• Adri Gabriel Sooai Universitas Katolik Widya Mandira Kupang, Indonesia
  • Sisilia Daeng Bakka Mau Universitas Katolik Widya Mandira Kupang, Indonesia
  • Yovinia Carmeneja Hoar Siki Universitas Katolik Widya Mandira Kupang, Indonesia
  • Donatus Joseph Manehat Universitas Katolik Widya Mandira Kupang, Indonesia
  • Shine Crossifixio Sianturi Universitas Sanata Dharma Yogyakarta, Indonesia
  • Alicia Herlin Mondolang Universitas Negeri Malang, Indonesia




Machine Learning, classification, Feature Extraction, image processing, lantana


As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers.  This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy.


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