Design and Development of Coffee Machine Control System Using Fuzzy Logic

Authors

  • Eko Hadianto Universitas Pradita, Serpong, Tangerang, Indonesia
  • Djaja Amanda Universitas Pradita, Serpong, Tangerang, Indonesia
  • Djarot Hindarto Universitas Pradita, Serpong, Tangerang, Indonesia
  • Amelia Makmur Universitas Pradita, Serpong, Tangerang, Indonesia
  • Handri Santoso Universitas Pradita, Serpong, Tangerang, Indonesia

DOI:

10.33395/sinkron.v8i1.11917

Keywords:

Coffee Machine, Fuzzy Logic, Inference Method, Fuzzy Inference, Fuzzy Takagi Sugeno Kang Method

Abstract

The food and beverage industry is currently rife in urban and outside cities. Many locations are used as places to sell drinks, especially coffee which is a native plant of Indonesia. Nowadays, coffee compounding requires good technology. There are many coffee processing machines on the market. The coffee machine is capable of making expresso coffee, latte coffee and others. This coffee machine also combines coffee ingredients, sugar and milk as a carrier for a delicious aroma. In addition, the water pressure from the coffee machine heating boiler, the strong pressure of the coffee machine piston also affects the results of making a cup of coffee, the stronger the pressure, the thicker the coffee produced and the slower the flow of water in the coffee machine. glass of water because basically the stronger the pressure applied to it. the coffee grounds, the tighter and tighter the gaps that the water itself will pass, as well as the thickness of the resulting coffee water will be more concentrated. With Fuzzy Inference, it is possible to determine the optimal pressure to be exerted by the coffee machine piston based on the weight of the coffee grounds (grams) on the coffee machine piston and the specifications of the type of coffee machine used. Determining the optimal pressure on the coffee grounds will affect the taste of the coffee water produced and the speed of making a cup of coffee. This study uses the optimal pressure on the piston using the fuzzy inference method. The purpose of this research is to create a simulation for evaluating the performance of a coffee machine using fuzzy logic to solve the problem of damage to the piston. The fuzzy approach in this research uses the fuzzy Takagi Sugeno Kang method.

GS Cited Analysis

Downloads

Download data is not yet available.

References

Barella, S., Gruttadauria, A., Mapelli, C., & Mombelli, D. (2013). Anomalous corrosion phenomena observed on electrovalves of coffee espresso machines. Engineering Failure Analysis, 33, 449–456. https://doi.org/10.1016/j.engfailanal.2013.06.025

Batubara, S. (2017). Analisis Perbandingan Metode Fuzzy Mamdani Dan Fuzzy Sugeno Untuk Penentuan Kualitas Cor Beton Instan. It Journal Research and Development, 2(1), 1–11. https://doi.org/10.25299/itjrd.2017.vol2(1).644

Cazarez-Castro, N. R., Aguilar, L. T., & Castillo, O. (2010). Fuzzy logic control with genetic membership function parameters optimization for the output regulation of a servomechanism with nonlinear backlash. Expert Systems with Applications, 37(6), 4368–4378. https://doi.org/10.1016/j.eswa.2009.11.091

Griffis, J. C., Metcalf, N. V., Corbetta, M., & Shulman, G. L. (2021). Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions. NeuroImage: Clinical, 30, 102639. https://doi.org/10.1016/j.nicl.2021.102639

Hasan, N., Mishra, A., & Ray, A. K. (2022). Fuzzy logic based cross-layer design to improve Quality of Service in Mobile ad-hoc networks for Next-gen Cyber Physical System. Engineering Science and Technology, an International Journal, 35, 101099. https://doi.org/10.1016/j.jestch.2022.101099

Hindarto, D., & Santoso, H. (2019). Plat Nomor Kendaraan Dengan Metode Convolutional Neural Network. Jurnal Inovasi Informatika Universitas Pradita, September 2021, 1–12.

Hindarto, D., & Santoso, H. (2022). PERFORMANCE COMPARISON OF SUPERVISED LEARNING USING NON-NEURAL NETWORK AND NEURAL NETWORK. Janapati, 11, 49–62.

