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Herbal plants are nutritious plants that can be used as an alternative totraditional disease healing. In Indonesia there are various types of herbalplants. But with the development of the times, the existence of herbal plantsas traditional medicines began to be forgotten so that not everyone couldrecognize them. Having the ability to identify herbal plants can have manypositive impacts. However, there is a problem where identifying plants can takea long time because it requires in-depth knowledge and careful examination ofplant criteria. So that the application of computer vision can help identifyherbal plants. Previously, research had been conducted on the introduction ofherbal plants from Vietnam using several algorithms, but from these researchthe accuracy was not high enough. Therefore, this study intends to implementtransfer learning from the Convolutional Neural Network (CNN) algorithm toclassify types of herbal plants from Indonesia. This research was conducted bycollecting image data of herbal plants from Indonesia independently through theGoogle Images search engine. After that, it will go through the datapreprocessing, classification using the transfer learning method from CNN, andanalysis will be carried out. The CNN transfer learning models used areResNet34, DenseNet121, and VGG11_bn. Based on the test results of the threemodels, it was found that DenseNet121 was the model with the highest accuracy,which was 87.4%. In addition, testing was also carried out using the scratchmodel and obtained an accuracy of 43.53%. The Hyperparameter configuration usedin this test is the ExponentialLR scheduler with a gamma value of 0.9; learningrate 0.001; Cross Entropy Loss function; Adam optimizer; and the number ofepochs is 50. Indonesia Medicinal Plant Dataset can be accessed at thefollowing link https://github.com/Salmanim20/indo_medicinal_plant

IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning