Penerapan Naive Bayes dalam Mengidentifikasi Penyebab Remaja Desa Terlibat dalam Judi Online - Annabil, M Haziq; R, Rakhmat Kurniawan (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
The development of digital technology and increased internet access have driven changes in adolescent social behavior, including increased involvement in online gambling activities, not only in urban areas but also in rural areas. This study aims to identify the factors that influence adolescent involvement in online gambling and to measure the level of influence of each factor using a machine learning-based classification approach. The research was conducted in Payageli Village, Sunggal District, Deli Serdang Regency, involving 507 adolescent respondents who were surveyed using a questionnaire containing 23 questions. The data obtained was analyzed using the Naive Bayes algorithm with data preprocessing, categorical attribute coding, and data division into training and test data with a ratio of 80:20. The results showed that the dominant factors influencing adolescent involvement in online gambling included peer influence, intensity of exposure to online gambling advertisements, online gaming habits, low positive leisure activities, and lack of parental supervision. The classification model built produced an accuracy rate of 98%, with high precision and recall values in each class. These findings indicate that the Naive Bayes algorithm is effective in identifying adolescents at risk of engaging in online gambling and has the potential to be used as a basis for developing data-driven prevention strategies at the village level.