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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL MEDIA INFORMATIKA BUDIDARMA J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Tunas Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat BEES: Bulletin of Electrical and Electronics Engineering Bulletin of Data Science Journal of Artificial Intelligence and Engineering Applications (JAIEA) JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Okprana, Harly
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
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Analisis Laju Pembelajaran dalam Mengklasifikasi Data Wine Menggunakan Algoritma Backpropagation Hardinata, Jaya Tata; Okprana, Harly; Windarto, Agus Perdana; Saputra, Widodo
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 3, No 2 (2019): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v3i2.161

Abstract

Backpropagation is an artificial neural network that has the architecture in conducting training and determining the right parameters to produce the correct output of similar but not the same input. One of the parameters that influences the determination of bacpropagation architecture is the rate of learning, where if the value of the learning rate is too high then the network architecture becomes unstable otherwise if the value of the learning rate is too low the network architecture converges and takes a long time in training network architecture. This research data is secondary data sourced from UCI Data Mechine Learning. The best network architecture in this study is 13-10-3, with different learning rates ranging from 0.01, 0.03, 0.06, 0.01, 0.13, 0.16, 0.2, 0.23, 0.026, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.9. From the 21 different learning rate values in the 13-10-3 network architecture, it is found that the level of learning rate is very important to get the right and fast network architecture. This can be seen in experiments with a learning rate of 0.65 can produce a better level of accuracy compared to a learning rate smaller than 0.65.
Co-Authors Agus Perdana Windarto Ambariyanto Ambariyanto Andi Sahputra Annas Prasetio Aulanda, Lulu Bahrudi Efendi Damanik Cici Astria Dea Dwi Rizki Tampubolon Dedy Hartama Eka Irawan Eka Irawan F Fitriyani GS , Achmad Daengs Hadinata, Jaya Tata Halimatusakdiah Pohan Haryoto, Parawystia Prabasini Hasudungan Siahaan Hendro Sanjaya Heru Satria Tambunan Heru Satria Tambunan Ilham Syahputra Saragih Ina Syafitri Ingkana Devi Cahaya Irfan Sudahri Damanik Jaya Tata Hardinata Millah Sari Muhammad Zarlis Nasution, Rizki Alfadillah Nasution, Selvi Salsabillah Nasution, Siti Andry Yani Ni Luh Wiwik Sri Rahayu Ginantra Poningsih Poningsih Poningsih Poningsih, Poningsih Primatua Sitompul Rizky Khairunnisa Sormin Robiansyah, Wendi Sepridho, Jaka Sonia Tarigan Surya Darma Syahfitri, Retno Ayu Syahputra, Muhammad Riza Teuku Afriliansyah Tri Ayu Lestari Tri Novika Wanto, Anjar Widodo Saputra Winanjaya, Riki Yuegilion Pranayama