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All Journal TEKNIK INFORMATIKA Jurnal Simantec Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIV : International Journal on Informatics Visualization INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] PROMINE Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Telematika Jurnal Teknologi Terpadu TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Tika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer
Muchamad Kurniawan, Muchamad
Institut Teknologi Adhi Tama Surabaya
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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 Kurniawan, Muchamad; Suciati, Nanik
Jurnal IPTEK Vol 22, No 2 (2018): December
Publisher : LPPM Institut Teknologi Adhi Tama Surabaya (ITATS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.4 KB) | 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 Abdullah, Ryan Gading Andy Rachman Ar, Nanda Hafiz Arinal Haq, Fatahillah Arum Indah Sari Bagus S, Dadang Bamahri, Fakhirah Budi, Adib Pakar Fahrezi Kusuma, Andra Fanani, Yazid Farida Farida Farida Farida Faruq, Umar Al Fawaz, Ahmad Gusti Eka Yuliastuti Habibah, Maryam Ummul Hadimarta, Tommy Ferdian Hendro Nugroho, Hendro Ilmi, Hilmi Maulana Istighfariansyah, Risaldi Maftahatul Hakimah, Maftahatul Misbahul Munir Moch. Kalam Mollah Moneter, Mochamad Rizky Muhammad Aditya Kushardiawan Muhima, Rani Rotul N, Muhammad Dinarta Nabila Dayu Mega Anjani Naili Saidatin Nanik Suciati Nurlaily, Firdausiyah O.T., Pambudi Pakarbudi, Adib Pambudi, Oktavian Tegar Pratikto, Dimas Priska Amelia de Jong Putri, Chatarina Natassya Qornain, Wafi Dzul Rewina, Anggita Eka Ritonga, Mario Franko Ezra Hasiholan Rosetya Wardhana, Septiyawan Sandi Alala, Pratama Setyawati, Indah Siti Agustini, Siti Sulistyowati Sulistyowati Ubaidillah, M. Yahya Wachid, Aldi Wardhana, Septiyawan Rosetya Wicaksono, Mukhlis Adi Yunanda, Sita Fara Zaqiyah, Hafida Nur