Klasifikasi Data pada Sistem Penjurusan dengan Preferensi Standar Simple Additive Weighting (PS-SAW)

Abstract

Academic potential of students becomes the main success factor in learning activity. Academic potential analysis with various types of variables requires reliable computational classification techniques. The main objective of this study is to implement the Simple Additive Weighting formula with Standard Preference approach (PS-SAW) as a new technique in classification field. In this study, PS-SAW was implemented based on research need and research objectives. The PS-SAW method was applied by finding average value of preferences from the best data classification as a standard preference, and further becomes the basis for determining the classification of new data. The results showed that the implementation of PS-SAW in data test was more selective than the basic SAW with selectivity about 21,02%, and early recommendation by 85,99%. This research can be a reference for building major system and can be implemented in general application classification system.