Perbandingan Algoritma Klasifikasi dalam Pendeteksian Hoax pada Media Sosial
DOI:
https://doi.org/10.26418/jari.v1i1.53126Keywords:
Klasifikasi, Hoax, Pembelajaran Mesin, Penggalian TeksAbstract
Pada media sosial, hoax atau berita palsu sering beredar. Pendeteksian hoax secara otomatis memerlukan program ataupun aplikasi yang mengimplementasikan algoritma klasifikasi untuk dapat membedakan konten hoax dengan konten fakta. Pada penelitian ini, lima algoritma klasifikasi, yaitu Multilayer Perceptron, Naïve Bayes, Support Vector Machine, Decision Tree, dan Random Forest dibandingkan kemampuannya dalam mengklasifikasikan data teks Tweet dari media sosial Twitter untuk mengetahui algoritma manakah yang paling akurat dalam mengklasifikasikan data tersebut. Pengujian yang dilakukan adalah pengujian, precision, recall, f1-score dan accuracy. Menurut hasil pengujian rata-rata nilai precision, algoritma Random Forest mendapat nilai tertinggi yaitu 0,8221, sedangkan Support Vector Machine terendah pada 0,7802. Untuk rata-rata nilai recall, Support Vector Machine mendapat nilai tertinggi dengan skor 1,000 sedangkan Multilayer Perceptron terendah dengan skor 0,7990. Untuk F1-Score, rata-rata nilai tertinggi terdapat pada algoritma Naive Bayes, yaitu 0,8742, sementara rata-rata nilai terendah terdapat pada algoritma Multilayer Perceptron dengan nilai 0,7989. Pada rata-rata nilai accuracy, nilai tertinggi berada pada algoritma Naive Bayes dengan nilai 0,7933 dan nilai terendah berada pada algoritma Multilayer Perceptron dengan nilai 0,7033.
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