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All Journal Indonesian Journal of Data and Science Jurnal Multidisiplin Madani (MUDIMA)
Ida Bagus Ary Indra Iswara
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics Social Sciences

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The Balinese Lontar Manuscript Metadata Model: An Ontology-Based Approach Ida Bagus Gede Sarasvananda; Putu Gede Surya Cipta Nugraha; Ida Bagus Ary Indra Iswara
Jurnal Multidisiplin Madani Vol. 3 No. 9 (2023): September, 2023
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/mudima.v3i9.5850

Abstract

The digitization of cultural heritage, particularly lontar manuscripts, is a focus of research to aid stakeholders in the management of lontar metadata. Managing the metadata of Balinese Lontar manuscripts through the application of ontology is one method for ensuring the preservation and accessibility of Balinese Lontar manuscripts. This research aims to apply ontology technology to the preservation of the cultural heritage of Balinese lontar manuscripts so that all information about lontar details can be categorized according to their respective properties and relevant information can be presented based on the user's preferences. This research method employs the stages of the Design Science Research Methodology (DSRM) when devising the ontology for the Balinese lontar manuscript. The results demonstrated that the construction of metadata using an ontology-based methodology can provide the information required to describe, categorize, and connect ontology entities. Metadata consists of entity descriptions, hierarchies and classifications, relationships and properties, as well as the necessary semantics for constructing effective ontologies
Classification Of Bougainvillea Flower Varieties Using Variant Of CNN: Resnet50 I Gede Agung Chandra Wijaya; I Gusti Agung Indrawan; I Nyoman Anom Fajaraditya; Ayu Gede Wildahlia; Ida Bagus Ary Indra Iswara
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.v6i2.266

Abstract

Bougainvillea is a tropical ornamental plant renowned for its vibrant colors and variety of cultivars, yet classifying its species remains challenging due to morphological similarities. This study aims to develop an automated classification system using the ResNet50 deep learning architecture to identify Bougainvillea flower varieties based on visual imagery. The dataset consists of 700 images from seven distinct classes, captured under natural lighting using a smartphone camera. The research process includes image preprocessing (resizing to 224x224 pixels), geometric data augmentation to increase dataset diversity, and evaluation using K-Fold Cross Validation to ensure robust model assessment. The model was trained using transfer learning, and its performance was compared between augmented and non-augmented datasets through evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that augmentation significantly improved the model's performance, achieving an average accuracy of 99.67% on augmented data compared to 93.39% on non-augmented data. The augmented model also exhibited greater consistency across all folds, with several achieving perfect scores. These findings highlight that combining ResNet50 with transfer learning and image augmentation produces a highly accurate and reliable Bougainvillea classification system. This research contributes to the field of AI-based plant phenotyping and lays the groundwork for future applications in horticulture, biodiversity conservation, and education. Further development is recommended to explore larger and more diverse datasets, investigate advanced architectures such as EfficientNet or Vision Transformers, and build real-time mobile-based classification tools for practical field usage
Co-Authors Ayu Gede Wildahlia I Gede Agung Chandra Wijaya I Gusti Agung Indrawan I Nyoman Anom Fajaraditya Ida Bagus Gede Sarasvananda Putu Gede Surya Cipta Nugraha