Fuel Efficiency Evaluation of Automatic Motorcycles in Indonesia Using MATLAB-Based Clustering

Eky Nur Fadhilla, Zelvia Monica, Farrah Anis Fazliatul Adnan, Jong Soo Rhee, Dianta Ginting

Abstract


The continuous rise in fuel prices in Indonesia has made fuel efficiency a crucial factor for consumers when selecting vehicles, particularly motorcycles. Automatic scooters with engine capacities below 160 cc have become increasingly popular in urban areas due to their fuel-saving benefits. This study aims to analyze the influence of engine capacity, vehicle weight, and engine torque on the fuel consumption of automatic scooters with engine capacities ranging from 109 cc to 156.9 cc. The study also considers additional performance parameters, including average fuel consumption, power output, and Power-to-Weight Ratio (PWR). Using statistical analysis and MATLAB-based modeling, the data were classified into three distinct clusters. Cluster 1 comprises scooters with engine capacities between 109 and 125 cc; Cluster 2 includes those with capacities between 150 and 160 cc; and Cluster 3 represents scooters with unique component specifications. The results show that Cluster 2 records the highest average maximum power output at 11.47 kW and torque at 14.25 Nm, while Cluster 1 has the lowest at 6.1 kW and 9.64 Nm, respectively. In terms of weight, Cluster 3 is the heaviest, averaging 129.33 kg, while Cluster 1 is the lightest at 96.14 kg. Fuel efficiency is highest in Cluster 1 at 55.3 km/l and lowest in Cluster 3 at 38.67 km/l. Comparative analysis using MATLAB confirms that scooters with lower engine capacities and weights tend to be more fuel-efficient, whereas higher engine capacities lead to increased torque, power, weight, and fuel consumption. These findings can guide consumers in selecting motorcycles that align with their usage needs and assist manufacturers in developing more efficient and high-performing scooters tailored to diverse market segments.

Keywords


Fuel efficiency modeling; automatic scooters; MATLAB-based clustering; regression

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DOI: http://dx.doi.org/10.22441/ijimeam.v7i2.31577

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