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Abstract

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.


 


Keywords: ANFIS, Backpropagation, Hybrid, Prediction

Keywords

ANFIS, Backpropagation, Hybrid, Prediction

Article Details

References

  1. [1] Agustin, Maria, 2012, ” Penggunaan Jaringan Syaraf Tiruan Backpropagation Untuk Penerimaan Mahasiswa Baru Pada Jurusan Teknik Komputer Di Politeknik Negeri Sriwijaya”, Jurnal : Jurusan Teknik Komputer Politeknik Negeri Sriwijaya.
  2. [2] Alven Safik Ritonga1, Suryo Atmojo, 2017, “ Pengembangan Model Jaringan Syaraf Tiruan Untuk Memprediksi Jumlah Mahasiswa Baru Di PTS Surabaya (Studi Kasus Universitas Wijaya Putra) “, Seminar Nasional Teknik Industri
  3. [3] A. Rahman, A.G. Abdullah, dan D.L. Hakim, 2012, “Prakiraan Beban Puncak Jangka Panjang pada Sistem Kelistrikan Indonesia Menggunakan Algoritma Adaptive Neuro-Fuzzy Inference Sistem”, Electrans, Vol.11, No.2, 18 -26.
  4. [4] Anugrah, 2012, ”Perbandingan jaringan saraf tiruan Backpropagation dan metode deret berkala Box-Jenkins (ARIMA) sebagai metode peramalan”, Jurnal: Jurusan Matematika Fakultas MIPA Universitas Negeri Semarang.
  5. [5] Bagus, Fatkhurrozi, M. Aziz Muslim, Didik R. Santoso, 2012, “Penggunaan Artificial Neuro Fuzzy Inference Sistem (ANFIS) dalam Penentuan Status Aktivitas Gunung Merapi”, Jurnal EECCIS Vol. 6, No. 2.
  6. [6] Candra, Dewi, Werdha, Wilubertha, Himawati, 2015, “Prediksi Tingkat Pengangguran Menggunakan Adaptif Neuro Fuzzy Inference System (ANFIS)”, Konferensi Nasional Sistem & Informatika.
  7. [7] Gandhi, Ramadhona, Budi, Darma, Setiawan, Fitra A. Bachtiar, 2012, “ Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation”, Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer.
  8. [8] Hanke, J.E., Wichern, D.W. 2015, “ Business Forecasting “. Prentice Hall, New York.
  9. [9] Linda, Puspa, 2015, “Peramalan Penjualan Produksi Teh Botol Sosro pada PT. Sinar Sosro Sumatera Bagian Utara Tahun 2014 dengan Metode Arima Box-Jenkins”, Jurnal : Fakultas Matematika dan IPA Universitas Sumatera Utara
  10. [10] Matonda, Arizona, Zakson, 2013, “Jaringan Syaraf Tiruan dengan Algoritma Backpropagation untuk Penentuan Kelulusan Sidang Skripsi”, Jakarta: Pelita Informatika Budi Darma.
  11. [11] Noor, Azizah, Kusworo, Adi b, Achmad, Widodo, 2013,“ Metode Adaptive Neuro Fuzzy Inference System (ANFIS) untuk Prediksi Tingkat Layanan Jalan “,Jurnal Sistem Informasi Bisnis 03.
  12. [12] Risa, Helilintar, Intan Nur Farida, 2018, “ Penerapan Algoritma K-Means Clustering Untuk Prediksi Prestasi Nilai Akademik Mahasiwa “, Jurnal Sains dan Informatika Volume 4, Nomor 2.
  13. [13] Rohana, Tatang , 2012, “ Teknik Pengolahan Citra Dan Adaptive Neuro Fuzzy Inference System (ANFIS) Untuk Mendeteksi Cacat Keping Printed Circuit Board (PCB)” Jakarta.
  14. [14] Siang, Jong Jek, 2005, “Jaringan Syaraf Tiruan dan Pemogramannya Menggunakan Matlab”. Yogyakarta: Andi Offset.
  15. [15] Wanti, Rahayu1, 2017, “ Model Penentuan Guru Berprestasi Berbasis Adaptive Neuro Fuzzy Inference System (ANFIS), Jurnal Sisfotek Global 2088 – 1762 Vol. 7 No. 1.
  16. [16] Wiwik, Anggraeni, 2012, “Aplikasi Jaringan Syaraf Tiruan Untuk Peramalan Permintaan Barang”, Jurnal: Jurusan Sistem Informasi, Institut Teknologi Sepuluh November.
  17. [17] Widodo, P.P., Handayanto, R.T., 2012, “ Penerapan Soft Computing dengan matlab “, Bandung.