APPLICATION OF MAMDANI FUZZY LOGIC IN REFRIGERATOR SELECTION
Abstract
Refrigerators are essential household appliances that preserve food freshness and optimize storage efficiency. Selecting a refrigerator requires careful consideration of factors such as price, capacity, and electricity consumption. This research applies the Mamdani-type Fuzzy Inference System (FIS) to recommend refrigerators based on these three criteria. Using a dataset of 82 refrigerator brands, this study implements fuzzification, rule formation, inference, and defuzzification, supported by MATLAB software. The results indicate that refrigerators with a normal price, medium capacity, and low power consumption are the most suitable choices. Based on the dataset, the Aqua AQR-415IM model meets these criteria. While this study confirms the effectiveness of fuzzy logic in structured decision-making, it does not quantitatively measure efficiency. Future research should explore alternative fuzzy logic methods, incorporate additional input variables, and consider demographic factors to enhance recommendation accuracy. Additionally, the Mamdani method can be adapted for broader applications in selecting other electronic products, contributing to both practical consumer guidance and theoretical advancements in fuzzy logic-based decision support systems.
Downloads
References
C. James, B. A. Onarinde, and S. J. James, “THE USE AND PERFORMANCE OF HOUSEHOLD REFRIGERATORS: A REVIEW,” Compr. Rev. Food Sci. Food Saf., vol. 16, no. 1, pp. 160–179, 2017, https://doi.org/10.1111/1541-4337.12242.
M. C. A. Remington, “The Effect of Freezing and Refrigeration on Food Quality,” Clemson University, 2017. [Online]. Available: https://tigerprints.clemson.edu/all_theses/2667
F. van Holsteijn and R. Kemna, “MINIMIZING FOOD WASTE BY IMPROVING STORAGE CONDITIONS IN HOUSEHOLD REFRIGERATION,” Resour. Conserv. Recycl., vol. 128, no. September 2017, pp. 25–31, 2018, https://doi.org/10.1016/j.resconrec.2017.09.012.
K. N. Widell, P. R. Dhakal, M. Thakur, and A. Hafner, “REFRIGERATION TO PREVENT FOOD LOSSES,” Refrig. Sci. Technol., vol. 2019-Augus, no. August, pp. 2956–2963, 2019, https://doi.org/10.18462/iir.icr.2019.0424.
F. S. Javadi and R. Saidur, “Thermodynamic And Energy Efficiency Analysis Of A Domestic Refrigerator Using Al2o3 Nano-Refrigerant,” Sustain., vol. 13, no. 10, 2021, https://doi.org/10.3390/su13105659.
G. Martinho, P. J. Castro, P. Santos, A. Alves, J. M. M. Araújo, and A. B. Pereiro, “ENVIRONMENTAL BEHAVIOURS AND RISK PERCEPTION OF DOMESTIC CONSUMERS: REFRIGERATION EQUIPMENT CASE STUDY,” Clean. Prod. Lett., vol. 3, no. June, 2022, https://doi.org/10.1016/j.clpl.2022.100024.
Y. T. Chen, “THE FACTORS AFFECTING ELECTRICITY CONSUMPTION AND THE CONSUMPTION CHARACTERISTICS IN THE RESIDENTIAL SECTOR—A CASE EXAMPLE OF TAIWAN,” Sustain., vol. 9, no. 8, 2017, https://doi.org/10.3390/su9081484.
Z. Lv and X. Zhang, “INFLUENCING FACTOR ANALYSIS ON ENERGY-SAVING REFRIGERATOR PURCHASES FROM THE SUPPLY AND DEMAND SIDES,” Sustain., vol. 15, no. 13, pp. 1–16, 2023, https://doi.org/ 10.3390/su15139917.
S. S. Bhatti, A. Kumar, R. R, and R. Singh, “ENVIRONMENT-FRIENDLY REFRIGERANTS FOR SUSTAINABLE REFRIGERATION AND AIR CONDITIONING: A REVIEW,” Curr. World Environ., vol. 18, no. 3, pp. 933–947, 2024, https://doi.org/: 10.12944/cwe.18.3.03.
J. Schleich, C. Faure, M. C. Guetlein, and G. Tu, “CONVEYANCE, ENVY, AND HOMEOWNER CHOICE OF APPLIANCES,” Energy Econ., vol. 89, p. 104816, 2020, https://doi.org/10.1016/j.eneco.2020.104816.
