PENENTUAN HARGA PRODUK FUNCTIONAL DAN NON-FUNCTIONAL - , ASTI NOVIA WULANDARI, Ir. Subagyo, Ph.D. ([Yogyakarta] : Universitas Gadjah Mada, 2014)
Product price is one of the key to corporate profits, moreover product price is also a determinant of products success in the market. So, a proper product pricing is an important decision for corporates. There are many pricing strategies, besides using cost plus strategy, there are other factors that can affect product price, such as intangible factors. Therefore, pricing is a difficult thing for corporate to do. This study aims to build a product pricing model that can be used for both functional and non-functional product. This study used 171 products from 12 kind of products that its characteristics will be identified to create a pricing model. Before the model was built, first the ratio of intangible value of a product must be calculated. Ratio of intangible value is the ratio between the intangible value of product (the difference between a decent price and the actual price of the product) with its production cost. Then, the ratio of intangible of the product are devided into several groups according to certain intangible ratio ranges within the same length of each interval. The characteristics of each group will be identified based on certain criterions. If these criterions are not able to distinguish the characteristics among groups, then additional criterions are needed, but the additional criterion must have a correlation with the groups. The criterions used in this study are usability level, types, needs levels, art-level, production cost and brand of products. The model will be built based on the characteristics of existing similarities in each product group. In this study, three decision tree models were built for product pricing based on product characteristics. Then, the best three models were selected based on a low error rate, the highest coefficient of determination and the best predictive ability. The best model generated in this study contain 13,35% of error, determination coefficient of 91,5% and predictive ability of 87,29%. Keywords: Product Price, Product Characteristics, Intangible Factors