Implementation of Model-based Convolutional Neural Network for Ginger Candy Image Feasibility Classification on Android Device
Keywords:
Ginger Candy, CNN, MobileNetV2, CISP-DM, Prototyping, AndroidAbstract
Ginger is one of the primary ingredients for ginger candy. The manual process of evaluating the feasibility of ginger candy at the Tasacika Company is still prone to errors and is less efficient. This research aims to develop a Convolutional Neural Network model for classifying the feasibility of ginger candy and create an Android-based application that facilitates this process. The research method uses an Experimental approach. Model development is carried out with a Convolutional Neural Network with the MobileNetV2 architecture, using the Cross Industry Standard Process for Data Mining methods. Software development is done using the Prototyping method. This research used a dataset of images taken directly from the Tasacika Company's ginger candy factory. The model is trained and tested using Google Colab with the Python programming language and the TensorFlow and Keras libraries. Implementation is carried out using Kotlin and XML. It can be concluded that the research has succeeded in developing a ginger candy feasibility classification model. The test results show that the developed model is effective in minimizing human error in the process of checking the feasibility of ginger candy. This research also succeeded in developing an Android-based ginger candy feasibility classification application
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