Analisis Jaringan Saraf Tiruan dengan Backpropagation pada korelasi Matakuliah Pratikum Terhadap Tugas Akhir
DOI:
https://doi.org/10.30865/jurikom.v9i1.3835Keywords:
Neural Network, Correlation, Final Project, Course, Artificial Neural Network, BackpropagationAbstract
Backpropagation is one of the methods contained in a neural network that is able to train dynamic networks using mathematical knowledge based on architectural models that have been developed in detail and systematically. Backpropagation itself is able to accommodate a lot of information that serves as a useful experience. The purpose of this research is to make it easier for AMIK Tunas Bangsa Pematangsiantar students to determine the topic of their final project with practical value so that they can do their final project quickly. So the authors conducted research using correlation in determining the topic of the final project. The data in this study were obtained directly from the AMIK Tunas Bangsa Education academics in Pematangsiantar City. The data used uses data on practical grades of AMIK Tunas Bangsa Stambuk students 2017 from semester 4 to semester 6. There are 5 network architecture models used in this study, namely 5-1-2, 5-6-2, 5-8 -2, 5-10-2, and 5-12-2. From the results of trials conducted with MATLAB software, the best architecture is the 5-1-2 model with an accuracy of 47%. Based on this background, it is hoped that the research results can help students in determining the topic of the final projectReferences
H. E. Prasetyo, R. Hadiani, and S. Setiono, “Analisis Data Runtun Waktu Debit Menggunakan Jaringan Syaraf Tiruan Di Das Wuryantoro Pada Awlr Kecamatan Wuryantoro,†Matriks Tek. Sipil, vol. 2, no. 2, pp. 64–71, 2014, [Online]. Available: https://matriks.sipil.ft.uns.ac.id/index.php/MaTekSi/article/view/164.
K. R. Ririh et al., “STUDI KOMPARASI DAN ANALISIS SWOT PADA IMPLEMENTASI KECERDASAN BUATAN ( ARTIFICIAL INTELLIGENCE ) DI INDONESIA,†vol. 15, no. 2, pp. 122–133, 2020.
P. Alkhairi, I. S. Damanik, A. P. Windarto, and S. S. Informasi, “Penerapan Jaringan Saraf Tiruan untuk Mengukur Korelasi Beban Kerja Dosen Terhadap Peningkatan Jumlah Publikasi,†no. September, pp. 581–601, 2019.
M. Hendriani, Rais, and L. Handayani, “Penerapan Artificial Neural Network Terhadap Identifikasi Wajah Menggunakan Metode Backpropagation,†Nat. Sci. J. Sci. Technol., vol. 8, no. 3, pp. 203–208, 2019, doi: 10.22487/25411969.2019.v8.i3.14599.
C. . I Made Suandi Putra MSc, CIA, C. Yullyan, SE, MAk, Ak., CPA, CIA, and Globaliia.org, “PERSPEKTIF DAN PANDANGAN GLOBAL Kecerdasan Buatan (Artificial Intelligence/AI) - Pertimbangan untuk Profesi Audit Internal,†Perspekt. DAN PANDANGAN Glob. Kecerdasan Buatan (Artificial Intell., vol. 9, no. 2, pp. 1–9, 2017.
H. Jaya et al., KECERDASAN BUATAN. .
W. Saputra, T. Tulus, M. Zarlis, R. W. Sembiring, and D. Hartama, “Analysis Resilient Algorithm on Artificial Neural Network Backpropagation,†J. Phys. Conf. Ser., vol. 930, no. 1, 2017, doi: 10.1088/1742-6596/930/1/012035.
L. Sinaga, E. Irawan, and W. Saputra, “Penerapan Jaringan Saraf Tiruan Metode Backpropogation Dalam Memprediksi Distribusi Air Pada PDAM Tirtauli Kota Pematangsiantar,†vol. 2, pp. 161–168, 2020.
