Analisis Sentimen Kecelakaan Lalu Lintas Di Tweet Twitter Menggunakan Metode Naïve Bayes - Aldy Sahputra Saragih; Evantrana Yordan Bangun; Fastabikha Akbar Fahlevi; Ariel Kurniawan; Kristian Sigalingging; Fasta Krel Four Wati Br Haloho; Salman Putra Jaya Hulu (Journal of Engineering and Applied Technology, 2026)
This study develops a robust computational framework using a structured Naïve Bayes architecture to mine, classify, and analyze public discourse regarding traffic accidents on Twitter (now X). Utilizing a massive corpus of 157,629 digital documents, the probabilistic model successfully extracts macro-level sentiment distributions with a high global accuracy of 82.60%. The empirical findings reveal an overwhelming dominance of external factor attributions, accumulating 128,613 tweets, which underscores a critical public sensitivity toward infrastructure malfunctions and suboptimal road conditions in urban environments. By transforming unorganized microblogging texts into structured analytical matrices, this research establishes an economical, real-time social sensor that effectively bypasses the logistical delays of conventional manual reporting systems. Ultimately, this study contributes a novel theoretical repositioning from reactive safety evaluations to data-driven preventive strategies, providing government authorities with a rigorous, scalable decision-making tool to optimize sustainable transportation infrastructure and mitigate urban traffic fatalities.