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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| Format: | Thesis |
| Language: | English English English |
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2018
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| Online Access: | http://eprints.uthm.edu.my/324/ 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_ | 1848887159953555456 |
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| author | Ramly, Nurfarawahida |
| author_facet | Ramly, Nurfarawahida |
| author_sort | Ramly, Nurfarawahida |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:49:58Z |
| format | Thesis |
| id | uthm-324 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T19:49:58Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-3242021-07-21T04:49:30Z http://eprints.uthm.edu.my/324/ Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income 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 NonPeerReviewed text en http://eprints.uthm.edu.my/324/1/24p%20NURFARAWAHIDA%20RAMLY.pdf text en http://eprints.uthm.edu.my/324/2/NURFARAWAHIDA%20RAMLY%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/324/3/NURFARAWAHIDA%20RAMLY%20WATERMARK.pdf Ramly, Nurfarawahida (2018) Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income. Masters thesis, Universiti Tun Hussein Onn Malaysia. |
| spellingShingle | QA150-272.5 Algebra Ramly, Nurfarawahida Improvisation of fuzzy c-means method and fuzzy linear regression model in predicting manufacturing income |
| title | 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_short | 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 |
| topic | QA150-272.5 Algebra |
| url | http://eprints.uthm.edu.my/324/ 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 |