scholarly journals PRAKIRAAN HARGA AKARWANGI: APLIKASI METODE JARINGAN SYARAF TIRUAN

2020 ◽  
Vol 13 (1) ◽  
pp. 14
Author(s):  
CHANDRA INDRAWANTO ◽  
ERIYATNO ERIYATNO ◽  
ANAS M. FAUZI ◽  
MACHFUD MACHFUD ◽  
SUKARDI SUKARDI ◽  
...  
Keyword(s):  
Time Series ◽  
Key Words ◽  
Time Series Data ◽  
Oil Prices ◽  
Series Data ◽  
Vetiveria Zizanioides ◽  
Output Layer ◽  
Network Method ◽  
Artificial Neural ◽  
Hidden Layer

ABSTRAK<br />Prakiraan harga terna akarwangi dan harga minyak akarwangi telah<br />dilakukan dengan menggunakan metode jaringan syaraf tiruan. Memakai<br />data harga dari Januari 2000 sampai Agustus 2006 dilakukan prakiraan<br />harga untuk 24 bulan kedepan. Prakiraan terbaik dengan Mse pelatihan<br />dan Mse testing yang rendah didapat pada kombinasi fungsi aktivasi layar<br />tersembunyi sigmoid biner dan fungsi aktivasi output sigmoid bipolar<br />dengan rentang data transformasi (0,1) untuk prakiraan harga terna<br />akarwangi. Sedangkan untuk prakiraan harga minyak akarwangi didapat<br />pada fungsi aktivasi layar tersembunyi sigmoid bipolar dan fungsi aktivasi<br />output sigmoid biner dengan rentang data (0,1). Hasil prakiraan harga<br />menunjukkan harga rata-rata terna akarwangi dan harga rata-rata minyak<br />akarwangi untuk tahun 2007 dan 2008 masih di atas harga titik impas<br />usahatani maupun usaha agroindustri minyak akarwangi.<br />Kata kunci : Akarwangi, Vetiveria zizanioides L., harga, prakiraan,<br />jaringan syaraf tiruan, Jawa Barat<br />ABSTRACT<br />Vetiver oil prices forecasting with artificial neural<br />network method<br />Vetiver and vetiver oil prices forecasting with artificial neural<br />network method has been done. Time series data from January 2000 to<br />August 2006 was used to forecast the prices for 24 months ahead. The best<br />result for forecasting of vetiver prices was gotten using sigmoid binary<br />activation in hidden layer, sigmoid bipolar activation in output layer and<br />transformation data spread (0,1). The best result for forecasting of vetiver<br />oil prices was gotten using sigmoid bipolar activation in hidden layer,<br />sigmoid binary activation in output layer and transformation data spread<br />(0,1). The result shows that the average forecasting prices of vetiver and<br />vetiver oil in 2007 and 2008 higher than the prices needed for vetiver<br />farming and vetiver oil agroindustry to reach break event point.<br />Key words: Vetiveria zizanioides L., prices, forecasting, artificial neural<br />network, West Jav

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

scholarly journals Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Narayanan Manikandan ◽  
Srinivasan Subha
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Stock Prices ◽  
Time Series Data ◽  
Financial Analysis ◽  
Parallel Implementation ◽  
Series Data ◽  
Artificial Neural

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

scholarly journals Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds

2009 ◽  
Vol 73 (1-3) ◽  
pp. 49-59 ◽  
Author(s):  
Thomas J. Glezakos ◽  
Theodore A. Tsiligiridis ◽  
Lazaros S. Iliadis ◽  
Constantine P. Yialouris ◽  
Fotis P. Maris ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Time Series Data ◽  
Training Model ◽  
Series Data ◽  
Artificial Neural ◽  
Mountainous Watersheds

scholarly journals A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244094
Author(s):  
Chao-Yu Guo ◽  
Tse-Wei Liu ◽  
Yi-Hau Chen
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Statistical Regression ◽  
Ann Model ◽  
Distributed Lag ◽  
Minimum Number ◽  
Artificial Neural

