Implementasi Background Subtraction Untuk Klasifikasi Keripik Kentang Berbasis Raspberry Pi Menggunakan Metode Naive Bayes - Yongki Pratama; Fitri Utaminingrum; Wijaya Kurniawan (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Potatoes are a kind of vegetables that can be processed into various foods, one of which is the chips. Many big companies in Indonesia produce potato chips. One of them is Istana Factory located in Batu Tourism City. The potatoes processed have various sizes from the largest size (Super), medium size (AB), and then the smallest size (A). The process of sorting at the factory has been done by human manually, then it will produce less relative output. Therefore, it is needed a research about a tool that can sort the potato chips automatically. In this study, the system made is in the form of conveyor, which a webcam is installed as a censor to take pictures from potato chips, then those are processed in Raspberry Pi using image processing of Background Subtraction method. Potato chips will be classified based on size read by the system by using the w and h value parameters or the width and height of the bounding box of potato chips that are converted to actual size by millimeters. The value is used as a reference to be classified with the Naive Bayes method. Naive Bayes is used for the classification method because it is a method that has high performance and has excellent accuracy for classification. From the results of test conducted Background Subtraction can read the image of potatoes well. The reading of potato chip size from the system gets a small error of 3.73%. Then the accuracy obtained with Naive Bayes method in chips classification with 90 training data and 30 test data is worth 93.33 having an average processing time speed of 1.7 ms from 30 times of the test. Then it is performed a test of hardware servo that has been running based on the system.