Sentiment Analysis of YouTube Social Media On Lumpur Lapindo Tourism Using the K-Nearest Neighbor (K-NN) Algorithm and Term Frequency – Inverse Document Frequency (TF-IDF)
Analisis Sentimen pada Media Sosial Youtube Terhadap Pariwisata Lumpur Lapindo Dengan Menggunakan Algoritma K-Nearest Neighbor (K-NN) dan Term Frequency – Invers Document Frequency
DOI:
https://doi.org/10.21070/ups.8423Keywords:
Youtube, Classification, K-Nearest Neighbor (K-NN), Term Frequenncy-Invers Document Frequency (TF-IDF), Sentiment analysisAbstract
Digital transformation in the current industrial era has had a significant impact on various aspects of human life, including communication patterns and social interactions. One rapidly growing digital platform is YouTube, which serves as a medium for sharing information in asynchronous video formats. This platform provides a space for users to interact through the comment feature, allowing the public to express opinions, criticisms, and suggestions regarding the content presented. To assess public perception of tourism in the Lapindo Mud area, sentiment analysis was conducted on YouTube user comments. This study utilizes the K-Nearest Neighbor (K-NN) algorithm for opinion classification. The research stages include data collection, text preprocessing to obtain clean and structured data, and word weighting using the Term Frequency–Inverse Document Frequency (TF-IDF) method. All these processes aim to produce accurate sentiment classification. The data used in this study is primary data obtained directly from the YouTube platform.
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