PENERAPAN METODE REGRESI LOGISTIK MULTINOMIAL DALAM MENGELOMPOKKAN JENIS KALENG BERDASARKAN CITRA RED GREEN BLUE (RGB)

ELVINA, SYINTIA and Resti, Yulia and Zayanti, Des Alwine (2021) PENERAPAN METODE REGRESI LOGISTIK MULTINOMIAL DALAM MENGELOMPOKKAN JENIS KALENG BERDASARKAN CITRA RED GREEN BLUE (RGB). Undergraduate thesis, Sriwijaya University.

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Abstract

Canned waste is difficult to remove, so that it has a negative impact on the environment. One way to reduce channed waste is by way of recycling. Recycling can be done by grouping cans into certain criteria. This research will group the types of cans based on the image of Red Green Blue (RGB). The data use consisted of 250 samples. The data is divided into training data and test data, the composition of the data used is 80% training data and 20% test data, In taking canned photos for test and training data, data is used in the form of lamp angle 45, Lamp light 1 and 2 speeds from the conveyer belt, with a speed of 1 is 0.181 m/s and a speed of 2 is 0.056 m/s. and the level of accuracy obtained from the color grouping of cans. This study uses the multinomial logistic regression method. The results showed that the highest level of accuracy was at speed 2 with an accuracy value of 80%. This show that the multinomial logistic regression method is quite good in classifying these types of cans.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Multinomial Logistic Regression, Red Green Blue Image, Grouping, Cans, Accuracy.
Subjects:	Q Science > QA Mathematics > QA1-43 General Q Science > QA Mathematics > QA801-939 Analytic mechanics > QA911.Z54 Fluid dynamics--Mathematical models. Multiphase flow--Mathematical models. Turbulence--Mathematical models. Combustion--Mathematical models. Combustion--Mathematical models. Fluid dynamics--Mathematical models. Multiphase flow
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Syintia Elvina
Date Deposited:	25 Jun 2021 03:59
Last Modified:	25 Jun 2021 03:59
URI:	http://repository.unsri.ac.id/id/eprint/48442

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