Along with the increasing number of bulk cargoes that are dismantled every year at belawan port and for the creation of services in accordance with expectations, it is necessary to develop services in support of indonesia's logistics improvement readiness, especially in terms of demolition. Utilization of machine learning using the C4.5 algorithm can make it easier to conduct selection and classification of the feasibility of ships that get permission for demolition activities. The use of the C4.5 algorithm will produce a decision tree that can equalize the results of data mining, so that the information obtained from the data will be easier to identify in testing methods using the Orange Data Mining tool. The results obtained by the C4.5 algorithm in the form of a decision tree with an accuracy value of 84%, 90% precision and 84% recall.

In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933.