Nisrina Nur Puspanegara
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Perbandingan Metode Simple Additive Weighting (SAW) Dan Weighted Product (WP) Dalam Pemilihan Aplikasi Belanja Sayur Online Nisrina Nur Puspanegara; Dwi Asih Haryanti
Journal of Informatics Management and Information Technology Vol. 5 No. 2 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jimat.v5i2.471

Abstract

The selection of the right online vegetable shopping application is becoming increasingly important as public awareness of a healthy lifestyle grows. The aim of this study is to develop a Decision Support System (DSS) that can help consumers choose the best online vegetable shopping application. This research contributes to the development of a decision support system for selecting an online vegetable shopping application by comparing the performance of two multi-criteria methods: Simple Additive Weighting (SAW) and Weighted Product (WP). A case study was conducted on three applications HappyFresh, Segari, and Sayurbox considering criteria such as price, product quality, application convenience, and promotions. This research is quantitative in nature, aiming to test predetermined hypotheses. The data sources used in this study include both primary and secondary data. The sampling technique applied was purposive random sampling, involving 100 respondents. The selected respondents were users of HappyFresh, Sayurbox, and Segari applications residing in the Jabodetabek area. The analysis results show that both methods produced consistent rankings, with the HappyFresh application consistently ranking first. This indicates that HappyFresh has the best performance in meeting user expectations based on the established criteria. Based on calculations using the SAW and WP methods, HappyFresh obtained the highest score in both methods, with a score of 1 using SAW and 0.3341 using WP. This suggests that HappyFresh is the most recommended application among HappyFresh, Sayurbox, and Segari..