Cattle Transport Drivers Clustering using PCA and K-Means Algorithm

Abstract

This research explores the logistics of cattle transportation in Indonesia, specifically focusing on its impact on cattle welfare during travel. Through interviews and observations involving 73 cattle transporters and ranchers, the study investigated critical factors such as physiological, thermal, and overall condition stress of cattle. With a methodological approach that includes fact-finding and data understanding, preprocessing, data transformation with PCA, and K-Means clustering, this analysis reveals that two main components can account for about 44.71% of the total data variation. 27 of the 72 rows of data fit into cluster 0, 22 rows of data went into cluster 1, and 24 rows of data went into group 2. The findings have implications for designing more humane and efficient transport strategies, potentially reducing stress-induced weight loss in cows. In addition, the results of the study provide insights for practitioners and stakeholders in optimizing route planning, driver selection, and overall logistics management. Research findings can provide input for improved regulation in the cattle sector, encouraging responsible and ethical practices. In addition, the study suggests the potential for developing guidelines or training programs for cattle transporters, improving their skills and awareness regarding good animal treatment during travel. Overall, this research opens up opportunities to improve practices and policies in the cattle farming industry in Indonesia, with a positive impact on animal welfare and the sustainability of the cattle supply chain as a whole.