Pratama Widya Pasraman Rare Semesta Anggara, I Gede Adi Sudi; Setiawan, Antonio Henry; Indrawan, I Gusti Agung; Setiawan, I Ketut; Fittryani, Yuri Prima
Jurnal KOMET Vol 1 No 1 (2024): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 1 Nomor 1, Juni 2
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v1i1.10

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan kesadaran masyarakat tentang Pratama Widya Pasraman Rare Semesta dan pentingnya pendidikan usia dini di Desa Besakih. Berdasarkan analisis situasi, ditemukan bahwa banyak orang tua tidak mengetahui keberadaan sekolah ini dan menganggap pendidikan usia dini tidak penting, yang berdampak pada rendahnya jumlah siswa. Solusi yang diambil adalah merancang dan memproduksi video company profile sebagai media promosi. Proses produksi meliputi pengumpulan data melalui wawancara, observasi, dan kuesioner, serta pembuatan video dalam tiga tahap: praproduksi, produksi, dan pascaproduksi. Hasil pengujian video menunjukkan penilaian sangat baik dari orang tua (98,2%), ahli konten (100%), dan ahli media (93,7%). Video ini efektif dalam menyampaikan informasi dan meningkatkan kesadaran orang tua tentang pentingnya pendidikan usia dini, serta berhasil memperkenalkan Pratama Widya Pasraman Rare Semesta kepada masyarakat.
Co-Authors Aniek Suryanti Kusuma, Aniek Suryanti Aranta, Arik Aristamy, I Gusti Ayu Agung Mas Atmaja, Ketut Jaya Ayu Gede Wildahlia Ayu Manik Dirgayusari Christina Purnama Yanti I Gede Adi Sudi Anggara I Gede Agung Chandra Wijaya I Gusti Ayu Anom I Ketut Setiawan I Komang Arya Ganda Wiguna I Made Marthana Yusa I Nyoman Agus Suarya Putra I Nyoman Anom Fajaraditya I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Dwi Guna Ambara Putra I Putu Eka Giri Gunawan I Wayan Agus Surya Darma Ida Bagus Ary Indra Iswara Lie Jasa Made Sudarma Murpratiwi, Santi Ika Ni Kadek Ariasih Ni Kadek Ariasih, Ni Kadek NI LUH PUTU MERY MARLINDA Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Sutramiani Pramitha, Gede Dana Prawira, I Made Karang Satria Putra, I Putu Dwi Guna Ambara Putra, I Putu Satria Udyana Putu Andy Novit Pranartha Putu Surya Wedra Lesmana Putu Wirayudi Aditama Qinantha, I Kadek Mahesa Chandra Sandika, I Kadek Budi Santi Ika Murpratiwi Setiawan, Antonio Henry Sutarwiyasa, I Ketut Tjokorda Istri Agung Pandu Yuni Maharani Welda Widiartha, Komang Kurniawan Willdahlia, Ayu Gede Yuri Prima Fittryani, Yuri Prima