Rancang Bangun Sistem Klasifikasi Tingkat Kematangan Pisang berdasarkan Warna Kulit dan Berat menggunakan Metode K-Nearest Neighbor berbasis Arduino - Pramandha Saputra; Dahnial Syauqy; Hurriyatul Fitriyah (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2021)
Currently, bananas are one of the favorite fruits that have good nutrition and taste that is liked by most people, because bananas have good nutritional content for the body. Banana is a fruit that is very beneficial for human life, which can be consumed at any time and at all ages. Constraints on the community in distinguishing ripe bananas that are suitable for management are sometimes experienced by ordinary people who do not know about the characteristics of ripe bananas and are good to manage. To make processed products, bananas with the right maturity are needed, for that a study was made on the ripeness of bananas based on the weight and color of banana peels that classify them using the K-Nearest Neighbor method. In this system there are several components, namely: Arduino Mega 2560 microcontroller to process k-nearest neighbor calculation data, TCS3200 sensor whichs is use to detect skin color on bananas, loadcell sensor as a weight gauge on bananas. The system in distinguishing the ripeness of bananas using the k-nearest neighbor method got an accuracy of 86.6%. Perform testing on the value of K = 3 and then the results of the changed K value are compared to see a more accurate K value in the k-nearest neighbour method applied to the system.