Analysis Sentiment Terhadap Ginjal Akut pada Twitter Menggunakan Algoritma Random Forest
DOI:
https://doi.org/10.54082/jiki.65Kata Kunci:
Cross Validation, Klasifikasi, Machine Learning, Particle Swarm Optimization, Random Forest, TwitterAbstrak
Analisis sentimen berkaitan dengan identifikasi dan klasifikasi pendapat atau sentimen yang diungkapkan dalam teks sumber. Sosial media menghasilkan sejumlah besar data yang penuh dengan sentimen berupa tweet, update status, postingan blog maupun yang lainnya. Analisis sentimen dari data yang dihasilkan pengguna ini sangat berguna dalam mengetahui pendapat dari kerumunan. Dalam makalah ini, dilakukan analisis postingan twitter tentang Ginjal Akut menggunakan pendekatan Machine Learning, yaitu dengan menggunakan Algoritma Random Forest dan Particle Swarm Optimization. Hasil penelitian menunjukkan akurasi yang di dapat pada saat melakukan klasifikasi menggunakan algoritma Random Forest dengan Cross Validation sebesar 94.47%.
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