Analysis of Fruit Consumer Satisfaction in an Online Purchasing System Using an E-commerce Platform: A Naive Bayes Approach

Abstract

E-commerce platforms have now developed into fresh agricultural products, one of which is fruit. Purchasing online means customers cannot choose their own products like in offline stores. Therefore, the aim of this research is to identify the attributes that consumers often talk about and analyze consumer satisfaction with fruit on e-commerce platforms. Data analysis in this research was carried out using descriptive statistical methods and the Naïve Bayes approach. Data was taken from the Tokopedia platform with a total of 316 reviews using random sampling. The research results show that the attributes most frequently reviewed by consumers are service attributes (49%) and product attributes (44%), while the price attribute is reviewed the least (7%). Attributes related to product, price and service were assessed as having negative performance overall because nine of the twelve attributes analyzed indicated consumer dissatisfaction. Seller reviews are very important for business continuity so that sellers can improve their services and pay more attention to product quality so that the reviews appear positive. Research using the naive Bayes approach to determine customer satisfaction sentiment towards fruit products has not been widely carried out so the results of this research can complement previous studies.