Decision Suport System for Student laptop Selection Using the Simple Additive Weighting Method Based on KNIME Analytic Platform

Abstract

The fast development of information technology makes the laptop an important device for students in supporting academic activities. However, the number of laptop brands, specifications and price variations often make students confused in choosing a laptop that suits their academic needs and financial conditions. Hence, there is a need for a decision support system to help students objectively and efficiently decide the best laptop alternative. This research aims to apply the Simple Additive Weighting (SAW) method in the decision to choose the best laptop for students based on several criteria such as price, performance, and battery durability. The data for the research were collected through questionnaires distributed to university students and processed by the KNIME Analytics Platform. Data processing steps are data collection, normalisation, weighting, preference value calculation and ranking. The results of the study show that the SAW method is able to provide an objective recommendation based on the criteria and weights that have been determined. The results of the ranking indicate that the Lenovo ThinkPad E14 Gen 5 has the highest preference value when compared to other laptop alternatives. The system is expected to help students to make faster, accurate and effective decisions in choosing laptops based on their academic needs.