JARINGAN SYARAF TIRUAN BACKPROPAGATION UNTUK MEMPREDIKSI LAYANAN PERAWATAN DI MAIRA SALON DAN SPA

LUTFIA LILIN KHARIROH, NIM. 12650039 (2016) JARINGAN SYARAF TIRUAN BACKPROPAGATION UNTUK MEMPREDIKSI LAYANAN PERAWATAN DI MAIRA SALON DAN SPA. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Data and information on care services is very important for the beauty salon. The data is used for planning the future care services, for example: the amount of drug which will be used for treatment, the number of reliable therapist to perform care services, and many other case. Prediction of hair care service aims to find out how much in percentage the hair care services taken by visitors in a week. The data used in this study is the data of hair care services (cream bath, hair mask, hair spa and hair rejuvenation) by summing all data services. At early stage, researcher normalizes data using minmax algorithm, and determine the most optimal backpropagation network is by varying the activation function in the hidden layer to obtain the greatest percentage of success. After obtaining the most optimal network conducted training on preliminary data. Then researcher performs test on the new data with the most optimal network. Overall the data used for training amounted to 110. The Data we use is 77 data or 70% of the overall data. Testing data is 33 data represent 30% of overall data. Networks used in prediction using tansig-purelin activation function, with a hidden layer node layer 5, the epoch of 1000, learning rate 0.9 and momentum 0.7. The result is prediction appropriate 66.66% with tolerance value 3 and for unappropriated data presentation is 33.33%.

Item Type: Thesis (Skripsi)
Additional Information: Nurochman, M.Kom
Uncontrolled Keywords: Backpropagation, Prediction, Salon and Spa, Treatment
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Miftahul Ulum [IT Staff]
Date Deposited: 06 Oct 2016 07:59
Last Modified: 06 Oct 2016 07:59
URI: http://digilib.uin-suka.ac.id/id/eprint/22237

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