Data Classification Training Using Naive Bayes to Develop Data Literacy at SMK Media Informatika

Abstract

Data processing skills are essential in the digital era, especially for vocational high school (SMK) students preparing for technology-driven careers. This community service activity aimed to enhance data literacy among SMK Media Informatika students through training in data classification using the Naive Bayes algorithm, a fundamental method in data science and machine learning. The algorithm was chosen for its simplicity, ease of understanding, and relevance in introducing probabilistic decision-making logic. The training was conducted interactively, covering basic data concepts, dataset visualization, and practical implementation using Python. The results showed improved student understanding of classification concepts and their application to real-world problems, such as user data category prediction. The activity also encouraged analytical thinking, awareness of valid data collection, and interest in data science. This training is expected to serve as a model for applied learning in vocational schools and support the development of data-oriented curricula at the vocational education level.