Perbandingan Algoritma Klasifikasi Sentimen Twitter Terhadap Insiden Kebocoran Data Tokopedia

Nadhif Ikbar Wibowo (Institut Teknologi Sepuluh Nopember)
Tri Andika Maulana (Institut Teknologi Sepuluh Nopember)
Hamzah Muhammad (Institut Teknologi Sepuluh Nopember)
Nur Aini Rakhmawati (Institut Teknologi Sepuluh Nopember)

JISKa (Jurnal Informatika Sunan Kalijaga)
Vol. 6 No. 2 (2021): Mei 2021

Public responses, posted on Twitter reacting to the Tokopedia data leak incident, were used as a data set to compare the performance of three different classifiers, trained using supervised learning modeling, to classify sentiment on the text. All tweets were classified into either positive, negative, or neutral classes. This study compares the performance of Random Forest, Support-Vector Machine, and Logistic Regression classifier. Data was scraped automatically and used to evaluate several models; the SVM-based model has the highest f1-score 0.503583. SVM is the best performing classifier.