B. Wang, N. Deng, X. Liu, Q. Sun, and Z. Wang, “EFFECT OF ENERGY EFFICIENCY LABELS ON HOUSEHOLD APPLIANCE CHOICE IN CHINA: SUSTAINABLE CONSUMPTION OR IRRATIONAL INTERTEMPORAL CHOICE?,” Resour. Conserv. Recycl., vol. 169, no. January, p. 105458, 2021, https://doi.org/10.1016/j.resconrec.2021.105458.
D. Ray and S. Roy Choudhury, “FACTORS AFFECTING CONSUMER DECISION MAKING FOR PURCHASING SELECTED HOME APPLIANCE PRODUCTS BASED ON MARKET SEGMENTATION-A FEEDBACK STUDY OF PEOPLE ASSOCIATED WITH MANAGEMENT EDUCATION,” Quest Journals J. Res. Bus. Manag., vol. 3, no. 2, pp. 6–11, 2015, [Online]. Available: www.questjournals.org
E. O. Stober, “THE PSYCHOLOGY OF CONSUMERS AND THE BEHAVIOUR ANALYSIS: A CASE OF ROMANIAN REFRIGERATOR MARKET,” East. Eur. Bus. Econ. J., vol. 2, no. 1, pp. 75–94, 2016.
K. Sivakumar, “FACTORS INFLUENCING THE PURCHASE DECISION OF ORGANIC TOFU,” Interal Res journa Manag. Sci Tech, vol. 6, no. 1, pp. 2348 – 9367, 2015, https://doi.org/10.32804/IRJMSt.
J. Lu, G. Ma, and G. Zhang, “FUZZY MACHINE LEARNING: A COMPREHENSIVE FRAMEWORK AND SYSTEMATIC REVIEW,” IEEE Trans. Fuzzy Syst., vol. 32, no. 7, pp. 3861–3878, 2024, https://doi.org/10.1109/TFUZZ.2024.3387429.
O. Surucu, S. A. Gadsden, and J. Yawney, “CONDITION MONITORING USING MACHINE LEARNING: A REVIEW OF THEORY, APPLICATIONS, AND RECENT ADVANCES,” Expert Syst. Appl., vol. 221, no. October 2021, p. 119738, 2023, https://doi.org/10.1016/j.eswa.2023.119738.
H. Wu and Z. S. Xu, “FUZZY LOGIC IN DECISION SUPPORT: METHODS, APPLICATIONS AND FUTURE TRENDS,” Int. J. Comput. Commun. Control, vol. 16, no. 1, pp. 1–28, 2021, https://doi.org/10.15837/ijccc.2021.1.4044.
N. Hendiyani, B. Suseta, Y. Kurniasari, and A. M. Abadi, “THE IMPLEMENTATION OF MAMDANI FUZZY INFERENCE SYSTEM (FIS) METHOD FOR DECISION MAKING TO CHOOSE DIRECT AND TRANSIT AIRLINE TYPES IN INDONESIA,” J. Phys. Conf. Ser., vol. 1581, no. 1, 2020, https://doi.org/10.1088/1742-6596/1581/1/012011.
D. Mulyadi, “THE IMPLEMENTATION OF LOGIC FUZZY MAMDANI METHOD AS THE DECISION SUPPORT ON THE GRADUAL SELECTION OF NEW STUDENTS,” Manag. J. Binaniaga, vol. 3, no. 02, p. 23, 2018, https://doi.org/10.33062/mjb.v3i2.256.
E. F. Ma’rif and A. M. Abadi, “FUZZY APPLICATION (MAMDANI METHOD) IN DECISION-MAKING ON LED TV SELECTION,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 2, pp. 1117–1128, 2024, https://doi.org/10.30598/barekengvol18iss2pp1117-1128.
P. An, “FUZZY DECISION SUPPORT SYSTEMS TO IMPROVE THE EFFECTIVENESS OF TRAINING PROGRAMS IN THE FIELD OF SPORTS FITNESS,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, 2024, https://doi.org/10.1007/s44196-024-00555-z.
