Implementation of Artificial Neural Networks In Predicting Students English Understanding Level Using The Backpropagation Method

Septri Wanti Siahaan, Kristin Daya Rohani Sianipar, Iin Parlina

Abstract


English is a language that unites humans in communicating with others. The existence of language differences can make it difficult for people to understand each other in dialogue. Therefore, the role of English is very useful to unite human communication. In this case, researchers make research to predict the level of understanding of students in English. Predicting the level of understanding of students in English is needed to determine the level of ability or understanding of students in English so that students can further enhance student abilities. English is very necessary for students to support a bright future. In this study implements the Artificial Neural Network in conducting research and applying the bacpropagation method in it. To complete this study, researchers used several criteria, namely: Reading References, Hearing from the Environment, Practicing, Utilizing Technology. Of the four criteria using the backpropagation method is useful for training in predicting the level of understanding of students in English. The results of this research test get that the level of understanding of students in English is with the level of accuracy and architecture

Keyword : Artificial Neural Network; Prediction; English; Level of Understanding; Backpropagation.

English is a language that unites humans in communicating with others. The existence of language differences can make it difficult for people to understand each other in dialogue. Therefore, the role of English is very useful to unite human communication. In this case, researchers make research to predict the level of understanding of students in English. Predicting the level of understanding of students in English is needed to determine the level of ability or understanding of students in English so that students can further enhance student abilities. English is very necessary for students to support a bright future. In this study implements the Artificial Neural Network in conducting research and applying the bacpropagation method in it. To complete this study, researchers used several criteria, namely: Reading References, Hearing from the Environment, Practicing, Utilizing Technology. Of the four criteria using the backpropagation method is useful for training in predicting the level of understanding of students in English. The results of this research test get that the level of understanding of students in English is with the level of accuracy and architecture

Keyword : Artificial Neural Network; Prediction; English; Level of Understanding; Backpropagation.


Full Text:

PDF

References


A. P. Windarto, "IMPLEMENTASI JST DALAM MENENTUKAN KELAYAKAN NASABAH PINJAMAN KUR PADA BANK MANDIRI MIKRO SERBELAWAN DENGAN METODE BACKPROPAGATION," Sains Komput. Inform., vol. 1, no. 1, pp. 12-23, 2017.

A. Revi et al., "DAGING SAPI BERDASARKAN PROVINSI," vol. 2, pp. 297-304, 2018.

A. Revi, S. Ramadan, R. N. Sari, and Solikhun, "MODEL JARINGAN SYARAF TIRUAN DALAM MEMPREDIKSI PENDAPATAN PERKAPITA MASYARAKAT PERKOTAAN PADA GARIS KEMISKINAN BERDASARKAN PROPINSI," Kumpul. J. Ilmu Komput., vol. 05, no. 02, pp. 122-135, 2018.

Fausett, L. (1994). Fundamentals of Neural Networks (Prentice-H). Prentice-Hall.

J. Cao, H. Cui, H. Shi, and L. Jiao, "Big Data : A Parallel Particle Swarm Optimization-Backpropagation Neural Network Algorithm Based on MapReduce," PLoS ONE, pp. 1-17, 2016.

M. Saxena, "Application of Backpropagation Neural Network for Prediction of Some Shell Moulding Parameters, "International Journal of Mechanical And Production Engineering, vol. 3, no. 12, pp. 1-5, 2015.

M. Yanto, "Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Perceptron Pada Pola Penentuan Nilai Status Kelulusan," Jurnal TEKNOIF, vol. 5, no. 2, pp. 79-87, 2017.

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

Maharani D. Wuryandari. Aji Sudarsono, "Jaringan Syaraf tiruan Untuk Memprediksi Laju Pertumbuhan Penduduk Menggunakan Metode Backpropagation (Studi Kasus Di Kota Bengkulu)", Bengkulu, 2016.

McCarthy, M. Dahria, "Jurnal SAINTIKOM. LPM STMIK TRI GUNA DARMA. Sumatera Utara", Medan, 2008.

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

S. Kusmaryanto, "Jaringan Saraf Tiruan Backpropagation untuk Pengenalan Wajah Metode Ekstraksi Fitur Berbasis Histogram," J. EECCIS Vol. 8, No. 2, Desember 2014, vol. 8, no. 2, pp. 297-304, 2018.

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

Simon Haykin, irma Handayani, "Peramalan Beban Tenaga Listrik Jangka Pendek Menggunakan Metode Jaringan Syaraf Tiruan", Banten, 2012.

Yani, E. Nurul M. Sukarno, "Perancangan Dan Implementasi Jaringan Saraf Tiruan Backpropagation Untuk Mendiagnosa Penyakit Kulit", 2005.




DOI: https://doi.org/10.30596/jcositte.v1i2.5072

Refbacks

  • There are currently no refbacks.