Implementation of Term Frequency-Inverse Document Frequency (TF-IDF) and K-Means Clustering for User Experience Research Startup

Abstract

A startup is an organization formed to look for repeatable and scalable business models. In recent years, startups have experienced significant growth. However, of the many startups in the world, most have failed. Factors related to the user is a factor that is very influential in startup failure. Therefore, a solution is needed to overcome problems related to these users. One solution is to do user experience research. In this study, the data used came from application reviews on the Google Playstore. To be able to process this data, the system implements the TF-IDF algorithm and K-Means Clustering. This research is expected to produce a system that functions to carry out user experience research automatically. So that it can be a solution to startup problems related to users and in the end can reduce the percentage of failed startups. From the Oy! app review data In Indonesia, there were 2,865 reviews that were implemented into the system using the K-Means Clustering algorithm, four topics that users often complain about, including the topic of credit exchange of 1,554 reviews, the redeem point feature of 172 reviews, the pulse redeem feature of 183 reviews, and the bank transfer feature of 541 reviews