Evaluating the Quality Grade of Cookies through Crispness and Baking Temperature Analysis Using a Fuzzy Inference System - Raihan Vivardian; Annisa Raihanah Maimun; Wuliddah Tamsil Barokah; Mrr. Lukie Trianawati; Arisya Putri Hanifah; Tausy Fadilla; Salma Anjela; Fransiska Yulia Chandra; Octari Rahmawati; Pelita Sari; Kayana Devin Bazzani Karo Karo; Roma Juliana Arios (Journal of Applied Science, Technology & Humanities | JASTH, 2026)
Cookies are one of the most popular processed food products due to their distinctive taste, long shelf life, and variety of textures ranging from soft to crisp. Physical properties such as crispiness play an important role in determining consumer preferences and acceptance. These characteristics are influenced by several factors, one of which is the baking temperature that determines the quality of soft cookies. Advances in computing technology have enabled objective food quality analysis through artificial intelligence approaches. This study aims to evaluate the suitability of cookies based on crispiness and baking temperature parameters using the Fuzzy Inference System (FIS) method. The research was conducted using a fuzzy system designed with input variables of temperature and crispiness, and output variables of product quality categorized as poor, moderate, and good. Each variable was represented in the form of a triangular membership function. The results showed that fuzzy logic was able to effectively integrate parameters, making it a smart and objective quality assessment method.