Optimasi Prediksi Dengan Algoritma Backpropagation Dan Conjugate Gradient Beale-Powell Restarts

Anjar Wanto



Abstrak


Optimization of a prediction (forecasting) is very important to do so that the predicted results obtained to be better and quality. In this study, the authors optimize previous research that has been done by the author using backpropagation algorithm. The optimization process will use Conjugate Gradient Beale-Powell Restarts. Data to be predicted is Consumer Price Index data based on health group from Medan Central Bureau of Statistics from 2014 until 2016. Previous research using 8 architectural models, namely: 12-5-1, 12-26-1, 12-29 -1, 12-35-1, 12-40-1, 12-60-1, 12-70-1 and 12-75-1 with best architectural models 12-70-1 with an accuracy of 92%. In contrast to previous research concentrating on finding accuracy using backpropagation, this study will optimize the backpropagation with Conjugate Gradient Beale-Powell Restart, which not only focuses on accuracy but also the convergence of the two algorithms and the translation of predicted results, which is not done in a previous study. This research will use the same architectural model as the previous research and will get the result with the accuracy of 92% with the best architectural model that is 12-70-1 (same as previous research). Thus, this model is good enough for prediction even with different algorithms, since the accuracy of converging backpropagation with Conjugate Gradient Beale-Powell Restarts.

Kata Kunci


Optimization, Prediction, Backpropagation, Beale-Powell Restarts


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Referensi


L. Handayani and M. Adri, “Penerapan JST ( Backpropagation ) untuk Prediksi Curah Hujan ( Studi Kasus : Kota Pekanbaru ),” Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 7, no. November, pp. 238–247, 2015.

R. Hrasko, A. G. C. Pacheco, and R. A. Krohling, “Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation,” Procedia Computer Science, vol. 55, no. Itqm, pp. 990–999, 2015.

A. Wanto and A. P. Windarto, “Analisis Prediksi Indeks Harga Konsumen Berdasarkan Kelompok Kesehatan Dengan Menggunakan Metode Backpropagation,” Sinkron Jurnal & Penelitian Teknik Informatika, vol. 2, pp. 37–43, 2017.

R. Y. Fa’rifah and Z. Busrah, “Backpropagation Neural Network Untuk Optimasi Akurasi Pada Prediksi Financial Distress Perusahaan,” Jurnal INSTEK, vol. 2, no. April, pp. 101–110, 2017.

I. Muzakkir, A. Syukur, and I. N. Dewi, “Backpropagation Dengan Seleksi Fitur Particle Swarm Optimization Dalam Prediksi Pelanggan Telekomunikasi,” Jurnal Pseudocode, vol. 1, pp. 1–10, 2014.

N. Aditiarini, “Metode Gradien Konjugat Dalam Menyelesaikan Masalah Optimasi Menggunakan Aplikasi Android,” Skripsi , Institut Pertanian Bogor, 2017.

K. S. Madhavan, “Knowledge Based Prediction through Artificial Neural Networks and Evolutionary Strategy for Power Plant Applications,” Journal of Scientific and Engineering Research, vol. 4, no. 9, pp. 371–376, 2017.

A. Wanto, M. Zarlis, Sawaluddin, D. Hartama, J. T. Hardinata, and H. F. Silaban, “Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves In The Predicting Process,” International Conference on Information and Communication Technology (IconICT), pp. 1–7, 2017.

M. S. Frits Fahridws Damanik, SST and S. Magdalena Sinaga, “Analisis Indeks Harga Konsumen (IHK) Kota Pematangsiantar,” Sensus Ekonomi, 2014.

A. Wanto, “Analisis Jaringan Saraf Tiruan Backpropagation Menggunakan Conjugate Gradient Fletcher Reeves Dalam Proses Memprediksi,” Tesis, Universitas Sumatera Utara, 2017.

E. P. Cynthia and E. Ismanto, “Jaringan Syaraf Tiruan Algoritma Backpropagation Dalam Memprediksi Ketersediaan Komoditi Pangan Provinsi Riau,” Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI) 9, pp. 18–19, 2017.

H. Y. Sari, “Optimasi Conjugate Gradient Pada Algoritma Backpropagation Neural Network Untuk Prediksi Kurs Time Series,” Jurnal Gema Aktualita, vol. 5, no. 1, pp. 86–90, 2016.

I. G. P. Arka, “Kajian Analisis Perfomansi Sistem Fire Alarm Dengan Mode Addresable dan Non Addresable Menggunakan Algoritma Genetika,” Jurnal Matrix, vol. 4, no. 1, pp. 33–34, 2014.

M. R. Lubis, “Metode Hybrid Particle Swarm Optimization - Neural Network Backpropagation Untuk Prediksi Hasil Pertandingan Sepak Bola,” Jurnal Sains Komputer & Informatika (J-SAKTI), no. 1, pp. 71–83, 2017.

S. Mirjalili, P. Jangir, S. Z. Mirjalili, S. Saremi, and I. N. Trivedi, “Optimization of problems with multiple objectives using the multi-verse optimization algorithm,” Knowledge-Based Systems, 2017.

A. Ehret, D. Hochstuhl, D. Gianola, and G. Thaller, “Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle,” Genetics Selection Evolution, vol. 47, no. 1, p. 22, 2015.

R. Olawoyin, “Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil,” Jurnal Chemosphere, vol. 161, pp. 145–150, 2016.

D. Huang and Z. Wu, “Forecasting outpatient visits using empirical mode decomposition coupled with backpropagation artificial neural networks optimized by particle swarm optimization,” PLoS ONE, vol. 12, no. 2, pp. 1–18, 2017.

P. Witoonchart and P. Chongstitvatana, “Structured SVM Backpropagation To Convolutional Neural Network Applying To Human Pose Estimation,” Journal of LATEX, vol. 92, pp. 39–46, 2017.

B. Keshtegar, “Limited conjugate gradient method for structural reliability analysis,” Engineering with Computers, vol. 33, no. 3, 2017.

Y. Sari, F. D. Marleny, R. Ansari, M. Izzana, A. P. Ricardus, and B. Lareno, “Optimasi Conjugate Gradient Pada Backpropagation Neural Network untuk Deteksi Kualitas Daun Tembakau,” Konferensi Nasional Sistem & Informatika, pp. 9–10, 2015.

U. N. Wisesty, I. Parwati, and Adiwijaya, “Deteksi Anomali pada Intrusion Detection System (IDS) Menggunakan Algoritma Backpropagation Termodifikasi Conjugate Gradient Polak Ribiere,” Indosc 2016, no. August, pp. 165–176, 2016.

A. Wanto, A. P. Windarto, D. Hartama, and I. Parlina, “Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density,” International Journal Of Information System & Technology (IJISTECH), vol. 1, no. 1, pp. 43–54, 2017.


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Universitas Andalas
Kampus Limau Manis, Padang 25163, Sumatera Barat

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