Analisis Sentimen Kualitas Bahan Bakar Pertamina Menggunakan Algoritma Support Vector Machine Pada Platform X - Setyawan, Jefri; Omegatha Lamanda, Julius; Ibnu Nasrulloh, Faisal Raihan; Nur Addin, Shofyan; Yogi Pamungkas, Achmad; Nur Hasan, Fuad (Program Studi Informatika Fakultas Teknik Universitas Janabadra, 2026)
Fuel quality is an important factor that influences engine performance and customer satisfaction. As the main fuel provider in Indonesia, Pertamina must ensure that its products meet public expectations. This study aims to analyze public sentiment toward the quality of Pertamina’s fuel products based on data obtained from Platform X (Twitter), using the Support Vector Machine (SVM) algorithm as the primary classification method. Data were collected through a crawling process using the tweet-harvest library version 2.6.1 from April 1, 2025, to September 30, 2025, resulting in 1,596 comments. The data then underwent preprocessing, including cleaning, case folding, normalization, tokenizing, stopword removal, and stemming. Sentiment labeling was performed automatically using a lexicon-based approach with two categories—positive and negative—resulting in 799 positive and 795 negative data points. The classification process was carried out using SVM with an 80% training and 20% testing data split. Based on the evaluation results, the SVM model demonstrated effective and stable performance in classifying Indonesian-language text sentiment in the context of public opinion toward Pertamina’s fuel products.