Perbandingan Klasifikasi Tipe Kesuksesan Generasi Z Menggunakan Algoritma Naïve Bayes dan Decision Tree

Abstract

This study aims to classify the types of success of Generation Z using the CRISP-DM method approach and using the Naïve Bayes and Decision Tree algorithms. Generation Z who grew up in a digital environment has a unique view of the meaning of success, which is no longer limited to income or position, but also includes life balance and self-development. This study identifies several important factors such as educational background, technological skills, work experience, personal branding, and use of social media as determining variables in the classification of types of success. The classification model produces four main categories of success, namely financial, career, self-development, and life balance. The results showed that life balance was the most dominant category of success among respondents. The use of the Naïve Bayes and Decision Tree algorithms showed that Decision Tree with balancing techniques (random oversampling) provided the highest classification accuracy, which was 94%, compared to Naïve Bayes which only reached 37%. This study makes an important contribution to the development of human resource strategies, education, and policies that are relevant to the characteristics and aspirations of Generation Z in the digital era.