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Fuzzy Logic System Application for Banana Shelf Life Prediction with Sensor-Based Microclimate Aryo Sumaryanto; Roma Juliana Arios; Annisa Raihanah Maimun; Mrr Lukie Trianawati; Zalfa Cantika; Tiara Arelya Priyanto; tangkas Patiasmara; Nashwa Zahrania Adiputri; Khaerun Nisa; Kezia Joy Audreyna; Fatia Syafana; Wulidah Tamsil Barokah
Journal of Applied Science, Technology & Humanities | JASTH Vol. 3 No. 1 (2026): January 2026
Publisher : Batrisya Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62535/8dap4r21

Abstract

Bananas are climacteric fruits with high postharvest loss rates due to rapid ripening and spoilage under uncontrolled microclimate conditions. This study aims to develop a fuzzy logic-based system for predicting banana shelf life using real-time sensor data and a web dashboard interface. The research employed a quantitative descriptive method using the Mamdani fuzzy inference system in MATLAB, with input variables including temperature (°C), relative humidity (%), and ethylene concentration (µL/L). The output variable was shelf life, categorized as Fresh, Starting to Spoil, or Spoiled. Simulation results showed that optimal conditions (temperature 18°C, humidity 85%, ethylene 1 µL/L) yielded a defuzzification value of 0.853, indicating high freshness. Conversely, suboptimal conditions (temperature 20°C, humidity 70%, ethylene 0.1 µL/L) produced a value of 0.493, reflecting moderate freshness. The fuzzy logic system effectively modeled nonlinear relationships and uncertainty in sensor data, enabling adaptive shelf life prediction. These findings confirm that integrating fuzzy logic with microclimate sensors and dashboard visualization enhances decision-making in fruit storage management
Co-Authors Annisa Raihanah Maimun Aryo Sumaryanto Fatia Syafana Kezia Joy Audreyna Khaerun Nisa Mrr Lukie Trianawati Nashwa Zahrania Adiputri Roma Juliana Arios Tiara Arelya Priyanto Wulidah Tamsil Barokah Zalfa Cantika