Comparative study of NBC, SVM, and CNN performance in sentiment analysis of the millennial farmers program on platform X - Adamu Abu Bakar Ibrahim; Junaedi Rahmat; Deshinta Arrova Dewi (Indonesian Artificial Neural Network Society, 2026)
Public perception of government programs is often expressed on social media, making sentiment analysis a vital tool for understanding public opinion. This research presents a comparative study of three machine learning algorithms, Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Convolutional Neural Network (CNN), applied to sentiment analysis on the Millennial Farmers Program 2024 from Platform X. The study aims to measure the performance of each algorithm in terms of accuracy, execution time, and memory efficiency by adopted a quantitative approach with a comparative experimental design. Sentiment data were collected from platform X (Twitter) between October 2024 and January 2025, with a total of 5,177 raw tweets acquired using the keyword