Analisis Pola Evolusi Hominid Berdasarkan Data Morfometrik Menggunakan Principal Component Analysis

Abstract

This study aims to analyze hominid evolutionary patterns based on morphometric data using Principal Component Analysis (PCA). The PCA method is used to reduce data dimensionality and extract the main components that can describe morphological variation among hominid species. The dataset used contains several quantitative features such as skull size, brain volume, and other anatomical characteristics from various hominid genera and species. The analysis results show that the first two principal components can explain over 80% of the total variation in the dataset, indicating a clear pattern separation among hominid groups. The application of the PCA method to hominid evolution data, visualized interactively using Python-based visualization with Seaborn, which has not been widely applied in the context of quantitative paleontology. This approach allows for more objective observation of evolutionary patterns based on morphometric characters without subjective intervention from manual interpretation.