A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income
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 m...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Published: |
Science Publishing Corporation
2018
|
| Subjects: |
| _version_ | 1825709968357064704 |
|---|---|
| author | Ramly, Nurfarawahida Rusiman, Mohd Saifullah Che Him, Norziha Nor, Maria Elena Suparman, S. Ahmad Basri, Nur Ain Zafirah Mohamad, Nazeera |
| author_facet | Ramly, Nurfarawahida Rusiman, Mohd Saifullah Che Him, Norziha Nor, Maria Elena Suparman, S. Ahmad Basri, Nur Ain Zafirah Mohamad, Nazeera |
| author_sort | Ramly, Nurfarawahida |
| collection | UTHM |
| description | 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 |
| first_indexed | 2024-03-05T21:50:28Z |
| format | Article |
| id | uthm.eprints-5122 |
| institution | Universiti Tun Hussein Onn Malaysia |
| last_indexed | 2024-03-05T21:50:28Z |
| publishDate | 2018 |
| publisher | Science Publishing Corporation |
| record_format | dspace |
| spelling | uthm.eprints-51222022-01-06T01:47:09Z http://eprints.uthm.edu.my/5122/ A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income Ramly, Nurfarawahida Rusiman, Mohd Saifullah Che Him, Norziha Nor, Maria Elena Suparman, S. Ahmad Basri, Nur Ain Zafirah Mohamad, Nazeera TA Engineering (General). Civil engineering (General) TA329-348 Engineering mathematics. Engineering analysis 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 Science Publishing Corporation 2018 Article PeerReviewed 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 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TA329-348 Engineering mathematics. Engineering analysis Ramly, Nurfarawahida Rusiman, Mohd Saifullah Che Him, Norziha Nor, Maria Elena Suparman, S. Ahmad Basri, Nur Ain Zafirah Mohamad, Nazeera A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title | A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title_full | A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title_fullStr | A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title_full_unstemmed | A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title_short | A new hybrid of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title_sort | new hybrid of fuzzy c means method and fuzzy linear regression model in predicting manufacturing income |
| topic | TA Engineering (General). Civil engineering (General) TA329-348 Engineering mathematics. Engineering analysis |
| work_keys_str_mv | AT ramlynurfarawahida anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT rusimanmohdsaifullah anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT chehimnorziha anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT normariaelena anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT suparmans anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT ahmadbasrinurainzafirah anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT mohamadnazeera anewhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT ramlynurfarawahida newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT rusimanmohdsaifullah newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT chehimnorziha newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT normariaelena newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT suparmans newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT ahmadbasrinurainzafirah newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome AT mohamadnazeera newhybridoffuzzycmeansmethodandfuzzylinearregressionmodelinpredictingmanufacturingincome |