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Journal : Jurnal IPTEK

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.
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 Adhira Riyanti Amanda Adni Navastara, Dini Agus Eko Minarno Agus Priyono Agus Zainal Arifin Agus Zainal Arifin Ahmad Saikhu Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Akwila Feliciano Pradiptatmaka Alam Ar Raad Stone Aldinata Rizky Revanda Altriska Izzati Khairunnisa Hermawan Amelia Devi Putri Ariyanto Amirullah Andi Bramantya Andika Rahman Teja Anny Yuniarti Antonius Kevin Wiguna Ardian Yusuf Wicaksono Ari Wijayanti Aris Fanani Arrie Kurniawardhani Arsy Bilahi Tama Ary Mazharuddin Shiddiqi Arya Yudhi Wijaya Atika Faradina Randa Atikah, Luthfi Avin Maulana Awangditama, Bangun Rizki Ayu Kardina Sukmawati Ayu Septya Maulani Baso, Budiman Bryan Nandriawan Bui, Ngoc Dung Chastine Fatichah Chastine Fatichah Chilyatun Nisa' Damayanti, Putri Daniel Sugianto Darlis Herumurti Davin Masasih Diana Purwitasari Dimas Rahman Oetomo Dini Adni Navastara Dini Adni Navastara, Dini Adni Dion Devara Aryasatya Eko Prasetyo Eva Yulia Puspaningrum Evelyn Sierra Fairuuz Azmi Firas Faishal Azka Jellyanto Faizin, Muhammad 'Arif Fajar Astuti Hermawati Fandy Kuncoro Adianto Fandy Kuncoro Adianto Febri Liantoni, Febri Fiqey Indriati Eka Sari Fitri Bimantoro Ginardi, R.V. Hari Glenaya Gou Koutaki Gurat Adillion, Ilham Hafidz, Abdan Handayani Tjandrasa Handayani Tjandrasa Hani Ramadhan Haq, Arinal Hidayat, Ahmad Nur Hidayati, Shintami Chusnul Hilya Tsaniya Imagine Clara Arabella Imam Kuswardayan Imam Mustafa Kamal Irawan Rahardja, Agustinus Aldi Isye Arieshanti Isye Arieshanti Januar Adi Putra Januar Adi Putra Kautsar, Faiz Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata M. Bahrul Subkhi Maulidan Bagus A.R Maulidiya, Erika Mawaddah, Saniyatul MIFTAHOL ARIFIN, MIFTAHOL Mochammad Zharif Asyam Marzuqi Muchamad Kurniawan Muchamad Kurniawan Muchamad Kurniawan, Muchamad Muhamad Nasir Muhammad 'Arif Faizin Muhammad Alif Satriadhi Muhammad Farih Muhammad Fikri Sunandar Mutmainnah Muchtar Nafa Zulfa Ni Luh Made ITS Novrindah Alvi Hasanah R Dimas Adityo R. Dimas Adityo Rachman, Rudy Rahma Fida Fadhilah Rangga Kusuma Dinata Rangga Kusuma Dinata Rayssa Ravelia Rizal A Saputra Rizal A Saputra, Rizal A Rohman Dijaya Romario Wijaya Safhira Maharani Safhira Maharani Salim Bin Usman Salim Bin Usman Salsabiil Hasanah Sarimuddin, Sarimuddin Septiana, Nuning Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shintami Chusnul Hidayati Shofiya Syidada Sjahrunnisa, Anita Suastika Yulia Riska Sugianela, Yuna Surya Fadli Alamsyah Syavira Tiara Zulkarnain Tanzilal Mustaqim Tiara Anggita Tiara Anggita Tsaniya, Hilya Wahyu Saputra, Vriza Wan Sabrina Mayzura Wibowo, Della Aulia Wicaksono, Farhan Wijayanti Nurul Khotimah Yulia Niza Yulia Niza Yuna Sugianela Yuna Sugianela Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas