scholarly journals Perbandingan Metode ANN-PSO dan ANN-GA untuk Peningkatan Akurasi Prediksi Harga Emas Antam

2019 ◽  
Vol 10 (2) ◽  
pp. 101-106
Author(s):  
Nadia Annisa Maori ◽  
Keyword(s):  
Neural Network ◽  
Algorithm Optimization ◽  
Swarm Optimization ◽  
A Value ◽  
The Neural Network ◽  
The Us ◽  
Us Dollar ◽  
Artificial Neural Network Ann ◽  
Backpropagation Artificial Neural Network

Antam's gold price is more expensive than the price of gold used by more investors for long-term use. Sometimes the price of antam gold cannot be predicted at any time. Antam's rising gold prices were moved by many factors, sent in exchange rates of US dollars (USD). If the exchange rate of the US dollar (USD) decreases, the price of gold will rise and vice versa, if the value of the US dollar (USD) strengthens, the price of gold will increase. This condition makes it difficult for investors to predict the price of gold in the future. Backpropagation Artificial Neural Network (ANN) is known as one of the good methods in predicting. In this study an evaluation of the results of the price of gold using ANN with the help of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) optimization. PSO has many similarities with GA, which is an algorithm adopted from the process of supporting humans. The results of the study prove that PSO Optimization is able to provide an increase in optimizing the weights on the Neural Network by producing the best RMSE value, which is equal to 0.026, while GA optimization only produces a value of 0.09.

scholarly journals Fluktuasi Biaya Haji Dalam Rupiah, Dolar AS dan Dinar: Perspektif Investasi Syariah

2018 ◽  
Vol 3 (1) ◽  
pp. 139
Author(s):  
Arina Hidayati
Keyword(s):  
Data Exchange ◽  
Purchasing Power ◽  
A Value ◽  
The Us ◽  
Qualitative Descriptive ◽  
Us Dollar ◽  
The Stability ◽  
Financial Transactions ◽  
Descriptive Method

Abstract: Haji fee always fluctuated from time to time due to changes in economic conditions. The study compared fluctuations Haji Cost of Year 1998-2017 in the currency Rupiah, US Dollar and Gold Dinar to determine the stability of Hajj Cost fluctuations from year to year as consideration of financial planning in preparation for Hajj and other long-term needs as a form of investment sharia. The research method in this study is qualitative descriptive method. Data collected from the Presidential on Fees Hajj of the Year 1998-2017, the data exchange in the BI and the price of the dinar in geraidinar.com then process the data in the form of a non descriptive statistics. From the discussion of research on the fluctuation of the Year 1998-2017 Haji fee in Rupiah, US Dollar and the Dinar result that the US dollar is more stable than the amount, is more stable than Rupiah Dinar and Dinar value and purchasing power of the most volatile than Rupiah and US Dollar. Dinar most encouraged to be the choice of savings in preparing for Hajj fees and other long-term funding needs, poor second to the US Dollar and Rupiah ranked third. Islamic investment safest long term is in dinars, as it has a value and purchasing power of the most stable, followed by the US dollar and the rupiah is recommended only used in everyday financial transactions in the short term.Abstrak: Biaya Haji senantiasa mengalami fluktuasi dari waktu ke waktu seiring dengan perubahan kondisi perekonomian. Penelitian ini membandingkan fluktuasi Biaya Haji dari Tahun 1998-2017 dalam satuan mata uang Rupiah, Dolar AS dan Dinar Emas untuk mengetahui stabilitas fluktuasi Biaya Haji dari tahun ke tahun sebagai bahan pertimbangan perencanaan keuangan dalam rangka persiapan Ibadah Haji dan kebutuhan jangka panjang lainnya sebagai bentuk investasi syariah. Metode penelitian dalam penelitian ini bersifat kualitatif dengan metode deskriptif. Data dikumpulkan dari Kepres tentang Biaya Haji dari Tahun 1998-2017, data nilai tukar mata uang di BI dan harga dinar di geraidinar.com kemudian mengolah data dalam bentuk non statistik deskriptif. Dari pembahasan penelitian terhadap fluktuasi Biaya Haji dari Tahun 1998-2017 dalam Rupiah, Dolar AS dan Dinar diperoleh hasil bahwa Dolar AS lebih stabil daripada Rupiah, Dinar lebih stabil daripada Rupiah dan Dinar memiliki nilai dan daya beli paling stabil daripada Rupiah dan Dolar AS. Dinar paling dianjurkan menjadi pilihan tabungan dalam mempersiapkan Biaya Haji dan kebutuhan dana jangka panjang lainnya, urutan ke dua Dolar AS dan Rupiah diurutan ketiga. Investasi syariah jangka panjang paling aman adalah dalam Dinar, karena memiliki nilai dan daya beli paling stabil, disusul Dolar AS dan Rupiah dianjurkan hanya digunakan dalam transaksi keuangan sehari-hari dalam jangka pendek.

scholarly journals Long-Term evaluation of biogas energy potential based on the neuronal network approach

2020 ◽  
Vol 167 ◽  
pp. 05006
Author(s):  
M Córdova-Suárez ◽  
J. Sosa-Cárdenas ◽  
Y. Cifuentes-Suárez ◽  
L. Sánchez-Almeida
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Back Propagation ◽  
Waste Generation ◽  
Energy Potential ◽  
The Neural Network ◽  
Term Evaluation ◽  
Biogas Energy ◽  
Artificial Neural Network Ann

