Application of Tsukamoto Fuzzy Logic in Expert System Application for Diagnosing Web-Based Skin Diseases

Nurwijayanti KN, Rasna Rasna, Rima Ruktiari Ismail, Agus Sugiharto, Fifto Nugroho

Abstract


Skin health is essential for everyone. In addition to supporting someone who can reduce self-confidence, skin diseases can also interfere with a person's concentration in activities. An expert system is a system designed to be able to imitate the expertise of an expert in answering questions and solving a problem. The expert system will solve a problem obtained from a dialogue with the user. With the help of an expert system, someone who is not an expert can answer questions, solve problems, and make decisions that an expert usually makes. This needs to be anticipated and handled seriously, especially for types of skin diseases, some of which can be fatal, and some can even be classified as cancer. Experts are needed to diagnose each disease in this case, but consultation with experts requires costly funds. For this reason, this system is designed to help people diagnose skin diseases online, making it easier for sufferers to diagnose the diseases they suffer from by themselves. The method used is the fuzzy Tsukamoto method. Analysis of the introduction of the disease is carried out by identifying various symptoms of the disease. The types of diseases diagnosed include tinea versicolor [P001], scabies [P002], ringworm [P003], dandruff [P004], vitiligo [P005], pityriasis alba [P006], hives [P007], erythema multiforme [P008], acne [P009], keloids [P010], melanoma [P011], eczema [P012], boils [P013], measles [P014], psoriasis [P015], impetigo [P016], and herpes [P017]. Skin disease sufferers can diagnose their disease without consulting with a specialist directly. This system can be used as a substitute for a specialist in producing a diagnosis in the form of the name of the disease suffered by the system user (user). This system provides a solution for users regarding more economical disease diagnosis.


Keywords


Diagnosis, Disease, Fuzzy Tsukamoto, Expert System, Symptoms

Full Text:

PDF

References


A. Agusta, F. Y. Arini, and R. Arifudin, “Implementation of Fuzzy Logic Method and Certainty Factor for Diagnosis Expert System of Chronic Kidney Disease,” J. Adv. Inf. Syst. Technol., vol. 2, no. 1, pp. 61–68, Apr. 2020, doi: 10.15294/JAIST.V2I1.44369.

K. N. Meiah Ngafidin, S. Suryono, and R. R. Isnanto, “Diagnosis of Tuberculosis by Using a Fuzzy Logic Expert System,” Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, Oct. 2019, doi: 10.1109/ICIC47613.2019.8985788.

M. S. Ibrahim and D. W. Al-Dulaimee, “Design Multimedia Expert Diagnosing Diseases System Using Fuzzy Logic (MEDDSFL),” Mar. 2020.

D. Aldo, A. R. Wijaya, A. Utami, Y. S. R. Nur, and N. Putra, “Expert System for Identification of Early Illness Due to Cellulitis Complications with the Case Based Reasoning Method,” Proc. - 2023 Int. Conf. Networking, Electr. Eng. Comput. Sci. Technol. IConNECT 2023, pp. 1–6, 2023, doi: 10.1109/ICONNECT56593.2023.10326946.

R. Boadh et al., “Study of fuzzy expert system for the diagnosis of various types of cancer,” Mater. Today Proc., vol. 56, pp. 298–307, Jan. 2022, doi: 10.1016/J.MATPR.2022.01.161.

W. Febriani, G. W. Nurcahyo, and S. Sumijan, “Diagnosa Penyakit Rubella Menggunakan Metode Fuzzy Tsukamoto,” J. Sistim Inf. dan Teknol., vol. 1, no. 3, pp. 12–17, Sep. 2019, doi: 10.35134/JSISFOTEK.V1I3.4.

R. R. Isnanto, Mustafid, A. B. Prasetijo, and S. I. Fitria Ali, “Expert System for Diagnosis of Diphtheria Disease Using Fuzzy Logic,” 6th Int. Semin. Res. Inf. Technol. Intell. Syst. ISRITI 2023 - Proceeding, pp. 397–402, 2023, doi: 10.1109/ISRITI60336.2023.10467389.

M. Natsir and B. Waseso, “Analysis And Design Of Decision Making Information System For Diagnosis Of Blood Fever Using Fuzzy Tsukamoto Method Application Diagnosis Of Blood Fever,” IJCSIS) Int. J. Comput. Sci. Inf. Secur., vol. 17, no. 10, 2019.

