JARINGAN SYARAF TIRUAN MENGGUNAKAN ALGORITMA BACKPROPAGATION UNTUK MEMPREDIKSI PRESTASI SISWA

  • Fatmi Zola SMK Negeri 2 Pinggir
Keywords: Backpropagation, Prediction, Artificial Neural Network, Software, Matlab

Abstract

Vocational High School Negeri 1 Bunga Raya (SMK N 1 Bunga Raya) is a vocational school in the field of Multi Competency. Currently there are 265 students with 5 majors (field) including Computer Network Engineering (TKJ), Motorcycle Engineering (TSM), Accounting, Agricultural Processing Technique (TPHP), Plantation Agribusiness (ATP) .To improve student achievement, then schools provide scholarships in the form of academic achievement scholarships. Artificial Neural Network System using Backpropagation method applied in this research to predict student achievement, and expected to help solve the problem of giving scholarship to student achievement. Implemented using Matlab as a supportive software. With the value of School examination as training data and testing. The result of testing with architectural pattern 4-2-1, the data into two parts that is 20 training data with percentage error 95,6%. And 20 test data with 100% error percentage. The smaller the level of accuracy error used will be the smaller deviation results Artificial Neural Network with the desired target.

References

Anike M., Suyoto., and Ernawati . (2012). “Pengembangan Sistem Jaringan Syaraf Tiruan dalam Memprediksi Jumlah Dokter Keluarga menggunakan Backpropagation”.

Anwar B. (2011) “Penerapan Algoritma Jaringan Syaraf Tiruan Backpropagation dalam Memprediksi Tingkat Suku Bunga Bank”.

Berutu R. ( 2017 ). “Implementasi Jaringan Syaraf Tiruan untuk Memprediksi Penjualan Kosmetik Decorative dengan Metode Backpropagation”.

Gupta A., and Shreevastava M. ( 2011 ). “Medical Diagnisis using Backpropagation Algorithma” :Internationla Journal, ISSN:2250-2459.

Kusmaryanto S. ( 2014 ). “Jaringan Syaraf Tiruan Backpropagation untuk Pengenalan Wajah Metode Ekstraksi Fitur Berbasis Histogram”.

Madontang Z.A. ( 2013 ). “ Jaringan Syaraf Tiruan dengan Algoritma Backpropagation untuk menentukan kelulusan Sidang Skripsi”.

Malik B.A., Naqash A., and Bhat. ( 2016). “Backpropagation Artificial Neural Network for Determination of Glocose Concentration from Near-Infrared Spectra”: India ICACCI, September 21-24.

Mustaqim J.R., Ratianingsih R., and Lusiyanti. ( 2017). “Prediksi Kualitas Air Bersih PDAM Kota Palu Menggunakan Metode Backpropagation”.

Pratama R.A., and Anifah L. ( 2011). “Peramalan Beban Listrik Jangka Panjang Provinsi D.I Yogjakarta menggunakan Neural Network Bacpropagation”.

Setiabudi D. ( 2015). “Sistem Informasi Peramalan Beban Listrik Jangka Panjang di Kabupaten Jember Menggunakan Jaringan Syaraf Tiruan Backpropagation”: ISSN:2476-9754.

Siang J.J. (2004). “Jaringan Syaraf Tiruan & Pemogramannya Menggunakan Matlab”: Andi.

Tanjung D.H. (2015). “Jaringan Syaraf Tiruan dengan Backpropagation untuk Memprediksi Penyakit Asma” : ISSN: 2354-5771.

Tisan A., and Chin J. ( 2016). “An End User Platform For FPGA-Based Design and Rapid Prototyping of Feed Forward Artificial Neural Networks with on-Chip Backpropagation Learning”.

Wuryandari M.D., and Afrianto I. ( 2012 ). “Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation dan Learning Vector Quantization pada Pengenalan Wajah”.

Yani E. ( 2005 ). “Pengantar Jaringan Syaraf Tiruan”

Published
2018-06-30