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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika INTEGER: Journal of Information Technology Prosiding Seminar Nasional Sains dan Teknologi Terapan Indonesian Journal of Electrical Engineering and Computer Science Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Muchamad Kurniawan
Institut Teknologi Adhi Tama Surabaya
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : Jurnal IPTEK

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Premise Parameter Optimization on Adaptive Network Based Fuzzy Inference System Using Modification Hybrid Particle Swarm Optimization and Genetic Algorithm Muchamad Kurniawan; Nanik Suciati
Jurnal IPTEK Vol 22, No 2 (2018)
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.iptek.2018.v22i2.274

Abstract

ANFIS is a combination of the Fuzzy Inference System (FIS) and Neural Network (NN), which has two training parameters, premise and consequent. In the traditional ANFIS, Least Square Estimator (LSE) and Gradient Descent (GD) are commonly used learning algorithms to train the two parameters. The combination of those two learning algorithms tends to produce the local optimal solution. Particle Swarm Optimization (PSO) can converge quickly but still allow for getting the local optimal solution because PSO is unable to find a new solution space. Meanwhile, Genetic Algorithm (GA) has been reported to be able to find a wider solution space. Hybrid PSOGA is expected to give a better solution. In this study, modification of hybrid PSOGA is used to train the premise parameter of ANFIS. In experiments, the accuracy of the proposed classification method, which is called ANFIS-PSOGA, is compared to ANFIS-GA and ANFIS-PSO on Iris flowers, Haberman, and Vertebral datasets. The experiment shows that ANFIS-PSOGA achieves the best result compared to the other methods, with an average of accuracy 99.85% on Iris flowers, 84.52% on Haberman, and 91.83% on Vertebral.
Co-Authors Abdul Holik Adela Rizky Oktavyani Ahmad Fawaz Aji Wicaksono Aldi Wachid Arifin Alexandria Felicia Seanne Ananda Galang Moeslim Humania Andy Rachman Anindya Berliana Santoso Anton Yudhana Astrid Dwi Karolin Nofana Bagas Eka Prasetyo Chelvin Suprapto Chrisna Adrian Dwiputra Haryono Ester Yulitania Toker Fadhilatur Rochman Fajar Yulian Siska Utama Hendro Nugroho Iqbal Izha Mahendra Khisby Al Ghofari M. Faj’rul Falaah Maftahatul Hakimah March Angga V. W Maryam Ummul Habibah Mitra Adhimukti Mohammad Fahry Sholahuddin Nadia Talina Syafina Naili Saidatin Nanda Hafiz Ar Nanik Suciati Pakarbudi, Adib Pambudi O.T. Rakha Satria Putra Rani Rotul Muhima Rani Rotul Muhima Sadewa Wibawanto Septiyawan Rosetya Wardhana Siti Agustini, Siti Sunardi, Sunardi