KLASIFIKASI PELAYANAN MASKAPAI PENERBANGAN BERDASARKAN KOMENTAR TWITTER SEBAGAI OPINI PUBLIK MENGGUNAKAN METODE CLUSTERING K-MEANS
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
Bayu Suciandi, Rama
Subject
Q Science (General)
Datestamp
2024-02-27 03:20:04
Abstract :
Aviation service is one of the most popular means of transportation in Indonesia today. In recent years, there has been an increasing number of domestic and international companies operating and providing transportation services with various facilities and costs. Social media Twitter is a place for passengers' opinions. This is used as research for passenger comments on social media Twitter. This study aims to create a sentiment analysis model using the K-Means clustering method for positive or negative comments from Indonesian garuda passengers. This study analyzes the penutan comments from Twitter using the K-Means clustering method with a value of k = 2, data retrieval using the netlytic website taken from December 30 2022 ? February 25 2023 yielded 3,779 tweet data. The data taken is data related to services. The training data is 413 data, with details of 213 data labeled positive and 200 data labeled negative. As for testing data, 142 tweets were used with details of 64 positive data and 78 negative data with an accuracy value of 95.07%.
Aviation service is one of the most popular means of transportation in Indonesia today. In recent years, there has been an increasing number of domestic and international companies operating and providing transportation services with various facilities and costs. Social media Twitter is a place for passengers' opinions. This is used as research for passenger comments on social media Twitter. This study aims to create a sentiment analysis model using the K-Means clustering method for positive or negative comments from Indonesian garuda passengers. This study analyzes the penutan comments from Twitter using the K-Means clustering method with a value of k = 2, data retrieval using the netlytic website taken from December 30 2022 ? February 25 2023 yielded 3,779 tweet data. The data taken is data related to services. The training data is 413 data, with details of 213 data labeled positive and 200 data labeled negative. As for testing data, 142 tweets were used with details of 64 positive data and 78 negative data with an accuracy value of 95.07%.
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