Fadhillah Suhendar
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

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Implementasi Metode Simple Additive Weighting (SAW) dalam Penentuan Menu Best Seller pada Warkop Gasskeun, Jakarta Timur Fadhillah Suhendar; Veri Arinal; Mesra Betty Yel
Journal Digital Technology Trend Vol. 5 No. 2 (2026): December
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jdtt.v5i2.450

Abstract

At Warkop Gasskeun East Jakarta, the determination of the best-selling menu item currently relies on the owner's intuition and manual observation, making the decision-making process subjective and unable to evaluate multiple performance criteria simultaneously. This study aims to implement the Simple Additive Weighting (SAW) method to determine the best-selling menu objectively through a decision support system framework. Evaluation was conducted across four criteria with predetermined weights: sales volume (50%), customer ratings (30%), profit (15%), and menu price (5%), categorized into benefit and cost attributes. Research data were gathered through direct observation, interviews with the business owner, and sales documentation, covering all available food and beverage items through total population sampling. The SAW procedure involved criteria weighting, decision matrix normalization, preference value calculation, and alternative ranking. The calculation results demonstrated that Indomie Double Telur achieved the highest preference score of 0.9667, establishing it as the primary best-selling menu item, closely followed by Nasi Daun Jeruk Telur with a score of 0.9625. The method was successfully integrated into a web-based decision support system, where verification confirmed an exact match between manual calculations and automated system outputs. Furthermore, black box testing confirmed that all functional modules—including data management, SAW processing, and reporting—operated as intended, thereby providing the business owner with a faster, objective, and data-driven decision-making tool.