Metode Hybrid Particle Swarm Optimization - Neural Network Backpropagation Untuk Prediksi Hasil Pertandingan Sepak Bola

Muhammad Ridwan Lubis(1*),

(1) AMIK Tunas Bangsa
(*) Corresponding Author

Abstract


Hybrid is using two methods to a problem with the aim to improve their approach towards the specified target data. Hybrid PSO-ANN one optimal algorithm to solve such predictions in football matches. The process begins with determining the outcome of test dataset with the neural network architecture, specify the input parameters, the value of weight up to the value of hidden layer and output layer. Then the optimization of the results of the first test on a training dataset optimized by Particle Swarm Optimization. Testing will continue over using back propagation neural network until the maximum iteration and the results of the initial approach the target value. Furthermore, from the output obtained to search the value of the average error.

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References


Agus Perdana Windarto, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject,” Int. J. Softw. Eng. Its Appl., vol. 10, no. 10, pp. 189–204, 2016.

M.f. Andrijasa, mistianingsih, “ penerapan jaringan syaraf tiruan untuk memprediksi jumlah pengangguran di propinsi kalimantan timur dengan menggunakan algoritma pembelajaran backpropagation” viewed 1 februari 2010.

Arif jurmawanto, rudy hartono, dhidik prastiyanto, “ aplikasi jaringan syaraf tiruan backpropagation untuk memprediksi penyakit tht di rumah sakit mardi rahayu kudus” viewed 2009.

Muhammad erwin ashari haryono, “model identifikasi peta secara otomatis menggunakan konsep jaringan syaraf tiruan backpropagation” viewed 1 juni 2004.

Nazla nurmalia, aris sugiharto, eko adi sarwoko, “ algoritma back propagatiun neural network untuk pengenalan pola karakter huruf jawa “.

Sri kusumadewi.2003, “artificial intellegence (teknik aplikasinya)”, graha ilmu, yogakarta.

Tole Sutikno, Ardi Pujianto, Yuni Tri Supanti, “Prediksi Resiko Kredit Dengan Jaringan Syaraf Tiruan Backpropagation”, viewed 2007.

Wati, Dwi Ana Ratna, 2011. Sistem Kendali Cerdas: Bandung

Sumijan, AgusPerdana Windarto, Abulwafa Muhammad and Budiharjo, 2016, “Implementation of Neural Networks in Predicting the Understanding Level of Students Subject”, International Journal of Software Engineering and Its Applications Vol. 10, No. 10, pp. 189-204.

Yohanes Suhari, “Jaringan Syaraf Tiruan : Aplikasi Pemilihan Merk “, viewed 2 Juli 2010.

Erick paulus, yessica nataliani.2007, “gui matlab”, penerbit andi , yogyakarta.

Jong jek siang.2009, “ jaringan syaraf tiruan & pemrograman menggunakan matlab”, penerbit andi, yogyakarta.

Hsieh, L.F., Huang, C.J. & Huang, C.L. 2007. Applying Particle Swarm Optimization To Schedule Order Picking Routes In A Distribution Center. Asian Journal of Management and Humanity Sciences. Vol. 1, No. 4. pp. 558-576.

Bernard renaldy suteja, “penerapan jaringan saraf tiruan propagasi balik studi kasus pengenalan jenis kopi”, viewed juni 2007.

Asriningtias, s. R., dachlan, h. S., & yudaningtyas, e. (2015). Optimasi training neural network menggunakan hybrid adaptive mutation pso-bp. Jurnal eeccis, 9(1), pp-79.

Chen, R.M. & Shih, H.F. 2013. Solving University Course Timetabling Problems Using Constriction Particle Swarm Optimization with Local Search. Article Algorithms 2013, 6, 227-244; doi:10.3390/a6020227. ISSN 1999-4893

Engelbrecht, A.P. 2006. Fundamentals of Computational Swarm Intelligence. Wiley.

Kusumawati, d., winarno, w. W., & arief, m. R. (2015). Prediksi kelulusan mahasiswa menggunakan metode neural network dan particle swarm optimization. Semnasteknomedia online, 3(1), 3-8.

Raharjo, j. S. D. (2013). Model artificial neural network berbasis particle swarm optimization untuk prediksi laju inflasi. Jurnal sistem komputer, 3(1), 10-21.

Bai, Qinghai. 2010. Analysis Of Particle Swarm Optimization Algorithm. CCSE, Computer and Information Science. www.ccsenet.org/cis College of Computer Science and Technology. Inner Mongolia University for Nationalities. Tongliao 028043: China

Ramanda, k. (2015). Penerapan particle swarm optimization sebagai seleksi fitur prediksi kelahiran prematur pada algoritma neural network. Jurnal teknik komputer amik bsi, 1(2), 178-183.

Septiana, l. (2016). Penerapan neural network berbasis particle swarm optimization untuk seleksi atribut penentuan mahasiswa drop out. Pilar, 9(2).

Taqiyuddin, t., & hadi, s. P. (2013). Studi optimal power flow pada sistem kelistrikan 500 kv jawa-bali dengan menggunakan particle swarm optimization (pso). Jurnal nasional teknik elektro dan teknologi informasi (jnteti), 2(3).




DOI: http://dx.doi.org/10.30645/j-sakti.v1i1.30

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