Main Article Content

Abstract

This artificial neural network research article aimed to overview the current teaching process focusing on lecturer performance using the perceptron algorithm, improving the teaching process, developing the lecturer based on the perceptron algorithm's results, evaluating the speed and accuracy of the perceptron algorithm in evaluating performance lecturer and learning the rules of the perceptron algorithm in processing assessment criteria for lecturers in tertiary institutions. In this case, the perceptron algorithm was used to recognize the questionnaire data input patterns. The perceptron algorithm was trained and tested to recognize input data patterns so that this neural network could identify input data patterns from questionnaire data.

Keywords

artificial neural networks perceptron algorithms input patterns

Article Details

How to Cite
Irwan, I., Putra, R., Krismadinata, K., & Jalinus, N. (2021). Introduction to questionnaire data patterns using Perceptron Algorithm for lecturer improvement and development in higher education. INVOTEK: Jurnal Inovasi Vokasional Dan Teknologi, 21(1), 1-10. https://doi.org/https://doi.org/10.24036/invotek.v21i1.743

References

  1. [1] J. Siang, Jaringan Syaraf Tiruan dan Pemrograman Menggunakan MATLAB. Andi Yogyakarta, 2014.
  2. [2] K. Yudhistiro, “Pemanfaatan Neural Network Perceptron pada Pengenalan Pola Karakter,” Smatika J., vol. 7, no. 2, pp. 21–25, 2017.
  3. [3] D. Puspitanigrum, Pengantar Jaringan Syaraf Tiruan. Andi, 2006.
  4. [4] M. Yanto, R. Sovia, and P. Wiyata, “Jaringan Syaraf Tiruan Perceptron Pengenalan Pola Sistem Irigasi Lahan Pertanian Di Kabupaten Pesisir Selatan,” pp. 111–115, 2018.
  5. [5] M. U. Musthofa, Z. K. Umma, and A. N. Handayani, “Analisis Jaringan Saraf Tiruan Model Perceptron Pada Pengenalan Pola Pulau di Indonesia,” vol. 11, no. 1, pp. 89–100, 2017.
  6. [6] M. Yanto, T. Informatika, and F. I. Komputer, “PENERAPAN JARINGAN SYARAF TIRUAN DENGAN ALGORITMA PERCEPTRON PADA POLA PENENTUAN NILAI STATUS KELULUSAN,” vol. 5, no. 2, pp. 79–87, 2017.
  7. [7] N. Lestari and L. L. Van FC, “Implementasi jaringan syaraf tiruan untuk menilai kelayakan tugas akhir mahasiswa (studi kasus di amik bukittinggi),” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 10–24, 2017.
  8. [8] Kusumadewi,Sri, Artificial Intelegent. Andi Publisher, 2010.
  9. [9] Haryo Kusma Pratam, “Analisis Perbandingan Pengenalan Tanda Tangan Menggunakan Metoda Perceptron dan Backpropagation”,Universitas Islam Negeri Syarif Hidayahtullah, Jakarta, 2011.
  10. [10] Arifin, Muhamad, “Aplikasi Jaringan Saraf Tiruan Metode Pada Pengenalan Pola Notasi”, vol. 9, no. 1,2018.
  11. [11] Hermawan, Arif, Jaringan Saraf Tiruan Teori dan Aplikasi,Andi,2006
  12. [12] Pujiyanta Ardi,”Pengenalan Citra Objek Sederhana Dengan Jaringan Syaraf Tiruan Metode Perceptron”,vol. 3, no.1,2009
  13. [13] Simbolon, Regina,”Perangkat Lunak untuk Identifikasi dan Pengenalan Huruf Braile dengan Algoritma Perceptron”, Jurnal Pelita Informatika Budi Darma,2013
  14. [14] Fitri, Diana L, “Analisa Dan Perancangan Untuk Penerapan Metode Artificial Neural Network (ANN) Perceptron Dalam Menentukan Penyakit Pada Daun Tembakau Dan Daun Cengkeh” ,jurnal STMIK HIMSYA ,2012.
  15. [15] Demuth, H., Beale, M., Neural Network Toolbox, For Use with MATLAB, The MathWorks, 2001