IMAGE CLASSIFICATION RECOGNITION OF GAMELAN MUSICAL INSTRUMENT TYPES USING CNN METHOD ANDROID BASED

Abstract

In the ever-evolving digital age, the use of digital images has become a significant and widespread phenomenon in various fields. Digital image processing and understanding has become an important requirement in various applications, including pattern recognition and computer vision. On the other hand, the sustainability and understanding of cultural treasures, such as Gamelan, is becoming increasingly crucial. UNESCO has recognized Gamelan as Indonesia's 12th World Intangible Cultural Heritage, reminding us of the responsibility to maintain and preserve this cultural heritage. In the digital era, where interest in traditional musical instruments is declining, Convolutional Neural Network (CNN) is implemented as a solution to classify Gamelan musical instrument types based on visual patterns in images. CNN, implemented in an Android system, showed good results with accuracy reaching 98% in the model test stage and 79% in the Android application test. The classification model using TensorFlow Lite, specifically MobilNetV2, was able to recognize Gamelan musical instrument types in the training dataset. However, it should be noted that this model is limited to that dataset. This research contributes to the merging of technology and cultural heritage, enabling the use of technology to enhance cultural understanding and sustainability.