In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the seriality and lead to a potentially biased outcome. Regarding this issue, a recent study investigated how different types of CV methods influence the predictive errors in conventional time-series data. Here, we examine a more complex distributed lag nonlinear model (DLNM), which has been widely used to assess the cumulative impacts of past exposures on the current health outcome. This research extends the DLNM into an artificial neural network (ANN) and investigates how the ANN model reacts to various CV schemes that result in different predictive biases. We also propose a newly designed permutation ratio to evaluate the performance of the CV in the ANN. This ratio mimics the concept of the R-square in conventional statistical regression models. The results show that as the complexity of the ANN increases, the predicted outcome becomes more stable, and the bias shows a decreasing trend. Among the different settings of hyperparameters, the novel strategy, Leave One Block Out Cross-Validation (LOBO-CV), demonstrated much better results, and the lowest mean square error was observed. The hyperparameters of the ANN trained by the LOBO-CV yielded the minimum number of prediction errors. The newly proposed permutation ratio indicates that LOBO-CV can contribute up to 34% of the prediction accuracy.

scholarly journals APLIKASI PERAMALAN JUMLAH KELAHIRAN DENGAN METODE JARINGAN SYARAF TIRUAN

2018 ◽  
Vol 12 (2) ◽  
pp. 213
Author(s):  
Safira Amudya Nurdela
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Secondary Data ◽  
Control Program ◽  
Series Data ◽  
Neural Network Method ◽  
Network Method ◽  
Artificial Neural Network Method ◽  
Artificial Neural

The  forecast is a statistic analysis to predict what it will happen in the future using the data and information from the past. This research aimed to apply Artificial Neural Network method for estimate the f ertility rate in Surabaya. The study was descriptive which using secondary data providing from Dinas Kesehatan Kota Surabaya. The study used time series data by recapitulation of  fertility rate monthly from 2012-2016. The data analysis used R Program. The result showed the best estimator model for Artificial Neural Network method was 1-3-1 architecture with preprocessing normalized. RMS value of Artificial Neural Network method was 338.1551. The conclusion of this research was the Artificial Neural Network method for estimate the f ertility rate in Surabaya could be used for planning birth control program especially Badan Kependudukan dan Keluarga Berencana Nasional.

scholarly journals Aplikasi Artificial Neural Network (ANN) dalamPeramalan Time Series Data: StudiKasus Lama SinarMatahari

2018 ◽  
pp. 207
Author(s):  
Sulistyarini Sulistyarini
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Time Series Data ◽  
Series Data ◽  
Marriage Ceremony ◽  
Library Research ◽  
Wedding Ceremony ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper discusses wedding ceremony in Central Lombok village of Plambik, which is potential to be a cultural attraction that supports the development of tourism. Marriage ceremony in Plambik has a number of stages, which are not necessarily similar to those customly practiced by other groups of Sasak people. in order to hold a wedding ceremony. This paper aimed to explore merariq tradition which is uniquely held by Sasak community in Plambik.  Data of this research were collected through library research and interviews with Plambik natives. The data were then analyzed by comparing the documentary notes with the actual practices of merariq by Plambik villagers. The finding indicated unique features of merariq stages in Plambik.

A Comparative Study on the Behavior of Islamic and Conventional Stocks in the Presence of Oil Price, Gold Price, and Financial Risk Factors: Evidence from Dow Jones Indices

2019 ◽  
Vol 2 (2) ◽  
pp. 117-128
Author(s):  
Danish Iqbal Godil ◽  
Salman Sarwat ◽  
Muhammad Umer Quddoos ◽  
Muhammad Hanif Akhtar
Keyword(s):  
Time Series ◽  
Financial Risk ◽  
Time Series Data ◽  
Oil Prices ◽  
Gold Price ◽  
Oil Price ◽  
Series Data ◽  
Volatility Index ◽  
West Texas

The research aims to analyze the influence of the gold price, oil price and financial risk on Islamic and conventional securities on comparative as well as on individual bases. Monthly prices of oil and gold are extracted from the websites of West Texas Intermediate and World Gold Council, whereas time series data for financial risk is derived from the Volatility Index of S&P 500.  All these variables are found to be cointegrated at the first difference with both the Dow Jones indices, which means that gold, oil and financial risk have long term association with Islamic and conventional stocks. In order to find the direction and magnitude, this study applied the Newey-West HAC test, which also handles autocorrelation and heteroscedasticity issues in the time series data. The findings of the study suggest that gold prices are positively associated whereas oil prices and financial risk are negatively associated with both types of securities. Though the direction of the nexus is similar for Islamic and conventional stocks, but the magnitude differs especially in case of oil and financial risk. Nevertheless, it can be concluded that there is no diversification prospect between conventional and Islamic stocks under the influence of oil prices, financial risk, and gold prices.

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