The energy potential of biogas is estimated from the biomass quantity, that is, a biodegradability values obtained from the organic fraction of municipal solid waste (MSW). In this study, the percentage contribution of each and every type of waste was quantified according to the waste classification., In addition, the waste generation data was projected by applying both artificial neural network (ANN) and mathematical models and 4 types of biomass wastes which accounts for a contribution of about 63% of the total waste sampled were obtained. The projection of the weights of the waste was carried out from 2015 to 2030, with the application of the neural network model with Back-propagation. All in all, under the application of the mathematical models, it has been shown that the Ecuadorian model predicted not only a high average volume, but also a large annual value of biogas energy.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

scholarly journals Cointegration of Stock Market and Exchange Rate in Indonesia

2019 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Pribawa E Pantas ◽  
Muhamad Nafik Hadi Ryandono ◽  
Misbahul Munir ◽  
Rofiul Wahyudi
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Capital Market ◽  
Global Financial Crisis ◽  
The Us ◽  
Us Dollar ◽  
The Exchange Rate ◽  
Johansen Cointegration Test ◽  
The Global Financial Crisis

This study aims to determine the long-term relationship between stock market and exchange rate in Indonesia. The research method used is Johansen cointegration test. The results of this study found no cointegration between the variables tested. Thus the exchange rate, JII, and IHSG have no relationship in the long term. The fluctuation of the rupiah exchange rate in recent years did not generally affect the performance of stock indices especially after the global financial crisis of 2008. This shows the capital market in Indonesia has a good performance so that it is not so sensitive to the sentiment of the decline in the rupiah against the US dollar. This finding is in line with the findings of Syahrer (2010) which states the exchange rate has no effect on the stock market.

scholarly journals Neural Networks Applied to Estimating Subglacial Topography and Glacier Volume

2009 ◽  
Vol 22 (8) ◽  
pp. 2146-2160 ◽  
Author(s):  
Garry K. C. Clarke ◽  
Etienne Berthier ◽  
Christian G. Schoof ◽  
Alexander H. Jarosch
Keyword(s):  
Neural Network ◽  
Regional Scale ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Ice Caps ◽  
Mountain Glaciers ◽  
The Neural Network ◽  
Glacier Ice ◽  
Subglacial Topography ◽  
Artificial Neural Network Ann

Abstract To predict the rate and consequences of shrinkage of the earth’s mountain glaciers and ice caps, it is necessary to have improved regional-scale models of mountain glaciation and better knowledge of the subglacial topography upon which these models must operate. The problem of estimating glacier ice thickness is addressed by developing an artificial neural network (ANN) approach that uses calculations performed on a digital elevation model (DEM) and on a mask of the present-day ice cover. Because suitable data from real glaciers are lacking, the ANN is trained by substituting the known topography of ice-denuded regions adjacent to the ice-covered regions of interest, and this known topography is hidden by imagining it to be ice-covered. For this training it is assumed that the topography is flooded to various levels by horizontal lake-like glaciers. The validity of this assumption and the estimation skill of the trained ANN is tested by predicting ice thickness for four 50 km × 50 km regions that are currently ice free but that have been partially glaciated using a numerical ice dynamics model. In this manner, predictions of ice thickness based on the neural network can be compared to the modeled ice thickness and the performance of the neural network can be evaluated and improved. From the results, thus far, it is found that ANN depth estimates can yield plausible subglacial topography with a representative rms elevation error of ±70 m and remarkably good estimates of ice volume.

The Effect of Covid-19, Rupiah Exchange Rate, and Inflation on the Indonesia Sharia Stock Index during the Covid-19 Pandemic

2021 ◽  
Vol 4 (1) ◽  
pp. 313-336
Author(s):  
Achmad Agus Priyono ◽  
Ari Kartiko
Keyword(s):  
Exchange Rate ◽  
Time Series Data ◽  
Negative Impact ◽  
Stock Index ◽  
Series Data ◽  
Stock Market Index ◽  
Short Term ◽  
The Us ◽  
Us Dollar

Purpose of this study is to clarify the effect of the number of daily cases reported to have contracted the Covid-19 virus, the exchange rate of the rupiah against the US dollar and inflation on the movement of the Indonesian Sharia stock index (ISSI) during the Pandemic Covid 19 in the short term and long term. Data analysis methods that used is analysis Error Correction Mechanism (ECM) using Eviews software 10. The data collected is daily time series data starting from March 2, 2020 to May 31, 2021 so that the number of samples collected obtained as many as 283 samples . The results of the study stated that the addition of the daily number of reported cases of contracting the Covid-19 virus has a negative impact on The Indonesian Sharia Stock Market Index (ISSI) during the Covid-19 pandemic, so that encourage the weakening of the Stock Index both in the long and long term short. Likewise, the weakening of the rupiah against the US dollar will caused the fall of the sharia index during the Covid 19 pandemic, both in the long term and long and short term. However, the study found no effect inflation on the Indonesian Sharia Stock Index (ISSI) during the Covid19 pandemic, good long term and short term

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

EEG Data Mining Using PCA

2011 ◽  
pp. 161-180 ◽  
Author(s):  
Lenka Lhotská ◽  
Vladimír Krajca ◽  
Jitka Mohylová ◽  
Svojmil Petránek ◽  
Václav Gerla
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Principal Components ◽  
The Neural Network ◽  
Eeg Data ◽  
Eigen Decomposition ◽  
Electroencephalogram Eeg ◽  
Epileptic Spike ◽  
Eeg Signal Processing

This chapter deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. The principal components are estimated from the signal by eigen decomposition of the covariance estimate of the input. Alternatively, they can be estimated by a neural network (NN) configured for extracting the first principal components. Instead of performing computationally complex operations for eigenvector estimation, the neural network can be trained to produce ordered first principal components. Possible applications include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

scholarly journals Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

2020 ◽  
Vol 39 (6) ◽  
pp. 9027-9035
Author(s):  
Xi Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Rolling Bearing ◽  
Short Term ◽  
Swarm Optimization ◽  
Term Memory

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with Particle Swarm Optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine. Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

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