Herbal plants are nutritious plants that can be used as an alternative to traditional disease healing. In Indonesia there are various types of herbal plants. But with the development of the times, the existence of herbal plants as traditional medicines began to be forgotten so that not everyone could recognize them. Having the ability to identify herbal plants can have many positive impacts. However, there is a problem where identifying plants can take a long time because it requires in-depth knowledge and careful examination of plant criteria. So that the application of computer vision can help identify herbal plants. Previously, research had been conducted on the introduction of herbal plants from Vietnam using several algorithms, but from these research the accuracy was not high enough. Therefore, this study intends to implement transfer learning from the Convolutional Neural Network (CNN) algorithm to classify types of herbal plants from Indonesia. This research was conducted by collecting image data of herbal plants from Indonesia independently through the Google Images search engine. After that, it will go through the data preprocessing, classification using the transfer learning method from CNN, and analysis will be carried out. The CNN transfer learning models used are ResNet34, DenseNet121, and VGG11_bn. Based on the test results of the three models, 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 scratch model and obtained an accuracy of 43.53%. The Hyperparameter configuration used in this test is the ExponentialLR scheduler with a gamma value of 0.9; learning rate 0.001; Cross Entropy Loss function; Adam optimizer; and the number of epochs is 50. Indonesia Medicinal Plant Dataset can be accessed at the following link https://github.com/Salmanim20/indo_medicinal_plant

翻译：草本植物是具有营养价值的植物，可作为传统疾病治疗的替代方案。印度尼西亚拥有多种类型的草本植物。然而，随着时代发展，作为传统药物的草本植物逐渐被遗忘，导致并非所有人都能识别它们。具备识别草本植物的能力可带来诸多积极影响。但问题在于，植物识别需要深入的专业知识和细致的特征检查，往往耗时较长。因此，计算机视觉的应用有助于辅助草本植物的识别。此前，已有研究利用多种算法对越南草本植物进行识别，但准确率不够理想。为此，本研究旨在通过卷积神经网络（CNN）的迁移学习方法，对印度尼西亚的草本植物类型进行分类。研究中，我们通过谷歌图片搜索引擎独立采集了印度尼西亚草本植物的图像数据集。随后，数据经过预处理，并采用CNN的迁移学习方法进行分类和结果分析。所使用的CNN迁移学习模型包括ResNet34、DenseNet121和VGG11_bn。三个模型的测试结果表明，DenseNet121模型准确率最高，达到87.4%。此外，我们还使用从头训练模型进行了测试，其准确率为43.53%。本次测试中使用的超参数配置为：ExponentialLR调度器（gamma值为0.9）、学习率0.001、交叉熵损失函数、Adam优化器以及50个训练周期。印度尼西亚药用植物数据集可通过以下链接访问：https://github.com/Salmanim20/indo_medicinal_plant