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All Journal Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) REKAYASA International Journal of Artificial Intelligence Research The Spirit of Society Journal : International Journal of Society Development and Engagement Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Teknik Elektro dan Komputer TRIAC Applied Technology and Computing Science Journal Infotekmesin Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Jurnal Ners Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Journal of Soft Computing Exploration JURNAL POLI-TEKNOLOGI Indonesian Journal of Innovation Multidisipliner Research Jurnal Cakrawala Maritim Indonesian Journal of Innovation Multidisipliner Research
Agus Khumaidi
Politeknik Perkapalan Negeri Surabaya
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Journal : REKAYASA

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Radial Basis Function Neural Network sebagai Pengklasifikasi Citra Cacat Pengelasan Rinanto, Noorman; Wahyudi, Mohammad Thoriq; Khumaidi, Agus
Rekayasa Vol 11, No 2: Oktober 2018
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (937.111 KB) | DOI: 10.21107/rekayasa.v11i2.4418

Abstract

Tingginya resiko kesalahan manusia dalam inspeksi visual untuk cacat pengelasan yang masih mengandalkan kemampuan manusia sulit untuk dihindari. Oleh sebab itu, penelitian ini mengusulkan sebuah klasifikasi cacat las visual dengan menggunakan algoritma Radial Basis Function Neural Network (RBFNN). Masukan RBFNN berupa citra las yang terdiri dari 5 (lima) kelas cacat las visual dan 1 (satu) kelas citra las normal. Citra las tersebut diproses terlebih dahulu menggunakan metode ekstraksi fitur Fast Fourier Transform (FFT) dan Descreate Cosine Transform (DCT). Hasil kedua metode ekstraksi fitur tersebut kemudian akan saling dibandingkan untuk mengetahui kinerja RBFNN. Hasil pengujian menunjukkan bahwa sistem dengan metode FFT-RBFNN dapat menggolongkan citra cacat las dengan akurasi sebesar 91.67% dan DCT-RBFNN sekitar 83.33% dengan jumlah neuron hidden layer sebanyak 15 dan parameter spread adalah 4.Kata Kunci: Radial Basis Function Neural Network (RBFNN), FFT, DCT, cacat las, klasifikasi.Radial Basis Function Neural Network as a Weld Defect Classifiers ABSTRACTThe high risk of human error in visual inspection of welding defects that still rely on human capabilities is difficult to avoid. Therefore, this study proposes a classification of visual welding defects using the Radial Base Function Neural Network (RBFNN) algorithm. The RBFNN input is in the form of a welding image consisting of 5 (five) visual welding defect classes and 1 (one) normal welding image class. The weld image is processed first using the Fast Fourier Transform (FFT) and Descreate Cosine Transform (DCT) feature extraction methods. The results of these two feature extraction methods will be compared to find out the RBFNN performance. The test results show that the system with FFT-RBFNN method can classify the image of weld defects with an accuracy of 91.67% and DCT-RBFNN around 83.33% with the number of hidden layer neurons as much as 15 and the parameters of spread are 4.Keywords: Radial Basis Function Neural Network (RBFNN), FFT, DCT, weld defect, classification.
Co-Authors Abdul Hafizh Abyan Faruq Achmad, Vandy Adhitya, Ryan Yudha Adi Rahmad Ramadhan Adi Wisnu Sahputera Adianto Adianto Afianto, Afif Zuhri Am Maisarah Disrinama Anggarjuna Puncak Pujiputra Ardiana, Mirza Arfianto, Afif Zuhri Arief Subekti Arninputranto, Wibowo Arumsari, Nurvita Astutik, Rina Puji Aulia Rahma Annisa Bagus Setiawan, Danis Basuki Rahmat Basuki Rahmat Masdi Siduppa Basuki Rahmat, Mohammad Bayu, Nurissabiqoh Binta Bhakti Bhakti Budi, Perdinan Setia Budiawati, Ratna Budiyanto, Ekky Nur C. I. Sutrisno Cahyono, Ferry Budi Danis Bagus Setiawan Darmawan, Wahyu Dewi Kurniasih Dianita Wardani Dianita Wardani Dika Rahayu Widiana Endrasmono, Joko Fadlol, Muhammad Thoriq Faturrahman, Bima Fitri Hardiyanti Hafid, Mohammad Arigo Al. Hananta A Hasin, M. Khoirul Hasin, Muhammad Khoirul Hendro Agus Widodo, Hendro Agus Ihsania, Tsabita Ii Munadhif Imah Luluk Kusminah Imam Sutrisno Imam Sutrisno Imam Sutrisno Indriawati, Melta Anindya Irfan Marzuqi Irma Rustini Aju Jami’in, Mohammad Abu Joesianto Eko Poetro Joko Endrasmono Joko Endrasmono Khoirun Nasikhin Kusminah, Imah Luluk Kusumah, Adam Lilik Subiyanto M. Basuki Rahmat Mades Darul Khairansyah Malik, Alfianto Taufiqul Mat Syai’in Mochamad Yusuf Santoso Mohammad Basuki Rahmat Mustika Kurnia Mayangsari Nasikhin, Khoirun Noorman Rinanto Oktavia, Shelly Pradana, Rizal Lucky Prahasta, Brendi Pristovani Riananda, Dimas Pristovani, Dimas Projek Priyonggo Sumangun Lukitadi Pujiputra, Anggarjuna Puncak Putra, Zindhu Maulana Ahmad Rafsanjani, Zainu Rahmat, M. Basuki Rahmawati, Nanda Putri Ramadhani, Mifta Aulia Riananda, Dimas Pristovani Rinanto, Noorman Rizal Fahmi Rizal Fahmi Rizky, Sofi Berliana Romadloni, Faiz Ryan Yudha Adhitya Santoso, Agus Dwi Setiani, Vivin Setyawati, Emeralda Eka Putri Sholahuddin Muhammad Irsyad Sholihah, Mar'atus Sri Wiwoho Mudjanarko, Sri Wiwoho Sryang T Sarena Suwandi, Donny Aryo Seno Syai’in, Mat Syai’in Wahyudi, Mohammad Thoriq Wibowo, Sekarsari Wisnu Sahputera, Adi Yudha Adhitya, Ryan Yugowati Praharsi Yuning Widiarti, Yuning Zazila, Mujtaba Fa'akuli Zindhu Maulana Ahmad Putra