Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income

Certain statistical systems for modelling are influenced by human perception. Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with. Thus, fuzzy structure system is considered. The objectives of this study were to: determine...

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Main Author:	Ramly, Nurfarawahida
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	QA150-272.5 Algebra
Online Access:	http://eprints.uthm.edu.my/324/1/24p%20NURFARAWAHIDA%20RAMLY.pdf http://eprints.uthm.edu.my/324/2/NURFARAWAHIDA%20RAMLY%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/324/3/NURFARAWAHIDA%20RAMLY%20WATERMARK.pdf
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spelling	my-uthm-ep.3242021-07-21T04:49:30Z Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income 2018-01 Ramly, Nurfarawahida QA150-272.5 Algebra Certain statistical systems for modelling are influenced by human perception. Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with. Thus, fuzzy structure system is considered. The objectives of this study were to: determine suitable cluster for predicting manufacturing income by using fuzzy c-means (FCM) method, apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni in predicting manufacturing income, improvise of FCM method and FLR model proposed by Zolfaghari in predicting manufacturing income and measure the performance of MLR model, FLR model and improvisation of FCM method and FLR model by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. Results showed that the improvisation of FCM method and FLR model obtained the lowest value of error measurement as compared to other models with cluster 1 recorded H=0.025 with MSE=1.824 11 10  , MAE=114508.0207 and MAPE=95.8043. Meanwhile, cluster 2 recorded H=0.05 with MSE=1.900 11 10  , MAE=254814.5620 and MAPE=20.1972. Therefore, it is concluded that the improvisation of FCM method and FLR model is the best model for predicting manufacturing income compared to the other models. 2018-01 Thesis http://eprints.uthm.edu.my/324/ http://eprints.uthm.edu.my/324/1/24p%20NURFARAWAHIDA%20RAMLY.pdf text en public http://eprints.uthm.edu.my/324/2/NURFARAWAHIDA%20RAMLY%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/324/3/NURFARAWAHIDA%20RAMLY%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA150-272.5 Algebra
spellingShingle	QA150-272.5 Algebra Ramly, Nurfarawahida Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
description	Certain statistical systems for modelling are influenced by human perception. Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with. Thus, fuzzy structure system is considered. The objectives of this study were to: determine suitable cluster for predicting manufacturing income by using fuzzy c-means (FCM) method, apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni in predicting manufacturing income, improvise of FCM method and FLR model proposed by Zolfaghari in predicting manufacturing income and measure the performance of MLR model, FLR model and improvisation of FCM method and FLR model by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. Results showed that the improvisation of FCM method and FLR model obtained the lowest value of error measurement as compared to other models with cluster 1 recorded H=0.025 with MSE=1.824 11 10  , MAE=114508.0207 and MAPE=95.8043. Meanwhile, cluster 2 recorded H=0.05 with MSE=1.900 11 10  , MAE=254814.5620 and MAPE=20.1972. Therefore, it is concluded that the improvisation of FCM method and FLR model is the best model for predicting manufacturing income compared to the other models.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ramly, Nurfarawahida
author_facet	Ramly, Nurfarawahida
author_sort	Ramly, Nurfarawahida
title	Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
title_short	Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
title_full	Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
title_fullStr	Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
title_full_unstemmed	Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
title_sort	improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Applied Sciences and Technology
publishDate	2018
url	http://eprints.uthm.edu.my/324/1/24p%20NURFARAWAHIDA%20RAMLY.pdf http://eprints.uthm.edu.my/324/2/NURFARAWAHIDA%20RAMLY%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/324/3/NURFARAWAHIDA%20RAMLY%20WATERMARK.pdf
_version_	1747830582550724608

Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income

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