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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro JUTIK : Jurnal Teknologi Informasi dan Komputer Journal of Social Work and Empowerment Majalah Ilmiah Teknologi Elektro
I Wayan Agus Surya Darma
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Social Sciences Other

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Strawberry Disease Detection Based on YOLOv8 and K-Fold Cross-Validation Pranata, I Made Dicky; Darma, I Wayan Agus Surya; Sandhiyasa, I Made Subrata; Wiguna, I Komang Arya Ganda
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 11 No 3 (2023): Vol. 11, No. 3, December 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2023.v11.i03.p06

Abstract

Strawberry plant diseases can be detected by the condition of the strawberry leaves, flowers, and fruit, but farmers still need knowledge to identify the type of strawberry disease. This study aims to develop a detection model using YOLOv8. The detection model was trained using a dataset containing 3,243 images of strawberry plant leaves, fruit, and flowers, divided into seven disease classes and one healthy plant class. This study aims to develop a more effective strawberry plant disease detection technology. The proposed method is based on YOLOv8 by applying K-Fold Cross Validation to the detection model training and applied data albumentations to produce a robust model. Based on the experimental results, it shows that the YOLOv8s model obtained the highest precision, recall, F1-score, and mean average precision values of 1.00, 0.94, 0.84, and 0.885 respectively.
EKSTRAKSI FITUR AKSARA BALI MENGGUNAKAN METODE ZONING I Wayan Agus Surya Darma; I K. G. Darma Putra; Made Sudarma
Jurnal Teknologi Elektro Vol 14 No 2 (2015): (July - December) 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.2015.v14i02p09

Abstract

Feature extraction is an important process in character recognition system. The purpose of this process is to obtain special feature from a character image. This paper is focuses on how to obtain special feature from a handwritten Balinese character image using zoning. This algorithm dividing Balinese character image into multiple regions, then a special feature on each region resulting the data extracted feature. The test result in this paper generates a various  semantic and direction feature data. This is because this paper using handwritten Balinese character. Furthermore, the features that produced in this paper can be used on Balinese character image recognition process
Enhancing Breast Cancer Recognition in Histopathological Imaging Using Fine-Tuned CNN Darma, I Wayan Agus Surya; Sutramiani, Ni Putu
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 12 No 3 (2024): Vol. 12, No. 3, December 2024
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2024.v12.i03.p04

Abstract

Global Cancer Statistics reports that of the 2.3 million cases of breast cancer worldwide, 600,000 result in death. Factors contributing to breast cancer in women include both genetic and lifestyle influences. One method for recognizing breast cancer is through histopathology images. Recently, deep learning has gained significant attention in machine learning due to its powerful capabilities in modeling complex data, such as images. In this study, we classify breast cancer by training a Convolutional Neural Network (CNN) model on a dataset of histopathology images annotated and validated by experts, containing two classes. We propose an optimization strategy for CNN models to enhance breast cancer recognition performance, applying a fine-tuning strategy to MobileNetV2 and InceptionResNetV2 to evaluate CNN performance in classifying breast cancer within histopathological images. The experimental results demonstrate that the model achieves optimal performance with an accuracy of 96.22%.
Co-Authors Anak Agung Ngurah Hary Susila Arsa, Dewa Made Sri Desak Ayu Sista Dewi Dwikasari, Ni Made Dita Dyanggi, Anak Agung Mayra Candra I Gusti Agung Indrawan I ketut Gede Darma Putra I Putu Eka Giri Gunawan Kusuma, Nyoman Tri Rahaditya Made Sudarma Ni Putu Sutramiani Pramana, Made Dimas Aldi Dwi Pranata, I Made Dicky Putri, Komang Sri Yanisa Putu Andy Novit Pranartha Putu Praba Santika Sandhiyasa, I Made Subrata Sastaparamitha, Ni Nyoman Ayu J. Wayan Gede Suka Parwita Wiguna, I Komang Arya Ganda Wiguna, I Putu Yoga Kresna