Implementasi Metode Naive Bayes Classifier Terhadap Klasifikasi Topik Kemacetan Lalu Lintas Indonesia Melalui Tweet

Authors

  • Romindo Romindo Universitas Pelita Harapan, Jakarta
  • Okky Putra Barus Universitas Pelita Harapan, Jakarta
  • Jefri Junifer Pangaribuan Universitas Pelita Harapan, Jakarta

DOI:

https://doi.org/10.30865/mib.v8i2.7470

Keywords:

Traffic Congestion, Data Mining, Social Media, Tweet, Naive Bayes Classifier

Abstract

The causes of traffic congestion in Indonesia include traffic accidents, poor road infrastructure, and the increasing number of motor vehicles. In 2023, the number of vehicles reached 152.6 million, exceeding half of Indonesia's population of 276 million, according to the Indonesian Traffic Police Corps data. Twitter has a user base of approximately 4.23% of the total global population, which amounts to 436 million user and Indonesia is one of the countries with the largest number of Twitter users. Twitter data will be used to determine the sentiment level of traffic congestion in Indonesia using the Naïve Bayes Classifier method to evaluate overall accuracy performance, precision, recall, and f1-score. The research classified two groups, negative and positive. Classification is carried out through several stages, including data pre-processing, data training, data testing, and evaluation. After evaluating the Naive Bayes algorithm, the highest results achieved an overall accuracy of 77%, precision of 86%, recall of 82%, and f1-score of 84%.

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Published

2024-04-30

Issue

Vol. 8 No. 2 (2024): April 2024

Section

Articles

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