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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) eProceedings of Engineering
Rita Rismala
Universitas Telkom
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : International Journal of Artificial Intelligence Research

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Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data Wisesty, Untari Novia; Sthevanie, Febryanti; Rismala, Rita
International Journal of Artificial Intelligence Research Vol 4, No 2 (2020): December 2020
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.127 KB) | DOI: 10.29099/ijair.v4i2.188

Abstract

Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression.  In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.
Co-Authors Ade Romadhony Agung Toto Wibowo Al Faraby, Said Alfredo Alfredo Anditya Arifianto Arby Fahrizi Subiyanto Arief Hutauruk Bobby Brillian Yerikho Dade Nurjanah Dimitri Mahayana Eko Darwiyanto Fahrudin Julianto Fakhri Fauzan Febryanti Sthevanie Fikri Bahiransyah Hasna Haifa Zahrah Hifzi Priabdi Hilmi Eko Arianto Iqbal Dwihanandrio Jondri Jondri Kadek Byan Prihandana Jati Kadek Byan Prihandana Jati Kridanto Surendro Kurniawan Nur Ramadhani Ledya Novamizanti Lugina Muhammad Mahmud Dwi Sulistiyo Mohammad Fathurrahman Muhammad Hanif Oktavianto Nur Ulfa Maulidevi Parjuangan, Sabam Ramadhan, Sageri Fikri Retno Novi Dayawanti Retno Novi Dayawati Rudy Prabowo Sari Rahmawati Serli Fatriandini Serli Fatriandini Shendy Murty Siti Sa’adah Tjokorda Agung Budi Wirayuda Untari Novia Wisesty YUYUN SITI ROHMAH Zaki Mudzakir Hidayatullah ZK Abdurahman Baizal