Comparison of three fuzzy logic algorithm methods for cellular selection
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Published:
Aug 24, 2023
Keywords:
Cellular , Fuzzy Logic , Fuzzy Mamdani , Fuzzy Sugeno , Fuzzy Tsukamoto
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Abstract
Many cell phone types are on the market today, increasingly making users feel confused and confused about choosing the cell that suits their needs. As one of the most essential needs at this time, users must be able to match their cellular needs with their income. Many smartphone products are offered. To help users in this study using three methods from the Fuzzy Logic algorithm for Decision Support Systems in choosing cellular according to their needs and desires; from the research that has been done, it is found that using the Fuzzy Tsukamoto method the accuracy is better than Mamdani which is equal to 0.02135, Mamdani is as large as 0.0643, while Sugeno is 0.1007. The cellular chosen is the Samsung A73 brand.
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How to Cite
Gunawan, G., Andriani, W., & Anandianskha, S. (2023). Comparison of three fuzzy logic algorithm methods for cellular selection. Journal of Intelligent Decision Support System (IDSS), 6(3), 129-137. https://doi.org/10.35335/idss.v6i3.154
References
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Zaaidatunni, U., Dewi, M. T., & Hakim, M. F. Al. (2021). Implementation of fuzzy tsukamoto in employee performance assessment. Journal of Soft Computing Exploration, 2(2). https://doi.org/10.52465/joscex.v2i2.52
Bala, B. P. (2020). Significant of Smartphone: An Educational Technology Tool for Teaching and Learning. International Journal of Innovative Science and Research Technology, 5(5), 1634–1638.
Burhanuddin, A. (2023). Analisis Komparatif Inferensi Fuzzy Tsukamoto, mamdani dan Sugeno Terhadap Produktivitas Padi di Indonesia. LEDGER: Journal Informatic and Information Technology, 2(1), 48–57.
Castillo, O., & Melin, P. (2021). A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics. Chaos, Solitons and Fractals, 151, 111250. https://doi.org/10.1016/j.chaos.2021.111250
Darmawan, M. D. (2018). The effect of price, product quality, promotion, social factor, brand image on purchase decision process of loop product on youth segment (Case Study of PT Telekomunikasi Selular). International Seminar & Conference on Learning Organization ISCLO, 6, 1–16.
Davvaz, B., Mukhlash, I., & Soleha, S. (2021). Himpunan Fuzzy dan Rough Sets. Limits: Journal of Mathematics and Its Applications, 18(1), 79. https://doi.org/10.12962/limits.v18i1.7705
Jane, J. B., & Ganesh, E. N. (2019). A review on big data with machine learning and fuzzy logic for better decision making. International Journal of Scientific and Technology Research, 8(10), 1221–1225.
Kahar, N., & Riki. (2021). Comparative Study of SMART and FMCDM Methods in Smartphone Selection Decision Support System. International Journal of Image, Graphics and Signal Processing, 13(4), 1–13. https://doi.org/10.5815/ijigsp.2021.04.01
Karyaningsih, D., & Rizky, R. (2020). Implementation of Fuzzy Mamdani Method for Traffic Lights Smart City in Rangkasbitung, Lebak Regency, Banten Province (Case Study of the Traffic Light T-junction, Cibadak, By Pas Sukarno Hatta Street). Jurnal KomtekInfo, 7(3), 176–185. https://doi.org/10.35134/komtekinfo.v7i3.78
Lunde, P., Nilsson, B. B., Bergland, A., Kværner, K. J., & Bye, A. (2018). The effectiveness of smartphone apps for lifestyle improvement in noncommunicable diseases: Systematic review and meta-analyses. Journal of Medical Internet Research, 20(5), 1–12. https://doi.org/10.2196/jmir.9751
Milošević, T., Pamučar, D., & Chatterjee, P. (2021). Model for selecting a route for the transport of hazardous materials using a fuzzy logic system. Vojnotehnicki Glasnik, 69(2), 355–390. https://doi.org/10.5937/vojtehg69-29629
Nugraha, E., Wibawa, A. P., Hakim, M. L., Kholifah, U., Dini, R. H., & Irwanto, M. R. (2019). Implementation of fuzzy tsukamoto method in decision support system of journal acceptance. Journal of Physics: Conference Series, 1280(2). https://doi.org/10.1088/1742-6596/1280/2/022031
Pavement, P., Index, C., Fuzzy, U., & Technique, L. (2022). Logic Technique. 1–15.
Qafzezi, E., Bylykbashi, K., Ikeda, M., Matsuo, K., & Barolli, L. (2020). Coordination and management of cloud, fog and edge resources in SDN-VANETs using fuzzy logic: A comparison study for two fuzzy-based systems. Internet of Things (Netherlands), 11, 100169. https://doi.org/10.1016/j.iot.2020.100169
Rajesh Mavani, N., Lim, C. Y., Hashim, H., Abd. Rahman, N., & Mohd Ali, J. (2021). Fuzzy Mamdani based user-friendly interface for food preservatives determination. Food and Bioproducts Processing, 126, 282–292. https://doi.org/10.1016/j.fbp.2021.01.012
Rohimah, L. (2019). Prediksi Nilai Ekspor Sepatu Kulit HS 6403 ke Jepang dengan Metode Mamdani, Sugeno dan Tsukamoto. Jurnal Ilmu Pengetahuan Dan Teknologi Komputer, 4(Februari), 239–244.
Seno, N. I., Salman, M. A. W., & Farhan, R. N. (2022). A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image. Bulletin of Electrical Engineering and Informatics, 11(3), 1624–1632. https://doi.org/10.11591/eei.v11i3.3561
Silva, S. N., Lopes, F. F., Valderrama, C., & Fernandes, M. A. C. (2020). Proposal of Takagi–Sugeno fuzzy-PI controller hardware. Sensors (Switzerland), 20(7), 1–28. https://doi.org/10.3390/s20071996
Wahyuni, D., Sumarminingsih, E., & Astutik, S. (2022). COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification. JTAM (Jurnal Teori Dan Aplikasi Matematika), 6(4), 937. https://doi.org/10.31764/jtam.v6i4.10096
Yudono, M. A. S., Faris, R. M., De Wibowo, A., Sidik, M., Sembiring, F., & Aji, S. F. (2022). Fuzzy Decision Support System for ABC University Student Admission Selection. Proceedings of the International Conference on Economics, Management and Accounting (ICEMAC 2021), 207(Icemac 2021), 230–237. https://doi.org/10.2991/aebmr.k.220204.024
Yunan, A., & Ali, M. (2020). Study and Implementation of the Fuzzy Mamdani and Sugeno Methods in Decision Making on Selection of Outstanding Students at the South Aceh Polytechnic. Jurnal Inotera, 5(2), 152–164. https://doi.org/10.31572/inotera.vol5.iss2.2020.id127
Zaaidatunni, U., Dewi, M. T., & Hakim, M. F. Al. (2021). Implementation of fuzzy tsukamoto in employee performance assessment. Journal of Soft Computing Exploration, 2(2). https://doi.org/10.52465/joscex.v2i2.52
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