Application of Self-Organizing Map and K-Means to Cluster Bandwidth Usage Patterns in Campus Environment

Abstrak

Unequal bandwidth distribution in campus environments often stems from a lack of understanding of WiFi usage patterns, as seen at Itenas Bandung. Here, bandwidth is allocated equally across all buildings, ignoring differences in demand, leading to inefficiencies in high-usage areas and poor money management due to unnecessary allocation of resources to low-demand buildings. This study aims to optimize bandwidth allocation by analyzing usage patterns using a combination of Self-Organizing Map (SOM) and K-Means clustering methods. SOM is used to group buildings into low, medium, and high bandwidth usage categories, while K-Means refines these clusters to enhance accuracy. The proposed approach demonstrated significant improvements in clustering quality, with the Silhouette Index increasing from 0.321 to 0.773 and the Davies-Bouldin Index dropping from 0.896 to 0.623 in the first test. Similar enhancements were observed in subsequent tests, highlighting the effectiveness of this method in addressing unequal bandwidth distribution. This research offers a practical solution for more efficient network and financial management in educational institutions.