Skripsi
VALIDASI SILANG PENGELOMPOKKAN JENIS KALENG MENGGUNAKAN METODE DECISION TREE
Cans are storage places for food and non-food items made of aluminum. Canned waste is difficult to decompose on the ground so that a recycling process needs to be carried out so that it does not cause problems for the environment. The process of grouping the cans is the initial process in recycling. This study classifies cans based on RGB images using the Decision Tree. The decision tree explains the effect of attributes by depicting trees. The data consisted of 250 samples of cans given treatment factors in the form of one lighting angle, one type of light lamp, and two conveyor belt speeds. Delivery of treatments on the data is conveyor belt speed 1, lamp1, angle 30o, and conveyor belt speed 2, lamp 1, angle 30o.With cross validation 5 times get 1 fold as data testing and training 4 times data tested over and over. From the results of cross validation using the Decision Tree the greatest accuracy rate is 75.25%.
Inventory Code | Barcode | Call Number | Location | Status |
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2107003436 | T41141 | T411412021 | Central Library (Referens) | Available but not for loan - Not for Loan |
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