Jeong, S. K., Han, C. H., Hua, L., & Wibowo, W. K. (2018). Systematic design of membership functions for fuzzy logic control of variable speed refrigeration system. Applied Thermal Engineering, 142(June), 303–310. https://doi.org/10.1016/j.applthermaleng.2018.06.082

Kusumadewi, S. (2006). Fuzzy Multi Attribute Decision Making. Graha Ilmu.

Luthfia Rohimah, Sinta Rukiastiandari, J. S. (2022). Penerapan Logika Fuzzy Metode Sugeno Untuk Optimalisasi Nilai Ekspor Ikan Tuna Hs 160414 Ke Italia. Jurnal Teknik Komputer AMIK BSI, 8(2), 174–180. https://doi.org/10.31294/jtk.v4i2

Masoumi, A. P., Tavakolpour-Saleh, A. R., & Rahideh, A. (2020). Applying a genetic-fuzzy control scheme to an active free piston Stirling engine: Design and experiment. Applied Energy, 268(January), 115045. https://doi.org/10.1016/j.apenergy.2020.115045

Mudia, H. (2020). Comparative Study of Mamdani-type and Sugeno-type Fuzzy Inference Systems for Coupled Water Tank. Indonesian Journal of Artificial Intelligence and Data Mining, 3(1), 42. https://doi.org/10.24014/ijaidm.v3i1.9309

Mukminna, H., Putri, D. M., & Handayani, A. N. (2017). Simulasi Kinerja Siswa Dengan Metode Fuzzy Inference Sugeno Menggunakan Aplikasi Matlab. Jurnal Ilmiah Teknologi Informasi Asia, 11(1), 71. https://doi.org/10.32815/jitika.v11i1.53

Pandey, A., & Parhi, D. R. (2014). MATLAB Simulation for Mobile Robot Navigation with Hurdles in Cluttered Environment Using Minimum Rule based Fuzzy Logic Controller. Procedia Technology, 14, 28–34. https://doi.org/10.1016/j.protcy.2014.08.005

Setiawan, A., Yanto, B., & Yasdomi, K. (2018). Logika Fuzzy Dengan Matlab (Contoh Kasus Penelitian Penyakit Bayi dengan Fuzzy Tsukamoto). In Jayapangus Press Books (Issue July).

Shakouri G., H., & Nadimi, R. (2013). Outlier detection in fuzzy linear regression with crisp input-output by linguistic variable view. Applied Soft Computing Journal, 13(1), 734–742. https://doi.org/10.1016/j.asoc.2012.07.001

Sze, E., Hindarto, D., & Wirayasa, I. K. A. (2022). Performance Comparison of Ultrasonic Sensor Accuracy in Measuring Distance. 7(4), 2556–2562.

Sze, E., Santoso, H., & Hindarto, D. (2022). Review Star Hotels Using Convolutional Neural Network. 7(1), 2469–2477.

Teodorescu, H. N. (2010). Generalized fuzzy logic systems and generalized defuzzification operators. IFAC Proceedings Volumes (IFAC-PapersOnline), 8(PART 1), 103–108. https://doi.org/10.3182/20100929-3-ro-4017.00019

Wang, J., Li, H., Wang, Y., & Lu, H. (2021). A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm. In Expert Systems with Applications (Vol. 168). Elsevier Ltd. https://doi.org/10.1016/j.eswa.2020.114364

YAO, T., MIAO, R., WANG, W., LI, Z., DONG, J., GU, Y., & YAN, X. (2022). Synthetic damage effect assessment through evidential reasoning approach and neural fuzzy inference: Application in ship target. Chinese Journal of Aeronautics, 35(8), 143–157. https://doi.org/10.1016/j.cja.2021.08.010

Yazid, E., Garratt, M., & Santoso, F. (2019). Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots. Applied Soft Computing Journal, 78, 373–392. https://doi.org/10.1016/j.asoc.2019.02.023

Downloads


Crossmark Updates

How to Cite

Hadianto, E., Amanda, D. ., Hindarto, D. ., Makmur, A. ., & Santoso, H. . (2023). Design and Development of Coffee Machine Control System Using Fuzzy Logic. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(1), 130-138. https://doi.org/10.33395/sinkron.v8i1.11917

Issue

Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023

Section

Articles

License

Copyright (c) 2023 Eko Hadianto, Djaja Amanda, Djarot Hindarto, Amelia Makmur, Handri Santoso

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)

1 2 3 4 5 > >>