Perbandingan Analisis Sentimen Aplikasi Traveloka dan Tiket.com pada Twitter dengan Metode Support Vector Machine - Rukmana, Putri Utami; Pratiwi, Oktariani Nurul; Fakhrurroja, Hanif (APIC, 2023)
The emergence of the COVID-19 pandemic in Indonesia resulted in an economic crisis, including in the world of tourism, which caused a decline in the national economy. With the existence of Online Travel Agencies (OTA) such as Traveloka and Tiket.com, it is hoped that they can help improve the tourism sector for the Indonesian economy. As a popular OTA and to see the opinion of the Indonesian people, it can be seen from public opinion in the form of tweets on the Twitter application. The tweets data will be taken and sentiment analysis will be carried out on the OTA Traveloka and Tiket.com applications which will be classified into certain classes based on opinions and modeling will be carried out using the Support Vector Machine (SVM) algorithm method. This research aims to determine the level of accuracy of the SVM algorithm and find out how sentiment analysis compares between Traveloka and Tiket.com. In the sentiment analysis comparison, in terms of price, Traveloka is superior and in terms of service, Tiket.com is superior. After modeling by comparing splitting data and handling imbalanced data using Synthetic Minority Oversampling Technique (SMOTE), the best SVM accuracy results for the Tiket.com price dataset were 68%, for Traveloka prices it was 97%, for Tiket.com services it was 92%, and for Traveloka services it is 89%.