Implementasi Metode Weighted Aggregated Sum Product Assesment (WASPAS) dalam Pemilihan Oli Mesin Sepeda Motor 150 CC

Abstract

There are many types and types of engine lubricating oil, so you must be selective in choosing the right lubricant (oil) to preserve the life time of the engine. With the oil, the friction between the components in the engine becomes smoother and makes it easier for the engine to reach the ideal temperature. Oil is often used to reduce friction (friction), if two surfaces that are attached to each other move, friction will arise. If the engine heat is not absorbed, the wear and tear of engine components will accelerate. The purpose of this study is to implement a decision support system using the Weighted Aggregated Sum Product Assessment (WASPAS) method in selecting the best motorcycle engine oil so that it can assist motorcycle riders in choosing the best engine oil. Motorcycle owners do not experience difficulties and do not need a long time in the selection of engine oil for their motorcycles. The Waspas method can be applied in solving the problem of determining the best lubricant (oil) for 150cc sport motorbikes. The best lubricant (oil) based on the calculation results of 5 alternatives with a value above 0.60, namely deltalube daily with the highest score of 0.6906, repsol mx25 with a value of 0.6902, ahm oil mpx 2 with a value of 0.6644, federal ultratec with a value of 0.6238 and shell advance ax5 with a value of 0.6097. With the application of the desktop-based Waspas method in decision making, it can make it easier for motorcycle owners to select the best lubricant (oil) for 150cc sport motorbikes.

Pemilihan Penerima Bantuan Hunian Rumah Susun Kota Tanjungbalai Dengan Metode WASPAS

“…WASPAS (Weighted Aggregated Sum Product Assesment) merupakan metode yang dapat mengurangi kesalahankesalahan ataupun mengoptimalkan dalam sebuah penaksiran untuk pemilihan dari nilai yang tertinggi hingga terendah [5] [6]. Metode WASPAS adalah mencari prioritas pilihan lokasi yang paling sesuai dengan menggunakan pembobotan [7]. Metode WASPAS ini merupakan kombinasi unik dari MCDM (Multi Criteria Decision Making) [8] yaitu model jumlah tertimbang yang dapat memperkecil kesalahan-kesalahan atau meningkatkan penilaian untuk pemilihan nilai tertinggi dan terendah [9] [10].…”