Web-Based Decision Support System for Best Laptop Selection Using MABAC Method

Abstract

The advancement of technology in the modern era has made devices such as laptops essential in daily life. According to a report from Ministry of Communication and Information Technology of Indonesia that published in 2017, from a survey of 2,121 respondents showed that more than half percent respondent use laptop for work and study, while 34.94% use laptop for entertainment. However, selecting the right laptop often poses a challenge, especially for students in the Informatics Engineering Department at Universitas Negeri Surabaya, who frequently use outdated laptops. To address this issue, a Decision Support System (DSS) is needed, utilizing the Multi-Attributive Border Approximation Area Comparison (MABAC) method. In this study, the MABAC method was used to select laptops based on criteria such as price, CPU, RAM, and storage. By applying the MABAC method, the DSS is believed to effectively address the issue of selecting the most suitable laptop, thereby enhancing productivity and performance. This research successfully developed a web-based Decision Support System (DSS) for selecting the best laptops using the Multi-Attributive Border Approximation Area Comparison (MABAC) method, which simplifies the evaluation process for users. The DSS incorporates 10 criteria: price, processor, RAM, storage, storage type, screen size, graphics card, laptop weight, battery, operating system, and warranty. The MABAC calculations ranked the Asus Vivobook 14 A1400EA as the best laptop with a score of 0.15, followed by the HP 14s EP0022TU and Lenovo Ideapad Slim 3 14ITL6 with scores of 0.05, while the Dell Latitude 3420 ranked last with a score of -0.05.