Penentuan Paket Bundling dan Promo Menggunakan FP-Growth Berdasarkan Behavior Costumer

Alyssa, Diva Risana Fauziyah (2024) Penentuan Paket Bundling dan Promo Menggunakan FP-Growth Berdasarkan Behavior Costumer. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

The increasing use of online health applications has driven the need for more careful assessment in application selection. User reviews are the main aspect in assessing the quality of the application, because sometimes the ratings do not always reflect the reviews given. In this research, sentiment analysis functions to process user review data. The Riliv application, an online counseling platform that has received many prestigious awards focusing on the field of mental health, is the object of research. This research aims to understand the discrepancy between text reviews and numerical ratings, so as to provide more valid and useful insights into user experience and satisfaction. The Multinomial Naive Bayes method is used in the analysis because it has good performance in classification. From 2458 data taken from the Google Play Store and App Store, the results of classification testing using Multinomial Naive Bayes gave an accuracy of 87%, with the Good Classification category. Keywords: Sentiment Analysis, Multi-Class, Naive Bayes, Text Mining, Riliv

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