J. Kaur, B. S. Khehra, and A. Singh, “Significance of Fuzzy Logic in the Medical Science,” pp. 497–509, 2022, doi: 10.1007/978-981-16-8225-4_38.

C. A. Sari, W. S. Sari, and A. D. Krismawan, “Expert System for Diagnosing Potential Diabetes Attacks Using the Fuzzy Tsukamoto,” J. Appl. Intell. Syst., vol. 7, no. 2, pp. 146–161, Sep. 2022, doi: 10.33633/JAIS.V7I2.6796.

M. Fiqran, “Expert System for Diagnosing Torch Disease in Pregnant Women with Certainty Factor and Fuzzy Logic Methods,” J. Comput. Scine Inf. Technol., pp. 33–39, Apr. 2022, doi: 10.35134/JCSITECH.V8I2.32.

M. Y. T. Irsan, M. I. Kasau, and I. P. Simbolon, “Penggunaan Fuzzy Logic & Metode Mamdani untuk Menghitung Pembelian, Penjualan dan Persediaan,” JAAF (Journal Appl. Account. Financ., vol. 3, no. 1, p. 37, May 2019, doi: 10.33021/JAAF.V3I1.677.

J. F. Panggabean and A. Manik, “Expert System For Diagnosis Of Vitiligo Disease Using The Fuzzy Mamdani Method Based On Web-Based,” J. ICT Inf. Commun., vol. 12, no. 1, 2021.

G. Kipngetich, “Design of a Non-invasive IOT-Based system for prediction and early detection of type 2diabetes using Fuzzy logic,” Dec. 2022.

M. A. Ali Raza, M. S. Liaqat, and M. Shoaib, “A Fuzzy Expert System Design for Diagnosis of Skin Diseases,” 2019 2nd Int. Conf. Adv. Comput. Sci. ICACS 2019, Apr. 2019, doi: 10.23919/ICACS.2019.8689140.

B. A. Akinnuwesi, B. A. Adegbite, F. Adelowo, U. Ima-Edomwonyi, G. Fashoto, and O. T. Amumeji, “Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique,” Informatics Med. Unlocked, vol. 18, p. 100279, Jan. 2020, doi: 10.1016/J.IMU.2019.100279.

C. Kavalc?o?lu, “A Fuzzy Logic Implemented Classification Indicator for the Diagnosis of Diabetes Mellitus in TRNC,” Stud. Comput. Intell., vol. 1115, pp. 101–114, 2023, doi: 10.1007/978-3-031-42924-8_8.

M. Casal-Guisande, A. Comesaña-Campos, J. Cerqueiro-Pequeño, and J. B. Bouza-Rodríguez, “Design and Development of a Methodology Based on Expert Systems, Applied to the Treatment of Pressure Ulcers,” Diagnostics 2020, Vol. 10, Page 614, vol. 10, no. 9, p. 614, Aug. 2020, doi: 10.3390/DIAGNOSTICS10090614.

D. S. B. Ginting, F. Y. Manik, R. Arrahmi, M. A. A. R. Saragih, M. D. Arbani Asfi Dalimunthe, and M. Iqbal Aldeena, “Performance of Fuzzy Tsukamoto and Fuzzy Sugeno Methods in Predicting Types of Neurotic Disorder,” Proceeding - ELTICOM 2023 7th Int. Conf. Electr. Telecommun. Comput. Eng. Sustain. Resilient Communities with Smart Technol., pp. 194–199, 2023, doi: 10.1109/ELTICOM61905.2023.10443173.

D. Santra, S. K. Basu, J. K. Mandal, and S. Goswami, “Lattice-Based Fuzzy Medical Expert System for Management of Low Back Pain: A Preliminary Design,” Adv. Intell. Syst. Comput., vol. 1157, pp. 119–129, 2020, doi: 10.1007/978-981-15-4288-6_8.




DOI: https://doi.org/10.52088/ijesty.v5i2.827

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Nurwijayanti KN, Rasna Rasna, Rima Ruktiari Ismail, Agus Sugiharto, Fifto Nugroho

International Journal of Engineering, Science and Information Technology (IJESTY) eISSN 2775-2674