Implementation and Comparison of Coffee Bean Drying Temperature SettingsBased on Fuzzy Logic
DOI:
https://doi.org/10.62535/cj817x36Keywords:
Drying coffee beans, Fuzzy Logic, Mamdani, Sugeno, Temperature regulation.Abstract
This research examines the implementation and measurement of temperature in drying coffee beans
using a fuzzy logic approach. Two main methods, namely the Mamdani Method and the Sugeno
Method, were applied and evaluated in this context. Data on temperature, humidity, and water content
of coffee beans are collected during the drying process for use in the implementation of both methods.
Implementation is carried out using MATLAB software, with detailed steps for each method. The
Mamdani method involves fuzzification processes, inference using fuzzy rules, and defuzzification to
obtain concrete values for temperature settings. Meanwhile, the Sugeno Method also involves
fuzzification of input data, but uses a linear fuzzy model for inference, so it does not require a
defuzzification stage. The results and discussion of this study highlight the performance differences
between the two methods. Evaluation is carried out based on temperature prediction accuracy and
energy efficiency. The Mamdani Method shows good accuracy in predicting temperature, while the
Sugeno Method highlights efficiency and efficiency in the temperature regulation process. Therefore,
this study provides valuable insight into selecting a suitable method for temperature regulation of
coffee bean dryers
References
Adawiyah, D. (2020). The relationship between water sorption, glass transition
temperature and water mobility and their influence on product stability in food
models. Postgraduate School, 5, 21–25.
https://penerbitbukudeepublish.com/shop/buku-menanam-dan-meutilkan.
Baihaqi, B., Desparita, N., Fridayati, D., Akmal, A., & Hakim, S. (2022). Study of
strategies for implementing post-harvest technology in the coffee supply chain from
the aspect of added value and post-harvest losses. Journal of Agricultural
Processing Technology, 4(1), 18. https://doi.org/10.35308/jtpp.v4i1.5683
Bayu Setia, A. R. (2019). Application of Fuzzy Logic to Intelligent Systems. Journal of
Intelligent Systems, 02, 61–66.
Hafiz, M. A., & Sriani. (2023). Application of Fuzzy Sugeno Logic to Optimize Coffee
Bean Stock at Rooster Cafe. FASILKOM JOURNAL, 13(02).
https://doi.org/10.37859/jf.v13i02.5460
Kholidi, A. N., Trisanto, A., & Nasrullah, E. (2015). Design of Automatic Feeding
Equipment and Temperature Control for Broiler Chickens Based on Programmable
Logic Controller in Closed Cages. Journal of Electrical Engineering and
Technology, 9(2), 87–95.
Coffee, A., West, S., West, S., West, S., & West, S. (2013). AIR FLOW SPEED IN
SOLAR DRYER Endri Yani * & Suryadi Fajrin Department of Mechanical
Engineering Faculty of Engineering Andalas University Limau Manis Campus
Padang TeknikA. 20(1), 17–22.
Kurniawan, W. M., & Hastuti, K. (2017). Determining the Quality of Arabica Coffee
Beans Using Analytical Hierarchy Process (Case Study on a Coffee Plantation on
the Slopes of Mount Kelir Jambu Semarang). Symmet: Journal of Mechanical
Engineering, Electrical And Computer Science, 8(2), 519.
https://doi.org/10.24176/simet.v8i2.1358
Munarso, S. J. (2017). Postharvest Handling to Improve the Quality and
Competitiveness of Cocoa Commodities. Journal of Agricultural Research and
Development, 35(3), 111. https://doi.org/10.21082/jp3.v35n3.2016.p111-120
Nafisah, N., Syamsiana, I. N., Kusuma, W., Putri, R. I., & Sumari, A. D. (2023).
Comparative Analysis of Sugeno and Mamdani Interference Based Fuzzy Logic
Temperature Control in Coffee Bean Dryers. Agrotechnics, 6(2), 272–288.
https://doi.org/10.55043/agroteknika.v6i2.240
Namora, J., Sihombing, M., & Rahardjo, M. (2020). Effect of Drying Method on
Volatile Compounds on the Formation of Flavor of Robusta Coffee Beans. Food
Source Commodities to Improve Health Quality in the Covid -19 Pandemic Era,
–979.
Nugroho, B. I., Muzakhim, A., & Mustafa, L. D. (2021). Capacitor Sensor Design and
Use of Color Sensors to Detect the Maturity Level of Kawi Roasting Coffee Beans
Using Arduino-Based Fuzzy Logic. Jartel Journal Journal of Telecommunications
Networks, 11(3), 136–140. https://doi.org/10.33795/jartel.v11i3.114
Santoso, D., & Egra, S. (2018). The Effect of Drying Methods on the Characteristics and
Organoleptic Properties of Arabica Coffee Beans (Coffeae Arabica) and Robusta
Coffee Beans (Coffeae Cannephora). Agricultural Engineering Rona, 11(2), 50–56.
https://doi.org/10.17969/rtp.v11i2.11726
Santoso, D., Muhidong, D., & Mursalim, M. (2018). MATHEMATICAL MODEL OF
THIN LAYER DRYING OF ARABIC COFFEE BEANS (Coffeae arabica) AND
ROBUSTA COFFEE BEANS (Coffeae cannephora). Andalas Journal of
Agricultural Technology, 22(1), 86. https://doi.org/10.25077/jtpa.22.1.86-95.2018
Santoso, D., & Waris, A. (2020). Control System Performance Test for Temperature
Control in Fuzzy Logic Based Grain Dryers. Scientific Journal of Agricultural and
Biosystem Engineering, 8(1), 33–39. https://doi.org/10.29303/jrpb.v8i1.161
Sary, R. (2017). Experimental study of coffee bean drying using a forced convection
system. POLYMACHINE Journal, 14(2), 13. https://doi.org/10.30811/jpl.v14i2.337
Satria, D., Listijorini, E., & Nurghodan, M. R. (2015). Design of Temperature Control
System on Hybrid Drying Machine using Fuzzy Logic Method. Scientific Journal
of Agricultural and Biosystem Engineering, 3(2).
Soolany, C., & Aji, D. O. P. (2022). Design of a Coffee Bean Dryer Using Energy from
Agricultural Biomass Waste to Improve the Quality of Coffee Beans. AME
(Applications of Mechanics and Energy): Scientific Journal of Mechanical
Engineering, 8(1), 29. https://doi.org/10.32832/ame.v8i1.5395
Thomas Edvan, B., Edison, R., & Same, M. (2016). Effect of Temperature and Roasting
Time on. Journal of Plantation Agro Industry, 4(1), 31–40.
