Consumer Preference Analysis of Snack Using Conjoint Analysis Method (Case Study : Telur Gabus)

Abstract

Recently, the production of snacks continues to experience developments ranging from types, variants of taste, basic ingredients, and appearance. The aim of this research is to develop snack products in an effort to increase market share of the product itself. The example of the product for this research commonly known as Telur Gabus. Conjoint analysis and cluster analysis methods are used in processing the data obtained. Through the distribution of questionnaires, was obtained attributes that are considered in the development of this product that consist of texture, variant of taste, how to open the package, the weigh of contents, how to carry the packaging, and packaging design. The number of combination profiles is 16 combination profile level attributes and analyzed so that the combination of attributes with the highest utility value is chosen. The combination of attributes with the highest utility value are products that have a solid texture, have a salty taste, use zipper packaging, contents inside the packaging is 100-150 gr, carried by hand and have a packaging product design with a sticker covering half the packaging.

The development of vegetarian meatballs with oyster mushroom and tempeh flour using conjoint analysis and organoleptic test

“…The assessment results are valid with Pearson's R validity test because they used the rating assessment. The value of α=5% was used as a parameter, obtained R (0.989) > 0.707 and significance (0.000) < 0.05 means the data in this study can be trusted to analyze [14]. Furthermore, the conjoint survey was reliable due to Cronbach's Alpha (0.88) being more than the minimum value (0.7).…”

“…Attribute levels refer to variations in attributes [15]. Determination of attributes based on answers related to factors determining respondents' buying meatball products [14]. Respondents' answers were grouped, and there are four selected factors with a frequency of answers that appears more than 15% on these questions, such as dominant taste (31.17%), price (16.88%), quality (16.88%), and texture (15.58%).…”

“…Attributes refer to product characteristics. If attributes meet the consumer's needs or desires, the demand for the product will increase [14]. Attribute levels refer to variations in attributes [15].…”

“…Table 3 showed 16 stimuli rated by respondents with a Likert scale of 1 "dislike very much" to 5 "like very much" [19]. Respondents' assessment results were processed using IBM SPSS program-syntax menu to obtained attribute importance values, utility values per attribute level, and validity test results with Pearson's R correlation coefficient to determine the actual preference relationship with the model and valid if sig < 0.05 and R > 0.707 [14]. The results also analyzed for reliability with Cronbach's Alpha to determine the consistency of repeated measurements and reliable if the value is more than 0.7.…”