Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor

Dzul Asfi Warraihan, Inggih Permana, Mustakim Mustakim, Rice Novita

Abstract


Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.

Keywords


Sentiment Analysis; Instagram; K-Nearest Neighbor; Maxim; Naive Bayes Classifier

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References


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DOI: https://doi.org/10.30865/mib.v7i3.6336

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