PREDICTION OF 2024 PRESIDENTIAL ELECTION USING K-NN WITH METRIC APPROACHES CHEBYSHEV AND EUCLIDEAN BASED ON TWITTER DATA INVESTIGATION - Darmawan, Steven Ryan; Fatchan, Muhamad; Maulana, Donny (Jurnal Teknik Informatika (Jutif), 2024)
The potential difference between the popularity of presidential candidates on social media and in the general public poses a serious challenge in predicting the outcome of the 2024 presidential election. Technical constraints in collecting, cleaning and analyzing dynamic and large-scale social media data can threaten the accuracy and validity of predictions. To overcome this problem, careful steps and in-depth understanding are needed. Therefore, this study aims to predict the winner of the 2024 presidential election from the popularity of presidential candidates Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto on Twitter. The K-Nearest Neighbor (K-NN) method with the Both Metric approach (Euclidean and Chebyshev) was used to analyze 51,192 tweet data through the Knowledge Discovery in Database (KDD) stage using Orange software. The evaluation results show almost the same performance, with AUC values of 0.725 for Euclidean and 0.720 for Chebyshev. The CA result was 55.6% for Euclidean and 55.4% for Chebyshev. Although F1, precision, and recall were almost the same, overall, the Euclidean metric was better. The prediction shows Prabowo Subianto as the most popular candidate on Twitter. Nonetheless, these results need to be interpreted with caution and strengthened with further analysis and additional data to get a more comprehensive conclusion. This research shows that K-NN with both metrics can provide predictions above 50%, reliable enough to be able to predict the most popular candidates on Twitter.