Penerapan Algoritma Support Vector Machine dan XGBoost Dalam Mengklasifikasikan Sentimen Opini Publik Terhadap Aplikasi Uber
DOI:
https://doi.org/10.55606/jitek.v5i1.5735Keywords:
XGBoost, Support Vector Machine, Uber Reviews, SMOTE, NLPAbstract
The development of application-based transportation services such as Uber has driven an increase in the number of public opinions distributed through various digital platforms. Sentiment analysis of this public opinion is important to understand user perceptions of Uber services. This study applies the Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms to classify public opinion sentiment, by optimizing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The data used comes from Uber reviews on public platforms, which are grouped into positive, negative, and neutral sentiments. The experimental results show that the SVM algorithm has superior performance with an accuracy of 94%, while XGBoost experienced an increase in accuracy of up to 93% after applying SMOTE. This study provides insight into the effectiveness of machine learning algorithms in sentiment analysis and its implementation in the development strategy of application-based transportation services.
Abstrak: Perkembangan layanan transportasi berbasis aplikasi seperti Uber telah mendorong peningkatan jumlah opini publik yang disalurkan melalui berbagai platform digital. Analisis sentimen terhadap opini publik ini menjadi penting untuk memahami persepsi pengguna terhadap layanan Uber. Penelitian ini menerapkan algoritma Mesin Vektor Pendukung (SVM) dan Peningkatan Gradien Ekstrem (XGBoost) untuk mengklasifikasikan sentimen opini publik, dengan mengoptimalkan ketidakseimbangan data menggunakan Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan berasal dari ulasan Uber di platform publik, yang dikategorikan ke dalam sentimen positif, negatif, dan netral. Hasil eksperimen menunjukkan bahwa algoritma SVM memiliki performa lebih unggul dengan akurasi mencapai 94%, sementara XGBoost mengalami peningkatan akurasi hingga 93% setelah penerapan SMOTE. Penelitian ini memberikan wawasan mengenai efektivitas algoritma pembelajaran mesin dalam analisis sentimen serta implikasinya terhadap strategi pengembangan layanan transportasi berbasis aplikasi.
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