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All Journal Journal of Applied Data Sciences ARMADA : Jurnal Penelitian Multidisiplin
Malathy Batumalay
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Integration of Artificial Intelligence in the Preservation of Regional Arts and Languages: A Digital Humanities Approach in Indonesia Malathy Batumalay; Haryono; Masrin
ARMADA : Jurnal Penelitian Multidisiplin Vol. 3 No. 9 (2025): ARMADA : Jurnal Penelitian Multidisplin, September 2025
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/armada.v3i9.1756

Abstract

The development of artificial intelligence (AI) technology has opened up new opportunities for the preservation of cultural heritage, including endangered regional arts and languages. This study aims to analyze how the application of AI in the context of digital humanities can support efforts to document, revitalize, and disseminate local culture in Indonesia. The method used is descriptive-qualitative with a literature study approach and policy analysis of digital culture preservation projects that have been running, such as Google AI for Indigenous Languages and the Digital Culture program of the Ministry of Education, Culture, Research, and Technology. The results show that the integration of AI through Natural Language Processing (NLP), machine translation, speech recognition, and digital archiving is able to accelerate the process of documentation and learning of regional languages and encourage the participation of the creative community in the preservation of traditional arts. However, the main challenges lie in the limitations of linguistic data, algorithmic bias, and technological gaps between regions. This study concludes that the successful implementation of AI-based digital humanities depends on multidisciplinary collaboration between governments, academics, cultural communities, and technology developers.
Efficient Fruit Grading and Selection System Leveraging Computer Vision and Machine Learning Deshinta Arrova Dewi; Tri Basuki Kurniawan; Rajermani Thinakaran; Malathy Batumalay; Shabana Habib; Muhammad Islam
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.443

Abstract

Automated fruit grading is crucial to overcoming the time and accuracy challenges posed by manual methods, which are often limited by subjective human judgment. This study introduces an intelligent grading system leveraging computer vision and AI to improve speed and consistency in assessing fruit quality. Using high-resolution imaging and advanced feature extraction, including grayscale processing, binarization, and enhancement, the system achieves non-destructive, efficient sorting for fruits like apples, bananas, and oranges. Grayscale processing reduces image complexity while preserving essential details, binarization isolates the fruit from its background, and enhancement highlights critical features. Notably, the Edge Pixel method proved most effective, achieving 79.20% accuracy in grading, while the Grayscale Pixel method reached 93.94% accuracy for fruit types. Edge Pixel also achieved 80.32% in differentiating grading types, showcasing its ability to capture essential shapes and edges. Fruits are classified into four grades: Grade_01 (highest quality), Grade_02 (minor imperfections), Grade_03 (notable defects but consumable), and Grade_04 (unfit for consumption). A specialized dataset supports model training, ensuring practical real-world application. The study concludes that this automated system offers significant improvements over traditional grading, providing a scalable, objective, and reliable solution for the agricultural sector, ultimately enhancing productivity and quality assurance.
Modernizing Medicinal Plant Recognition: A Deep Learning Perspective with Data Augmentation and Hybrid Learning Preeti Nagrath; Malathy Batumalay; Dhruv Saluja; Harsh Kukreja; Devanshi Tegwal; Akash Saini
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.795

Abstract

This study proposes a deep learning-based solution to address the longstanding challenge of accurately identifying Indian medicinal plants, which are vital to Ayurvedic pharmaceutics but often misidentified due to their morphological similarities. The objective is to develop a reliable, automated classification system using image processing and advanced neural network architectures. A dataset of 5,945 images representing 40 distinct medicinal plant species was sourced from Kaggle and augmented to 11,890 images using techniques such as flipping, rotation, and scaling to enhance diversity. The models tested include a baseline Convolutional Neural Network (CNN), transfer learning with DenseNet121, DenseNet169, and DenseNet201, a voting ensemble of these DenseNet variants, and a hybrid DenseNet201-LSTM architecture. Experimental results show that the CNN model achieved the lowest accuracy at 69.58%, while the hybrid DenseNet201-LSTM model reached the highest validation accuracy of 93.38%, with a precision of 94.74%, recall of 93.38%, and F1-score of 93.42%. These findings confirm the hybrid model’s superior ability to capture spatial and sequential dependencies in leaf features. The novelty of this work lies in the integration of DenseNet201 with LSTM for medicinal plant classification, which has not been widely explored in this domain. The study also acknowledges dataset scalability as a limitation and proposes future work involving dataset expansion through botanical collaborations, integration of environmental metadata, and deployment of a mobile application using TensorFlow Lite for real-time, low-resource implementation. Overall, the research contributes a robust and scalable framework for medicinal plant identification, promoting trust in traditional medicine, supporting conservation efforts, and enabling practical field-level applications in both rural and clinical settings.
Co-Authors Akash Saini Deshinta Arrova Dewi Devanshi Tegwal Dhruv Saluja Harsh Kukreja HARYONO Masrin Muhammad Islam Preeti Nagrath Rajermani Thinakaran Shabana Habib Tri Basuki Kurniawan