Dental Disease Detection Using Hybrid Fuzzy Logic and Evolution Strategies

Andi Maulidinnawati Abdul Kadir Parewe, Wayan Firdaus Mahmudy, Fatwa Ramdhani, Yusuf Priyo Anggodo

Abstract


Dental disease detection is needed because majority of Indonesian have ever experienced dental disease. There are three areas affected by dental disease: South Sulawesi, West Sulawesi, and South Kalimantan according to Basic Health Research 2013. Obtaining accurate detection is difficult because it requires expert observations and interviews in order to improve their perception. Accurate dental disease detection is required by dentists as a tool to make it easier to improve patient interaction and time effeciency. Good and accurate detection requires an approach to obtain a model capable of processing observation data. This research proposes a method as solution utilizing hybrid approach employed both fuzzy logic and evolution algorithm. Evolution Strategies is used for optimization that get results better accuracy than simply using FIS Tsukamoto. Optimization focuses on the function of the degree of membership. This can be utilized to categorize the following dental disease. Variance: pulpitis, gingivitis, periodontitis and advacend periodontitis using formula Root Mean Square Error (RMSE) obtain with RMSE 0.82.

Keywords


FIS; Detection; Hybrid; Evolution Strategies;

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References


Kemenkes RI, “Riset Kesehatan Dasar (RISKESDAS),” Laporan Nasional 2013. pp. 1–268, 2013.

L. W. Santoso, R. Intan, F. Sugianto, U. K. Petra, and F. T. Industri, “Implementasi Fuzzy Expert System Untuk Analisa Penyakit Dalam Pada Manusia,” vol. 2008, no. Snati, pp. 13–18, 2008.

W. A. N. Dorland, KAMUS KEDOKTERAN DORLAND, 29th ed. Jakarta: EGC, 2002.

O. A. S. Youssef, “Applications of fuzzy inference mechanisms to power system relaying,” IEEE Power Syst. Conf. Expo., pp. 560–567, 2004.

N. Al-Hinai and T. Y. Elmekkawy, “An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem,” Flex. Serv. Manuf. J., vol. 23, no. 1, pp. 64–85, 2011.

W. J. Zhang and X. F. Xie, “DEPSO: Hybrid Particle Swarm with Differential Evolution Operators,” Proc. 2003 IEEE Int. Conf. Syst. Man Cybern., vol. 4, no. 1, pp. 3816–3821, 2003.

H. Milah and W. F. Mahmudy, “Implementasi Algoritma Evolution Strategies Untuk Optimasi Komposisi Pakan Ternak Sapi Potong,” no. 11, 2015.

A. Novruz and A. Tevfik, “A Fuzzy Expert System Design for Diagnosis of Periodontal Dental Disease,” IEEE Trans. Fuzzy Syst., vol. 7546, pp. 1–5, 2011.

A. M. A. K. Parewe and W. F. Mahmudy, “Dental Disease Identification Using Fuzzy Inference System,” J. Environ. Eng. Sustain. Technol., vol. 3, no. 1, pp. 33–41, 2016.

Y. Lin and G. A. Cunningham, “A New Approach to Fuzzy-Neural System Modeling,” IEEE Trans. Fuzzy Syst., vol. 3, no. 2, pp. 190– 198, 1995.

G. Der Wu and W. Z. Zhen, “Recurrent Fuzzy Neural Networks For Speech Decetion,” iFuzzy, pp. 18–21, 2015.

E. T. Luthfi, “Fuzzy C-Means Untuk Clustering Data ( Studi Kasus : Data Performance Mengajar Dosen ),” Semin. Nas. Teknol. 2007 (SNT 2007), no. November, pp. 1–7, 2007.

B. Kaur, “Improving the Color Image Segmentation using Fuzzy-CMeans,” no. 978, pp. 789–794, 2016.

K. Pytel, “Hybrid Fuzzy-Genetic Algorithm Applied to Clustering Problem,” vol. 8, pp. 137–140, 2016.

C.-F. Huang, C.-H. Chang, B. R. Chang, and D.-W. Cheng, “A study of a hybrid evolutionary fuzzy model for stock selection,” 2011 IEEE Int. Conf. Fuzzy Syst. FUZZIEEE 2011, pp. 210–217, 2011.

H. Kahramanli and N. Allahverdi, “Design of a hybrid system for the diabetes and heart diseases,” Expert Syst. Appl., vol. 35, no. 1–2, pp. 82–89, 2008.

A. M. A. . Parewe and W. F. Mahmudy, “Seleksi Calon Karyawan menggunakan Metode Fuzzy Tsukamoto,” Semin. Nas. Teknol. Inf. dan Komun. (SENTIKA), Yogyakarta, pp. 265–275, 2016.

S. Bandyopadhyay, H. Mistri, P. Chattopadhyay, and B. Maji, “Antenna array synthesis by implementing non-uniform amplitude using Tsukamoto fuzzy logic controller,” Proc. 2013 Int. Conf. Adv. Electron. Syst. ICAES 2013, pp. 19–23, 2013.

V. A. Lestari, I. Aknuranda, and F. Ramdani, “Usability Evaluation of E-Government using ISO 9241 and Fuzzy Tsukamoto Approach,” JTEC, pp. 2–6, 2016.

T.Sutojo, E. Mulyanto, and V. Suhartono, Kecerdasan Buatan. Yogyakarta, Semarang: ANDI, UDINUS, 2011.

Y. Jin, Advanced Fuzzy Systems Design and Applications. New York: Physica-Verlag, 2003.

P. Singhala, D. N. Shah, and B. Patel, “Temperature Control using Fuzzy Logic,” Int. J. Instrum. Control Syst., vol. 4, no. 1, pp. 1–10, 2014.

M. Merzougui, L. Matsi, and L. Matsi, “Entropic Approach and Evolution Strategies for Optimizing the Image Segmentation by Pixel Classification : Application to Quality Control,” Int. J. Comput. Appl., vol. 61, no. 13, pp. 22–28, 2013.

W. F. Mahmudy, “Optimization of Part Type Selection and Machine Loading Problems in Flexible Manufacturing System Using Variable Neighborhood Search,” IAENG Int. J. Comput. Sci., vol. 42, no. 3, pp. 254–264, 2015.


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