Analisis Sentimen Ulasan Game Simulator Indonesia di Google Play Store Menggunakan Algoritma Naive Bayes

sitti Masyitah Meliyana R; Sudarmin; Yuni Sabrina Effendy

doi:10.35580/variansiunm336

sitti Masyitah Meliyana R Department of Statistics, Universitas Negeri Makassar
Sudarmin Department of Statistics, Universitas Negeri Makassar
Yuni Sabrina Effendy Universitas Negeri Makassar

DOI: https://doi.org/10.35580/variansiunm336

Keywords: Keywords: Simulator Game, Sentiment analysis, Naive Bayes Classifier.

Abstract

Sentiment analysis is the process of text data to understand the opinion contained in a sentence. The commonly used algorithm in this analysis is the Naïve Bayes Classifier. Naive Bayes Classifier (NBC) is a classification that uses statistical and probabilistic methods to group texts into several categories of sentiments such as positive and negative. The Indonesian simulator game analyzed is Angkot d Game. This algorithm is used to understand users' perceptions of the game and to identify the factors that affect user sentiment. The results show that the Naive Bayes Classifier has a high level of accuracy in classifying the sentiments of simulator game reviews. The findings of this analysis are also expected to provide insights to game developers about user preferences and complaints, allowing them to adjust features or aspects of the game to better meet user needs. This enables game developers to make significant changes to the games they develop, potentially increasing revenue in the Indonesian gaming industry and focusing more on games created by local developers.

Keywords: Simulator Game, Sentiment analysis, Naive Bayes Classifier.

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