R. Rustum et al., “SUSTAINABILITY RANKING OF DESALINATION PLANTS USING MAMDANI FUZZY LOGIC INFERENCE SYSTEMS,” Sustain., vol. 12, no. 2, 2020, https://doi.org/10.3390/su12020631.
J. M. Belman-Flores, D. A. Rodríguez-Valderrama, S. Ledesma, J. J. García-Pabón, D. Hernández, and D. M. Pardo-Cely, “A REVIEW ON APPLICATIONS OF FUZZY LOGIC CONTROL FOR REFRIGERATION SYSTEMS,” Appl. Sci., vol. 12, no. 3, 2022, https://doi.org/10.3390/app12031302.
T. M. Tuan et al., “M-CFIS-R: MAMDANI COMPLEX FUZZY INFERENCE SYSTEM WITH RULE REDUCTION USING COMPLEX FUZZY MEASURES IN GRANULAR COMPUTING,” Mathematics, vol. 8, no. 5, 2020, https://doi.org/10.3390/MATH8050707.
H. Humaira, R. Rasyidah, and I. Rahmayuni, “DESIGNING MAMDANI FUZZY INFERENCE SYSTEMS FOR DECISION SUPPORT SYSTEMS,” Proc. ICAITI 2019 - 2nd Int. Conf. Appl. Inf. Technol. Innov. Explor. Futur. Technol. Appl. Inf. Technol. Innov., no. March 2021, pp. 111–115, 2019, https://doi.org/10.1109/ICAITI48442.2019.8982153.
Y. F. Hernández-Julio, M. J. Prieto-Guevara, W. Nieto-Bernal, I. Meriño-Fuentes, and A. Guerrero-Avendaño, “FRAMEWORK FOR THE DEVELOPMENT OF DATA-DRIVEN MAMDANI-TYPE FUZZY CLINICAL DECISION SUPPORT SYSTEMS,” Diagnostics, vol. 9, no. 2, 2019, https://doi.org/10.3390/diagnostics9020052.
J. Cao et al., “FUZZY INFERENCE SYSTEM WITH INTERPRETABLE FUZZY RULES: ADVANCING EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR DISEASE DIAGNOSIS—A COMPREHENSIVE REVIEW,” Inf. Sci. (Ny)., vol. 662, no. January, p. 120212, 2024, https://doi.org/10.1016/j.ins.2024.120212.
M. H. Eghbal Ahmadi, S. J. Royaee, S. Tayyebi, and R. Bozorgmehry Boozarjomehry, “A NEW INSIGHT INTO IMPLEMENTING MAMDANI FUZZY INFERENCE SYSTEM FOR DYNAMIC PROCESS MODELING: APPLICATION ON FLASH SEPARATOR FUZZY DYNAMIC MODELING,” Eng. Appl. Artif. Intell., vol. 90, p. 103485, 2020, https://doi.org/https://doi.org/10.1016/j.engappai.2020.103485.
J. Palanichamy, S. Palani, G. Anita Hebsiba, J. Viola, A. Tungsrimvong, and B. Babu, “SIMULATION AND PREDICTION OF GROUNDWATER QUALITY OF A SEMI-ARID REGION USING FUZZY INFERENCE SYSTEM AND NEURAL NETWORK TECHNIQUES,” J. Soft Comput. Civ. Eng., vol. 6, no. 1, pp. 110–126, 2022, https://doi.org/10.22115/SCCE.2022.285106.1314.
A. Sadollah, “INTRODUCTORY CHAPTER: WHICH MEMBERSHIP FUNCTION IS APPROPRIATE IN FUZZY SYSTEM?,” in Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications, A. Sadollah, Ed., Rijeka: IntechOpen, 2018. https://doi.org/10.5772/intechopen.79552.
V. C. Madanda, F. Sengani, and F. Mulenga, “APPLICATIONS OF FUZZY THEORY-BASED APPROACHES IN TUNNELLING GEOMECHANICS: A STATE-OF-THE-ART REVIEW,” Mining, Metall. Explor., vol. 40, no. 3, pp. 819–837, 2023, https://doi.org/10.1007/s42461-023-00767-5.
R. Saatchi, “FUZZY LOGIC CONCEPTS, DEVELOPMENTS AND IMPLEMENTATION,” Inf., vol. 15, no. 10, 2024, https://doi.org/10.3390/info15100656.
