Analisis Sentimen Terhadap Tragedi Kanjuruhan Malang dan Tragedi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Classification and Regression Tree

PAMUNGKAS, BAGAS DWI (2023) Analisis Sentimen Terhadap Tragedi Kanjuruhan Malang dan Tragedi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Classification and Regression Tree. S1 thesis, Universitas Mercu Buana Bekasi.

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Abstract

Pada 1 Oktober 2022 terjadi pertandingan sepak bola di Indonesia antara Arema melawan Persebaya di stadion Kanjuruhan, Malang, Jawa Timur. Pertandingan ini menjadi trending topik di twitter dan memunculkan berbagai macam komentar yang memicu timbulnya pro-kontra karena terjadi tragedi setelah pertandingan berakhir. Begitu juga dengan Covid-19 yang dimana menjadi wabah penyakit bagi dunia dan tentunya menjadi trending juga pada media sosial twitter. Tujuan penelitian ini untuk mengimplementasikan algoritma CART pada analisis sentimen terhadap tragedi kanjuruhan dan tragedi Covid-19 pada media sosial twitter. hasil testing report menggunakan Algoritma CART mendapatkan hasil akurasi atau f1-score sebesar 80%, presisi false negative 84%, presisi true positve 63%, recall negative 89% dan recall positive 53%. algoritma CART bisa digunakan untuk analisis sentimen dengan data splitt sebesar 75:25 mendapatkan hasil akurasi sebesar 85% data training dan 80% data testing. On October 1 2022, a football match took place in Indonesia between Arema and Persebaya at the Kanjuruhan stadium, Malang, East Java. This match became a trending topic on Twitter and gave rise to various comments that sparked pros and cons because a tragedy occurred after the match ended. Likewise with Covid-19 which has become a disease outbreak in the world and of course has also become trending on Twitter social media. The aim of this research is to implement the CART algorithm in sentiment analysis regarding the Kanjuruhan tragedy and the Covid-19 tragedy on Twitter social media. The results of the testing report using the CART algorithm obtained an accuracy or f1-score of 80%, false negative precision of 84%, true positive precision of 63%, negative recall of 89% and positive recall of 53%. The CART algorithm can be used for sentiment analysis with data split of 75:25 to get accuracy results of 85% of training data and 80% of testing data.

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