Implementasi Metode K-Nearest Neighbor (K-NN) untuk Analisis Sentimen Kepuasan Pengguna Aplikasi Teknologi Finansial FLIP
DOI:
https://doi.org/10.29408/edumatic.v6i1.5433Keywords:
financial, KNN, sentiment, technology, TF-IDFAbstract
The phenomenon of technological development can transform systems in various sectors to provide efficiency and convenience at a lower cost, including the financial sector. Flip is a financial service application that makes it easy to transfer money between banks without administrative fees. By the end of 2021, the Flip will have a 4.9 rating on the Google Play Store. The purpose of this study was to analyze user sentiment towards the Flip app to see if flip user ratings were as positive as the ratings received. This study uses a set of text mining processes on the user rating data of the Flip app on the Google Play Store, using the classification algorithm K-Nearest Neighbor with TF-IDF weighting. The results show that 77.67% of the test data are correctly classified as positive evaluation classes, with high accuracy and recall rates of 82.67% and 86.92%, respectively. In addition, from the results of applying the Flip user rating data classification method, the comparison between training data and test data is 80%:20%, and the classification accuracy using the K-Nearest Neighbor algorithm is 76.68%. User reviews of the Flip app have shown positive results, as well as the ratings obtained in the Google Play Store and the K-Nearest Neighbor algorithm, TF-IDF weighting process used to analyze user sentiment towards the Flip app.
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