Segmentasi Citra Makanan menggunakan Clustering Improved K-Means untuk Estimasi Sisa Makanan - Alip Setiawan; Yuita Arum Sari; Bayu Rahayudi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2021)
Every living thing needs food to live, grow and reproduce. Food is a basic need that is needed by humans as a source of energy to carry out activities in daily life. By eating nutritIoUs food can bring a person into a healthy diet. An unhealthy diet can cause a person to create diseases such as malnutrition or even death. It is important to regulate the amount of food consumption to maintain and maintain a healthy body, so we need a system that can estimate the food consumed. To estimate the weight of the food that has been consumed by someone, an image of the food is needed before and before consumption. In this study the data used is secondary data in the form of food images in the tray box which opens 31 images consisting of 124 compartment images. The improvement K-Means method was chosen for segmenting food images on the tray box. With the application of this method, it is expected to provide results with a small error rate and have good accuracy in the estimation of food waste in the tray box. the results based on the evaluation of the results of the highest average accuracy of segmentation results of 82% with the RMSE estimation of the smallest food waste weight achievement of 2.19. The test results show that the clustering method with the K-Means improvement algorithm can be used to estimate the weight of food images.