  This research aims to detect the physical characteristics of corn kernels and
analyze images using a deep learning model. The data analysis based on the
CRISP-DM framework which consists of six steps, business understanding, data
understanding, data preparation, modelling, evaluation, and deployment. The
business goal reduces the cost of the separation of abnormal corn kernels. The
dataset comprises 1,800 images of corn kernels and divided equally between
normal and abnormal corn kernels. The dataset was divided into three subsets:
1,000 images for training the deep learning model, 600 images for validation
and 200 images for testing. The tools for analysis in this research are Jupyter
Lab, Python, TensorFlow Keras, and Convolutional Neural Networks. The results
revealed that the deep learning model achieved the accuracy rate of 99% in
differentiating between normal and abnormal corn kernel images that is a highly
effective model in this context.
