Ramly, Nurfarawahida and Rusiman, Mohd Saifullah and Che Him, Norziha and Nor, Maria Elena and Suparman, S. and Ahmad Basri, Nur Ain Zafirah and Mohamad, Nazeera (2018) A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income. International Journal of Engineering & Technology, 7 (4.3). pp. 473-478. ISSN 2227-524X
Full text not available from this repository. (Request a copy)Abstract
Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. 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. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 11 10 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Fuzzy linear regression (FLR); fuzzy c-means (FCM); mean square error (MSE) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TA Engineering (General). Civil engineering (General) > TA329-348 Engineering mathematics. Engineering analysis |
| Divisions: | Faculty of Applied Science and Technology > Department of Mathematics and Statistics |
| Depositing User: | UiTM Student Praktikal |
| Date Deposited: | 06 Jan 2022 01:47 |
| Last Modified: | 06 Jan 2022 01:47 |
| URI: | http://eprints.uthm.edu.my/id/eprint/5122 |
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