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Ira Puspita Sari
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Journal : Jurnal Gaussian

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PREDIKSI DATA HARGA SAHAM HARIAN MENGGUNAKAN FEED FORWARD NEURAL NETWORKS (FFNN) DENGAN PELATIHAN ALGORITMA GENETIKA (Studi Kasus pada Harga Saham Harian PT. XL Axiata Tbk) Ira Puspita Sari; Triastuti Wuryandari; Hasbi Yasin
Jurnal Gaussian Vol 3, No 3 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (668.363 KB) | DOI: 10.14710/j.gauss.v3i3.6455

Abstract

Artificial neural network (ANN) or Neural Network (NN) is an information processing system that has characteristics similar to biological neural networks. One of the ANN models have network is quite simple and can be applied to time series data prediction is Feed Forward Neural Networks (FFNN). In general, FFNN trained using Backpropagation algorithm to obtain weights, but performance will decrease and trapped in a local minimum when applied to data that have great complexity like financial data. The solution to this problem is to train FFNN using Genetic Algorithm (GA). GA is a search algorithm that is based on the mechanism of natural selection and genetics to determine the global optimum. Training FFNN using GA is a good solution but the problem is how to understand the workings of FFNN training using the GA, the determination of the combination of the probability of crossover (), number of populations, number of generations, and the size of the tournament (k) on the AG to produce predictive value approaching actual value. One possible option is to use the technique of trial-end-error by experimenting for some combination of these four parameters. Of the 64 times the application of the AG test results to train FFNN models on daily stock price data PT. XL Axiata Tbk obtained results are sufficiently accurate predictions indicated by the proximity of the target to the output of the crossover probability () 0.8, a population of 50, the number of generations 20000 and tournament size of 4 produces the testing RMSE 107.4769.  
Co-Authors Adrian Adi Putra Anindita, Arzeti ANIP FEBTRIKO Aqil Farras Ariif, Fatihah Mohd Arisandi, Diki Asnawi Abdullah Astuti Setiyani As’ad Isma Azaria, Damarati Bambang Hadi Sugito Berliana Putri Darjati Debi Setiawan, Debi Dedi Kurniawan Demes Nurmayanti Diana Dwi Astuti Endarini, Lully Hani Evi Yunita N Evi Yunita Nugrahini Evy Diah Woelansari Fadhilah, Muhammad Naufal Ginarsih, Yuni Hadi Suryono Hakim, Ridho Abdul Hasbi Yasin Hermiyanti, Pratiwi Hustinawati Ilmi, Mainatul Indah Lestari, Indah Ira Rahayu Tiyar Isfentiani, Dina Izah Yoelanda Jacky Junaidi Juliana Christyaningsih Kasiati Khambali, Khambali Kiaonarni O.W Kiaonarni OW Kusma, Nila Lembunai Tat Alberta Lembunai Tat Alberta, Lembunai Tat Liadesvita, Rinne Liza Trisnawati Lully Hani Endarini Luluk Elvitaria Luluk Elvitaria, Luluk Meutia Zahara Muhamasri, Crisna Mujayanto Museyaroh, Museyaroh Mutiarawati, Diah Titik N. S. Widodo Nia Silviana Noni, Nurdin NUR AENI Nur Hatijah Nur Hatijah Nurjanah, Dinda Rajma Nurwening TW P, Teresia Retna Pengge, Nuning Marina Pratiwi, Yuharika Puspitadewi, Teresia Retna Putri, Zhena Younantha Verronika Rahayu Sumaningsih Rahayuningsih, Christ Kartika Rahmadani, Ade Rahmantya, Yanneri Elfa Kiswara Ramalia Noratama Putri Retno Sasongkowati Rudiansyah Rudiansyah, Rudiansyah Sahputri, Sella Inda Sambas, Febriana Saputra, Irwan Siagian, Hotmaida Siti Alfiah Sukri Sukri Suliati Sumasto, Hery Syaid Alarbi Teta Puji Rahayu Tri Rahayuningsih Triastuti Wuryandari Victor Diwantara Wahyudi, Mochammad Erlangga Wahyuni, Maulida Yohanes Kambaru W Yusianti Silviani