Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : journal of information technology software engineering and computer science

Web-Based Fuzzy Mamdani System for Adaptive Cost of Production in Culinary MSMEs Fallan Switly Fransisco Parengkuan; Chriestie Ellyane Juliet Clara Montolalu; Mahardika Inra Takaendengan
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 3 (2026): Volume 4 Number 3 July 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i3.352

Abstract

Culinary micro, small, and medium enterprises frequently experience financial instability because conventional deterministic costing methods lack the flexibility to handle daily fluctuations in raw material and overhead expenses. This study develops an adaptive Cost of Production model and generates pricing recommendations using a web-based Fuzzy Mamdani decision support system. The research employs an applied quantitative case study design focusing on a single culinary micro-enterprise operating in a volatile local market. The fuzzy engine processes raw material and overhead uncertainties as linguistic inputs using MIN-MAX inference, while treating direct labor as a constant parameter. Centroid defuzzification calculates the final adaptive cost. The results demonstrate that the conventional deterministic cost was Rp27,077, whereas the Fuzzy Mamdani model adjusted this value to Rp27,532. This 1.68 percent increase functions as a calculated risk premium that protects profit margins against minor supply chain volatilities. The system subsequently generated three margin-based pricing tiers. The standard tier with a 30 percent margin demonstrated a 97.74 percent goodness of fit when compared to actual market prices. The primary contribution is the deployment of a lightweight web artifact that removes the technical barriers associated with desktop-based fuzzy simulations. This tool enables non-technical business owners to transition from heuristic guesswork to transparent, data-driven pricing strategies that inherently buffer against operational cost uncertainty.