Klasifikasi Minyak Goreng Berdasarkan Frekuensi Penggorengan Menggunakan Metode K-Nearest Neighbor Berbasis Raspberry Pi - Linda Silvya Putri; Fitri Utaminingrum; Tibyani Tibyani (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Cooking oil is often used by the people as stapel for frying food ingredients. There are several types of oil, one of which is vegetable oil. Vegetable oil contains essential fatty acid which has function to prevent constriction of blood vessel that will effect accumulation of cholesterol. The cooking oil used repetitively can cause various diseases. The cooking oil used repetitively will make the double bonds of oxidized oil, and form peroxide groups and cyclic monomers, and will contain trans fatty acid. From these problems, it is necessary to have a system that can classify frequency of the use of cooking oil. In this study, the parameters studied in cooking oil are from color and turbidity. To determine classification of the frying frequency in cooking oil, for color detection of R (Red), G (Green), B (Blue) is obtained from the results of raspberry pi camera readings, and for turbidity is obtained from LDR (Light Emitting Diode) readings by Raspberry Pi 3 by using the KNN (K-Nearest Neighbor) method. From the results of study, it is known that the percentage of accuracy from R (Red), G (Green), B (Blue) readings on a raspberry pi camera with TCS3200 censor is R = 98.102%, G = 98.072%, B = 96.732%. In study of system using the KNN (K-Nearest Neighbor) method with 72 training data and 30 test data, is obtained an accuracy K=1, K=3, K=5 73.33% with an average time computing system of 3.9 ms.