<![CDATA[Palm cooking oil which belongs to the basic food category (SEMBAKO) is a food element made from triglycerides derived from palm oil. Yellow to orange is the normal color found in palm cooking oil. Using the same cooking oil continuously can reduce the quality of the cooking oil and can be dangerous for the health of consumers. Frequency classfication system is needed to find the accurate amount of used cooking oil. The parameters used in the classification process are color and turbidity which are tested using a TCS3200 sensor to process and measure color and an LDR sensor to process and measure the level of turbidity of cooking oil connected to Arduino Uno and use the Artificial Neural Network (ANN) classification method. The classification results are divided into 7 classes, namely pure oil, 1 time fish frying, 2 fish frying, 3 fish frying, 1 times tofu frying, 2 times tofu frying, and 3 times tofu frying. The classification results will be displayed on a 20x4 I2C lCD. Based on the test results, the accuracy of the color sensor is 98.827% and the LDR sensor can see the difference in the level of turbidity of a cooking oil so that the system can have an accuracy rate of 80% in computation time for 5,114 seconds after processing 70 training data and 20 test data.]]>
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