B. Cuka and D.-W. Kim, “FUZZY LOGIC BASED TOOL CONDITION MONITORING FOR END-MILLING,” Robot. Comput. Integr. Manuf., vol. 47, pp. 22–36, 2017, https://doi.org/https://doi.org/10.1016/j.rcim.2016.12.009.
A. El hamdaouy, I. Salhi, A. Belattar, and S. Doubabi, “TAKAGI–SUGENO FUZZY MODELING FOR THREE-PHASE MICRO HYDROPOWER PLANT PROTOTYPE,” Int. J. Hydrogen Energy, vol. 42, no. 28, pp. 17782–17792, 2017, https://doi.org/https://doi.org/10.1016/j.ijhydene.2017.02.167.
H. Bizimana and A. Altunkaynak, “MODELING THE INITIATION OF SEDIMENT MOTION UNDER A WIDE RANGE OF FLOW CONDITIONS USING A GENO-MAMDANI FUZZY INFERENCE SYSTEM METHOD,” Int. J. Sediment Res., vol. 35, no. 5, pp. 467–483, 2020, https://doi.org/https://doi.org/10.1016/j.ijsrc.2020.03.009.
F. Topaloğlu and H. Pehlıvan, “COMPARISON OF MAMDANI TYPE AND SUGENO TYPE FUZZY INFERENCE SYSTEMS IN WIND POWER PLANT INSTALLATIONS,” in 2018 6th International Symposium on Digital Forensic and Security (ISDFS), 2018, pp. 1–4. https://doi.org/10.1109/ISDFS.2018.8355384.
N. A. Dewanti and A. M. Abadi, “FUZZY LOGIC APPLICATION AS A TOOL FOR CLASSIFYING WATER QUALITY STATUS IN GAJAHWONG RIVER, YOGYAKARTA, INDONESIA,” IOP Conf. Ser. Mater. Sci. Eng., vol. 546, no. 3, 2019, https://doi.org/10.1088/1757-899X/546/3/032005.
S. Rizvi, J. Mitchell, A. Razaque, M. R. Rizvi, and I. Williams, “A FUZZY INFERENCE SYSTEM (FIS) TO EVALUATE THE SECURITY READINESS OF CLOUD SERVICE PROVIDERS,” J. Cloud Comput., vol. 9, no. 1, 2020, https://doi.org/10.1186/s13677-020-00192-9.
R. Isaac Sajan and V. B. Christopher, “A FUZZY INFERENCE SYSTEM FOR ENHANCED GROUNDWATER QUALITY ASSESSMENT AND INDEX DETERMINATION,” Water Qual. Res. J., vol. 58, no. 3, pp. 230–246, 2023, https://doi.org/10.2166/wqrj.2023.031.
A. Ebrahimnejad and J. L. Verdegay, “FUZZY SET THEORY BT - FUZZY SETS-BASED METHODS AND TECHNIQUES FOR MODERN ANALYTICS,” A. Ebrahimnejad and J. L. Verdegay, Eds., Cham: Springer International Publishing, 2018, pp. 1–27. https://doi.org/10.1007/978-3-319-73903-8_1.
S. Chakraverty, D. M. Sahoo, and N. R. Mahato, “DEFUZZIFICATION BT - CONCEPTS OF SOFT COMPUTING: FUZZY AND ANN WITH PROGRAMMING,” S. Chakraverty, D. M. Sahoo, and N. R. Mahato, Eds., Singapore: Springer Singapore, 2019, pp. 117–127. https://doi.org/10.1007/978-981-13-7430-2_7.
N. L. I. T. Agustina and A. M. Abadi, “FUZZY LOGIC APPLICATION FOR DETERMINING THE FEASIBILITY OF NICKEL MINING IN SOUTHEAST SULAWESI PROVINCE,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 2, pp. 1135–1146, 2024, https://doi.org/10.30598/barekengvol18iss2pp1135-1146.
MathWorks, “MATLAB R2019b.” The MathWorks, Inc., Natick, MA, USA, 2019. Used under an academic license at Universitas Negeri Yogyakarta, Dec. 12, 2024.
Copyright (c) 2025 Anisa Dwi Oktarina, Agus Maman Abadi, Syukrul Hamdi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
