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All Journal Majalah Ilmiah Teknologi Elektro Vokasi: Jurnal Riset Akuntansi Jurnal Pendidikan Bahasa Bali Undiksha JUIMA : Jurnal Ilmu Manajemen Jurnal Ilmu Gizi : Journal of Nutrition Science Widya Balina :Jurnal Ilmu Pendidikan dan Ekonomi JIIP (Jurnal Ilmiah Ilmu Pendidikan) Journal of Tourism Economics and Policy Jurnal Ilmiah Pengabdian dan Inovasi Jurnal Nirta: Studi Inovasi Majalah Ilmiah Teknologi Elektro Jurnal Maisyatuna
Komang Tri Wahyuni .
Fakultas Ekonomi Universitas Mahasaraswati Denpasar
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Journal : Majalah Ilmiah Teknologi Elektro

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Deteksi Tipe Modulasi Digital Pada Automatic Modulation Recognition Menggunakan Support Vector Machine dan Conjugate Gradient Polak Ribiere-Backpropagation Komang Tri Wahyuni; I Made Oka Widyantara; NMAE Dewi Wirastuti
Jurnal Teknologi Elektro Vol 18 No 2 (2019): (Mei-Agustus) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.982 KB) | DOI: 10.24843/MITE.2019.v18i02.P18

Abstract

This research uses randomly generated digital data to detect modulation types. The types of modulation are tested QPSK, 16-QAM, and 64-QAM.In the characteristic extraction process uses a statistical feature set approach with the mean, varian, kurtosis and skewness, while feature selection uses Multi-Class Support Vector Machine(SVM) with 5 classes in the classification including (i)Not a feature, (ii)Mean, (iii)Varian, (iv)Kurtosis and (v)Skewness.In detecting the type of modulation in this study using Backpropagation Artificial Neural Networks by learning process used the Conjugate Gradient Polak Ribiere algorithm. This study, researcher also compared the learning with Conjugate Gradient Polak Ribiere and learning using Gradient Discent. The results of the comparison learning was 401 training data using Conjugate Gradient Polak Ribiere much better with a higher accurancy value of 86,20% and lower error rate of 13,80% in the iteration to 781, while the Gradient Discent in the same iteration the accuracy rate is 67,83% and the error rate is 32,17%. From the test results there are 4 feature groups that was able to recognize the type of modulation including (i) Mean,Variant,Kurtosis, (ii)Mean, Variant, Skewness, (iii)Variant, Kurtosis, Skewness and (iv)Mean, Kurtosis, Skewness.
Co-Authors Ak. S.E. Desak Nyoman Sri Werastuti . Ariasih, Kadek Dwi DESI NATALIA, DESI I Gede Darma Putra I Gusti Agung Krisna LESTARI I Made Oka Widyantara Isabela, Fransiska Lestari, IGA Krisna Made Dewantari Maheswari, Putu Gittanirwana Adinda Ni Made Ary Esta Dewi Wirastuti Ni Made Dwi Puspitawati Pande Putu Sri Sugiani, Pande Putu Sri Prof. Dr. I Nengah Suandi,M.Hum . Sthanika, Putu Devia Wisnu, I Wayan Gede