Klasifikasi Umur Padi berdasarkan Data Sensor Warna dengan menggunakan Metode K-NN - Anggi Diatma Styandi; Dahnial Syauqy; Wijaya Kurniawan (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Indonesian country is one of the rice producers. Most of Indonesian people work in agriculture. However, it is unfortunately that Indonesia cannot meet the national rice needs so that supposes Indonesia to import rice from other countries. The cultivating time of farmers in Indonesia is still one of the causes lack of rice quality and quantity that is less than optimal, so that the quality of rice products is still said to be deficient. Rice that is cropped too late has very bad impacts. Therefore, farmers should be smart in choosing the right time to crop rice. Weather factors and large fields make it increasingly difficult for farmers to check the age of their whole rice regularly. Based on these problems, farmers now need a system to help observing the age of rice by seeing on changes in the color of rice plant so that the study entitled "Rice Plant Age Classification Based on TCS3200 Color Sensor Data Using the Knn Method" is proposed. This research utilizes TCS320 Integrated Circuit (IC) Color Sensor, Arduino Integrated Development Enviroenment (IDE) software and LED as an indicator to be arranged into a system. I hope the system that I create will help farmers to improve the quality and quantity of rice yields. So that the Indonesian government does not need to import rice from other countries.After testing several times of test, it is known that this system can detect colors precisely in 20 times the experiment by attaching objects. From the results of the KNN test the highest accuracy was found at K = 5, where the accuracy value obtained was 80%. While the lowest accuracy is at k = 9, where the accuracy value obtained is only 10%