Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk
The risk of product loss can be minimized by mathematical model of predictive success or failure of a product at the early design stage. Model is build from 30 graphics of strategy canvas industries.Canvas strategy contains success factors product overview. This research starts with standardise canv...
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| Format: | Article |
| Language: | Indonesian |
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Universitas Jenderal Soedirman
2013-06-01
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| Series: | Dinamika Rekayasa |
| Subjects: | |
| Online Access: | http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/99 |
| _version_ | 1818133515213996032 |
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| author | Niko Siameva Uletika |
| author_facet | Niko Siameva Uletika |
| author_sort | Niko Siameva Uletika |
| collection | DOAJ |
| description | The risk of product loss can be minimized by mathematical model of predictive success or failure of a product at the early design stage. Model is build from 30 graphics of strategy canvas industries.Canvas strategy contains success factors product overview. This research starts with standardise canvas intervaland factor successdescription. Next step is factors succesclasification, based on functionalemotional product orientation. The result of it are 66 data sets. Data set are constructed based on value innovation concept. Every data set consist ofone price factor, one innovation factor and one factor of succes indicator. The Mathematical model from desimal data obtained by Ordinary LeastSquare (OLS) estimation parameter method. Binary data obtained by Maximum Likelihood Estimator (MLE). Mathematical model selection base onmodel and coeficient significant (α=0.05). While model significances decimal data are then validated by One Way Analysis of Variance (ANOVA), binary data validated by Hosmer and Lemeshow analysis to testgoodness of fit of the model. Coefficient of significances are tested with t and wald statistic. Finally, mathematical model required is derived from prediction capability relied on R squareAdjusted for decimal data and R square Nagelkerke analysis for binary data. The result of this research is model with prediction capability up to 70%. Thereare three models developed, new emotional model with 74.1% predictioncapability, functional velocity model (73.1%), and functional capability (70.8%). |
| first_indexed | 2024-12-11T08:53:57Z |
| format | Article |
| id | doaj.art-7cc7f4ffc1244ec4ab42b57946153ebb |
| institution | Directory Open Access Journal |
| issn | 1858-3075 2527-6131 |
| language | Indonesian |
| last_indexed | 2024-12-11T08:53:57Z |
| publishDate | 2013-06-01 |
| publisher | Universitas Jenderal Soedirman |
| record_format | Article |
| series | Dinamika Rekayasa |
| spelling | doaj.art-7cc7f4ffc1244ec4ab42b57946153ebb2022-12-22T01:13:57ZindUniversitas Jenderal SoedirmanDinamika Rekayasa1858-30752527-61312013-06-01912933101Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional ProdukNiko Siameva Uletika0Prodi Teknik Industri Universitas Jenderal SoedirmanThe risk of product loss can be minimized by mathematical model of predictive success or failure of a product at the early design stage. Model is build from 30 graphics of strategy canvas industries.Canvas strategy contains success factors product overview. This research starts with standardise canvas intervaland factor successdescription. Next step is factors succesclasification, based on functionalemotional product orientation. The result of it are 66 data sets. Data set are constructed based on value innovation concept. Every data set consist ofone price factor, one innovation factor and one factor of succes indicator. The Mathematical model from desimal data obtained by Ordinary LeastSquare (OLS) estimation parameter method. Binary data obtained by Maximum Likelihood Estimator (MLE). Mathematical model selection base onmodel and coeficient significant (α=0.05). While model significances decimal data are then validated by One Way Analysis of Variance (ANOVA), binary data validated by Hosmer and Lemeshow analysis to testgoodness of fit of the model. Coefficient of significances are tested with t and wald statistic. Finally, mathematical model required is derived from prediction capability relied on R squareAdjusted for decimal data and R square Nagelkerke analysis for binary data. The result of this research is model with prediction capability up to 70%. Thereare three models developed, new emotional model with 74.1% predictioncapability, functional velocity model (73.1%), and functional capability (70.8%).http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/99mathematic model, prediction, success factor, product design |
| spellingShingle | Niko Siameva Uletika Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk Dinamika Rekayasa mathematic model, prediction, success factor, product design |
| title | Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk |
| title_full | Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk |
| title_fullStr | Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk |
| title_full_unstemmed | Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk |
| title_short | Model Matematis Prediksi Produk Sukses Berdasarkan Orientasi Fungsional Emosional Produk |
| title_sort | model matematis prediksi produk sukses berdasarkan orientasi fungsional emosional produk |
| topic | mathematic model, prediction, success factor, product design |
| url | http://dinarek.unsoed.ac.id/jurnal/index.php/dinarek/article/view/99 |
| work_keys_str_mv | AT nikosiamevauletika modelmatematisprediksiproduksuksesberdasarkanorientasifungsionalemosionalproduk |