Estimasi Sisa Makanan Otomatis pada Kotak Makan menggunakan Segmentasi Citra berbasis Clustering dengan Algoritme K-Means - Haris Bahtiar Asidik; Yuita Arum Sari; Agus Wahyu Widodo (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Food is a source of energy for living things, consuming food in standardized portions can help meet nutritional needs, but on the other hand, it will have a negative impact on the body. Foodservice in the hospital is a support system in accelerating the patient's healing process, the patient's leftover food reflects the patient's low acceptance of food which can increase the risk of malnutrition. Currently, the global volume of food waste is estimated at 1.6 billion tonnes with food waste carbon estimated at 3.3 billion tonnes of CO2 equivalent to greenhouse gases per year. It takes a tool to find out how much food a person has consumed. With the development of technology, the process of calculating the weight of food waste can be done through the image of the food before it is eaten and the image of the food after it is eaten. The food image in the lunch box is segmented to obtain food segments in the image through the clustering method using the K-means algorithm based on the blue color level in the image. The results of the Intersection over Union (IoU) segmentation accuracy on images with a white background reached 98.9%. Based on the segmentation results obtained, the weight of leftover food was predicted using the Elementary Leftover Food Estimation (EFLE) method. By using the Root Mean Square Error (RMSE), the experimental results reach the smallest error of 1.12. This indicates that the proposed method is capable to project the weight of the food residue.