APPLICATION OF THE WEIGHTED AGGREGATED SUM PRODUCT ASSESSMENT (WASPAS) METHOD FOR SELECTING ENGINE OIL FOR AUTOMATIC MOTORCYCLES AT DEDE EXTREM MOTORCYCLE WORKSHOP

Abstract

Selecting the right engine oil is essential for maintaining performance and extending the lifespan of automatic motorcycle engines. At Dede Extrem Motorcycle Workshop, oil recommendations are still made subjectively based on mechanic habits or preferences without systematic evaluation of technical criteria, which may cause mismatches between engine requirements and the lubricant used. This study aims to develop a Decision Support System (DSS) using the Weighted Aggregated Sum Product Assessment (WASPAS) method to determine the best oil based on six criteria: viscosity, temperature resistance, lubrication level, fuel efficiency, price affordability, and brand reputation. Using real-price data for three oil alternatives, the ranking results indicate Motul Scooter LE (A3) as the top recommendation with Qi = 0.9750, followed by Castrol Power 1 Scooter (A2) Qi = 0.9414 and Shell Advance AX7 Scooter (A1) Qi = 0.9367. In addition, a 1–5 scale evaluation test (five alternatives) shows A5 as the best alternative with Qi = 0.91 (rank 1), while A4 has the lowest score with Qi = 0.65 (rank 5). These results suggest that technical performance criteria have a stronger influence than price in determining oil recommendations. The developed system provides a more objective and consistent ranking output to assist mechanics in decision making.