SENTIMENT ANALYSIS OF ELECTRIC CAR PRODUCT TRENDS IN INDONESIA USING BM25 AND K-NEAREST NEIGHBOR

Abstract

The global and Indonesian shift towards electric vehicles (EVs) is driven by efforts to reduce emissions and promote sustainable energy. Social media, especially Twitter, functions as an important measuring tool regarding public sentiment towards electric vehicles in Indonesia, so that it can influence policy making. This research uses the BM25 and K-Nearest Neighbor (KNN) methods to analyze sentiment, which aims to improve EV adoption strategies. Conducted in 2023, this research applies data mining, specifically Knowledge Discovery and Data Mining (KDD), analyzing primary and secondary data descriptively and quantitatively starting with data collection from Twitter, followed by data crawling and initial text processing. Next, labeling, term frequency (TF) and inverse document frequency (IDF) calculations were carried out using the BM25 and KNN methods, with an Evaluation and Validation Diagram that visualized the process. The findings show that negative sentiment dominates at 48% (4800 data), followed by 34% (3400 data) neutral sentiment and 18% (1800 data) positive sentiment. The balanced distribution of sentiment highlights the diverse perceptions of society. BM25 and KNN pre-processing methods effectively reduce overfitting and underfitting, especially in negative and neutral sentiments. Accuracy testing without BM25 resulted in 58.6% to 60.25%, while integrating BM25 with KNN increased accuracy by 12.5% to 71% to 72.75%. Understanding sentiment provides a basis for decision making and policy development, as well as providing insight into public perceptions of electric vehicles in Indonesia. Implications include leveraging positive sentiment for marketing, adjusting strategies, refining pricing, addressing infrastructure and reliability issues, and collaborating with governments to increase adoption of electric vehicles in society.