Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network

Authors

  • Luther Latumakulita Sam Ratulangi University
  • Franklin Mandagi Sam Ratulangi University
  • Frangky Paat Sam Ratulangi University
  • Dedie Tooy Sam Ratulangi University
  • Sandra Pakasi Sam Ratulangi University
  • Sofia Wantasen Sam Ratulangi University
  • Diane Pioh Sam Ratulangi University
  • Rinny Mamarimbing Sam Ratulangi University
  • Bobby Polii Sam Ratulangi University
  • Jantje Pongoh Sam Ratulangi University
  • Arthur Pinaria Sam Ratulangi University
  • Edwin Tenda Sam Ratulangi University
  • Noorul Islam Kanpur Institute of Technology

DOI:

https://doi.org/10.31763/businta.v6i2.601

Keywords:

Medicinal plants, CNN, identification, leaf images, Cross-validation

Abstract

Indonesia has a variety of medicinal plants that are efficacious for preventing or treating various diseases. Each region has unique medicinal plants, such as in North Sulawesi, there are many medicinal plants with local names of "Jarak" (Jatropha curcas), "Jarak Merah" (Jatropha multifida), "Miana" (Coleus Scutellarioide), and "Sesewanua" (Clerodendron Squmatum Vahl). This research applies the Convolutional Neural Network (CNN) method to identify the types of medicinal plants of North Sulawesi based on leaf images. Data was collected directly by taking photos of medicinal plant leaves and then using the augmentation process to increase the data. The first stage is conducting training and validation processes using 10-fold cross-validation, resulting in 10 classification models. Evaluation results show that the lowest validation accuracy of 98.4% was obtained from fold-4, and the highest was 100% from fold-2. The third stage was to run the testing process using new data. The results showed that the worst model produced a test accuracy of 80.91% while the best model produced an accuracy of 87.73% which means that the identification model is quite good and stable in classifying types of medicinal plants based on its leaf images. The final stage is to develop a web-based system to deploy the best model so people can use it in real-time

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Published

2022-12-30

How to Cite

Latumakulita, L., Mandagi, F., Paat, F., Tooy, D., Pakasi, S., Wantasen, S., Pioh, D., Mamarimbing, R., Polii, B., Pongoh, J., Pinaria, A., Tenda, E., & Islam, N. (2022). Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network. Bulletin of Social Informatics Theory and Application, 6(2), 158–167. https://doi.org/10.31763/businta.v6i2.601

Issue

Vol. 6 No. 2 (2022)

Section

Articles

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Copyright (c) 2022 Luther Latumakulita, Franklin Mandagi, Frangky Paat, Dedie Tooy, Sandra Pakasi, Sofia Wantasen, Diane Pioh, Rinny Mamarimbing, Bobby Polii, Jantje Pongoh, Arthur Pinaria, Edwin Tenda, Noorul Islam

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