Abstract— Rhizome is part of the plant that has many benefits. Some types of rhizomes that are often found are ginger, turmeric and galangal. But in reality, for the three types of rhizomes, there are still many that cannot be recognized. This is because some types of rhizomes do have properties and textures. This research proposes a rhizome recognition system with the classification of SVM (Support Vector Machine) and KNN (K-Neirest Neighbor). SVM searches for the best hyperplane by maximizing the distance between classes. KNN classifies objects based on the learning data that is the most distant from the object. The types of rhizomes used in this research data collection are the three types of rhizomes mentioned above. Meanwhile, the number of images in this study consisted of 150 training images and 30 testing images. The test is carried out by calculating the accuracy value of the classification of testing data in 3 classes, namely Ginger, Kuyit, and Galangal classes using both methods. The rhizome recognition system using the second method of classification is expected to help get good accuracy and can be more easily recognized by the name of the rhizome. Keywords— Rhizome; SVM;  KNN   Abstrak— Rimpang merupakan bagian dari tanaman yang memiliki banyak manfaat. Beberapa jenis rimpang yang sering dijumpai adalah jahe, kunyit dan lengkuas. Namun pada kenyataannya untuk ketiga jenis rimpang tersebut masih banyak yang tidak bisa dalam mengenalinya. Hal tersebut dikarenakan pada beberapa jenis rimpang memang memiliki kemiripan dalam bentuk dan teksturnya. Dalam penelitian ini diajukan sebuah sistem pengenalan rimpang dengan metode klasifikasi SVM (Support Vector Machine) dan KNN (K-Neirest Neighbor). SVM mencari hyperplane terbaik dengan memaksimalkan jarak antar kelas. KNN melakukan klasifikasi terhadap objek yang berdasarkan dari data pembelajaran yang jaraknya paling dekat dengan objek tersebut Jenis rimpang yang digunakan dalam dataset penelitian ini adalah ketiga jenis rimpang yang disebutkan di atas. Sedangkan untuk jumlah citra dalam penelitian ini terdiri dari 150 citra training dan 30 citra testing. Pengujiannya dilakukan dengan menghitung nilai akurasi dari klasifikasi data testing pada 3 kelas, yaitu kelas Jahe, Kuyit, dan Lengkuas dengan menggunakan kedua metode tersebut. Sistem pengenalan rimpang menggunakan kedua metode klasifikasi ini diharapkan mendapatkan akurasi yang baik dan dapat membantu masyarakat untuk lebih mudah mengenali nama rimpang. Keywords— Rimpang; SVM; KNN

Abstract—The development of Information and Communication Technology (ICT) has changed the new face of education. The implementation of Information and Communication Technology (ICT) in educational institutions has become a must in educational institutions, not to close our eyes that schools are naturally responsive to technological developments. The use of interactive multimedia as a support for the teaching and learning process is considered to be able to increase flexibility in teaching and learning activities, especially for Kindergarten (TK) students. Children at this age should be introduced to the benefits of technology. So that in the future it is not wrong to use technology. An interactive multimedia application that makes it easy for students to learn anywhere and anytime using a mobile device. Learning abilities are focused on making animal recognition applications for kindergarten (TK) students. Because knowledge related to animals must be introduced to students from an early age. Interactive multimedia applications are made by utilizing mobile android and Augmented Reality which are packaged in the form of games (games).