Implementasi Text Mining Dalam Penentuan Kinerja Layanan Antara Grab dan Gojek Berdasarkan Opini Masyarakat Menggunakan LDA

Abstract

Public transportation is currently based on applications and the internet, so it is called online transportation. Grab and Gojek are two online transportation service providers who want to provide the best service. Users provide responses, experiences, criticism and suggestions via Twitter. This research will use the LDA algorithm to map topics that users frequently discuss regarding Gojek and Grab. Latent Dirichlet Allocation (LDA) is an unsupervised learning algorithm used to detect topics in a collection of text documents. LDA assumes that each document consists of a mixture of several topics, and that each topic consists of a probability distribution over words. LDA works by modeling the generative process of documents. This process begins by selecting a topic for the document, then selecting words from that topic. The probability of selecting certain topics and words is determined by parameters learned from the data. Data was obtained via Twitter with the keywords "#grabid" and "#gojekindonesia". From the research results, it was found that the best topic mapping results for Gojek objects were 2 topics, namely promotions and orders. Meanwhile, Grab objects are divided into 4 topics, namely communication, transactions, orders and orders