Analisis Sentimen Pengguna Twitter Terhadap Pemindahan Ibu Kota Negara Indonesia Menggunakan Algoritma Naive Bayes

Nurreza, Feggy (2023) Analisis Sentimen Pengguna Twitter Terhadap Pemindahan Ibu Kota Negara Indonesia Menggunakan Algoritma Naive Bayes. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

The relocation of IKNs in Indonesia has drawn various pro and con responses on social media, with environmental issues and the COVID-19 pandemic situation being the main concerns. Sentiment analysis is important to understand the views and opinions of the public regarding this issue. Twitter was chosen as the study platform because of its popularity as a popular means of communication among Indonesian people. This study adopts the approach that has been used in previous research and applies the Naive Bayes classification algorithm to classify sentiments in tweets related to the transfer of IKN. The sentiments analyzed are divided into three categories: pro, neutral, and con. The stages of data processing consist of data collection, data labeling, text preprocessing, data training and testing. This research resulted in the pro having a precision of 69%, a recall of 92%, and an f1-score of 79%. Models provide accurate and comprehensive results for pros. Neutral has a precision of 100%, but a recall of 72%, which indicates that the model has difficulty identifying accurately. Contra has a precision of 83% and a recall of 78%, which indicates the model has good performance. In terms of overall accuracy, the model has an accuracy of 81% which is the ratio of correct predictions to the total sample. Keywords: Relocation IKN, Sentiment Analysis, Twitter, Naive Bayes

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