The Effect of Temperature, Weight and Roasting Time on the Roasted Level of Coffee Beans Using Fuzzy Logic

Authors

  • Fannan Cakti Wibisono College of Vocational Studies, IPB University Author
  • Chika Hayya Sabillah College of Vocational Studies, IPB University Author
  • Muhammad Faiz Assariy College of Vocational Studies, IPB University Author
  • Daffa Zulqisthi College of Vocational Studies, IPB University Author
  • Amanda Catur Sukmarani College of Vocational Studies, IPB University Author
  • Putri Alya Faisal College of Vocational Studies, IPB University Author
  • Muhammad College of Vocational Studies, IPB University Author
  • Cut Mesya Meuthia College of Vocational Studies, IPB University Author
  • Farida Fadhilatun Nisa College of Vocational Studies, IPB University Author
  • Nuriah Nurani Muhsin College of Vocational Studies, IPB University Author
  • Lukie Trianawati College of Vocational Studies, IPB University Author

DOI:

https://doi.org/10.62535/crtn5j19

Keywords:

fuzzy logic, coffee roasting, Mamdani Method, roast maturity, quality control

Abstract

This study examines the effect of temperature, roasting time, and coffee bean weight on the maturity level of roasted coffee beans, utilizing fuzzy logic to model and predict optimal roast outcomes. Coffee bean roasting level, a critical factor in determining flavor, is assessed using Mamdani-type fuzzy logic, which takes three input parameters—temperature, time, and weight—divided into ranges for specific roast outcomes. Case study parameters, including a coffee bean weight of 450 grams, roasting time of 39 minutes, and temperature of 130°C, were analyzed to classify the roast maturity. The fuzzy logic output indicates that this specific case falls within the medium roast level by the similarity of centroid between moment calculation and matlab calculation is 10,7, demonstrating the model's capability to provide precise roast classifications. This structured approach to evaluating coffee roasting parameters contributes to enhancing consistency and quality in the coffee industry, highlighting the potential of fuzzy logic for robust decision support in coffee quality control.

Author Biographies

  • Fannan Cakti Wibisono, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Chika Hayya Sabillah, College of Vocational Studies, IPB University

    Student of Computer Engineering Technology, College of Vocational Studies, IPB University

  • Muhammad Faiz Assariy, College of Vocational Studies, IPB University

    Student of Computer Engineering Technology, College of Vocational Studies, IPB University

  • Daffa Zulqisthi, College of Vocational Studies, IPB University

    Student of Computer Engineering Technology, College of Vocational Studies, IPB University

  • Amanda Catur Sukmarani, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Putri Alya Faisal, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Muhammad, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Cut Mesya Meuthia, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Farida Fadhilatun Nisa, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Nuriah Nurani Muhsin, College of Vocational Studies, IPB University

    Student of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

  • Lukie Trianawati, College of Vocational Studies, IPB University

    Lecturer of Food Quality Assurance Supervisor at College of Vocational Studies, IPB University

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Published

2025-06-14

Issue

Vol. 2 No. 3 (2025): June 2025

Section

Articles

How to Cite

The Effect of Temperature, Weight and Roasting Time on the Roasted Level of Coffee Beans Using Fuzzy Logic. (2025). Journal of Applied Science, Technology & Humanities, 2(3), 365-381. https://doi.org/10.62535/crtn5j19