Developing Artificial Intelligence Prototype for Classifying Crude Stingless Bee Propolis towards Innovation in Dentistry

Authors

  • Danica Sawa Samuel Student, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital, 47000 Sungai Buloh, Selangor, Malaysia
  • Alexander Jusmin Student, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital, 47000 Sungai Buloh, Selangor, Malaysia
  • Nur’Azira Zulkifli Student, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital, 47000 Sungai Buloh, Selangor, Malaysia
  • Zaidah Ibrahim Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Norizan Mat Diah Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Nurul Ain Ramlan Centre of Comprehensive Care Studies, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital, 47000 Sungai Buloh, Selangor Malaysia
  • Ikmal Hisham Ismail Centre of Comprehensive Care Studies, Faculty of Dentistry, Universiti Teknologi MARA, Jalan Hospital, 47000 Sungai Buloh, Selangor Malaysia

DOI:

https://doi.org/10.12974/2311-8695.2025.13.04

Keywords:

Artificial Intelligence, Nutraceutical, Propolis, Prototype

Abstract

Introduction: Stingless bee propolis demonstrates excellent therapeutic properties and its innovation in dentistry. Due to species-specific variations in chemical composition, the nutraceutical potential of each propolis type may be differ.

Purpose: To create an Artificial Intelligence (AI) prototype to classify three different propolis namely Geniotrigona thoracica, Heterotrigona itama, and Tetrigona apicalis, using crude macerated, SEM images.

Methods and Materials; An AI prototype was developed with AI model to perform propolis classification using crude macerated SEM images, for each species. Fine-tuning on the number of images and the number of epochs of the AI model is performed to achieve high performance, which will then use to develop a prototype.

Results: Initial experiment with a small dataset achieved accuracy of 0.67. After augmenting the dataset with 50 additional images per species, accuracy improved to 0.86 after 80 epochs. Other metric evaluations such as precision, recall and F1 score have also been applied to measure the AI model’s performance. The results demonstrate that as the size of the training images and epoch increase, the classification performance is improved. This model has been utilized to develop a prototype with graphical user interface that allows user to insert a new macerated SEM images and the prototype will reveal the propolis species. Expert verification of this prototype has been performed, for use to classify the propolis.

Conclusion: The accuracy of propolis image classification can be further enhanced by using a larger dataset and a higher epoch value. The finding of this study can be used as quality control procedures to profile stingless bee propolis by improving the selection and enriching sustainable sources, as nutraceuticals, employed in dentistry. Future work include the utilization of colour images of crude macerated propolis.

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Published

06-10-2025

How to Cite

Samuel, D. S. ., Jusmin, A., Zulkifli, . N. ., Ibrahim, Z. ., Diah, N. M., Ramlan, N. A., & Ismail, I. H. (2025). Developing Artificial Intelligence Prototype for Classifying Crude Stingless Bee Propolis towards Innovation in Dentistry. The Journal of Dentists, 13, 18–25. https://doi.org/10.12974/2311-8695.2025.13.04

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Articles