Klasterisasi Hewan berdasarkan Morfologi dengan K-Means Klastering untuk Memudahkan Pemahaman Taksonomi Hewan

Abstract

Grouping animals based on their morphological characteristics is crucial for understanding biodiversity and evolutionary relationships among species. This study aims to apply the K-Means Clustering algorithm to group animals based on their morphological characteristics and evaluate its performance. A secondary dataset from Kaggle containing 101 animals with 18 morphological attributes was used. Data preprocessing techniques such as handling missing data, removing irrelevant columns, and data normalization were performed. The optimal number of clusters was determined using the Davies-Bouldin Index and Silhouette Score, which resulted in 5 clusters as the best number. The K-Means algorithm successfully grouped the data into 5 clusters: flying animals with feathers, aquatic animals with fins, terrestrial mammals with hair and milk production, animals with many legs such as reptiles and insects, and terrestrial predators. Visualization of the clustering results using 3D scatter plots provided a clear visual representation. Interpretation of the results revealed patterns and evolutionary relationships between different groups of animals based on their morphological characteristics. This research contributes to the understanding of biodiversity, ecology, evolution, and conservation through morphology-based clustering, and demonstrates the effectiveness of the K-Means algorithm in this task.