Distribution Analysis of Student Numbers by Gender Using Decision Tree and Data Visualization

Risca Sri Mentari; Sri Wahyuni

Authors

Risca Sri Mentari Universitas Pembangunan Panca Budi, Indonesia
Sri Wahyuni Universitas Pembangunan Panca Budi, Indonesia

Keywords:

Data mining, C4.5, Decision Tree

Abstract

Rapid technological developments have brought significant changes in various sectors, including education. In the context of education, data management and analysis are important elements in supporting data-driven decision-making. Data mining, specifically the Decision Tree method, provides valuable insights into analyzing patterns from large data sets. This study uses Decision Tree modeling and data visualization through RapidMiner to analyze the distribution of the number of students based on gender in various classes at SMK Negeri 1 Stabat in the 2023-2024 school year. This research includes data collection, preprocessing, and decision tree modeling to uncover gender-based trends in various skill programs. Visualization using Scatter Plot makes it easier to present data for clearer analysis. The results of the study show that administrative and fashion skills programs are dominated by women, while engineering skills programs, such as TKR and TITL, are dominated by men. Some classes showed a more balanced gender composition. This research provides useful insights for classroom management and decision-making in the educational environment, as well as provides a basis for designing more inclusive learning programs and addressing gender imbalances in certain areas.

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