Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks

Peramalan kunjungan wisatawan mancanegara (wisman) sangat penting bagi pemerintah dan industri, karena peramalan menjadi dasar dalam perencanaan kebijakan yang efektif. Penelitian ini menggunakan Generalized Regression Neural Network (GRNN) untuk meramalkan kunjungan wisman menurut 19 pintu masuk utama dan kebangsaan, seperti: Ngurah Rai, Soekarno-Hatta, Batam, Tanjung Uban, Polonia, Juanda, Husein Sastranegara, Tanjung Balai Karimun, Tanjung Pinang, Tanjung Priok, Adi Sucipto, Minangkabau, Entikong, Adi Sumarmo, Sultan Syarif Kasim II, Sepinggan, Sam Ratulangi, Bandara Internasional Lombok, dan Makassar. GRNN memiliki kelebihan tidak memerlukan estimasi jumlah bobot jaringan untuk mendapatkan arsitektur jaringan optimal, sehingga tidak memerlukan pengaturan parameter bebas. Uji coba penelitian dilakukan dengan menggunakan spread dari 0,1 sampai 1,0. Hasil uji coba menunjukkan bahwa kinerja Peramalan terbaik dengan menggunakan spread 0,1 baik untuk data latih maupun data uj

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Improved Shape Parameter Estimation in Pareto Distributed Clutter with Neural Networks

The main problem faced by naval radars is the elimination of the clutter input which is a distortion signal appearing mixed with target reflections. Recently, the Pareto distribution has been related to sea clutter measurements suggesting that it may provide a better fit than other traditional distributions. The authors propose a new method for estimating the Pareto shape parameter based on artificial neural networks. The solution achieves a precise estimation of the parameter, having a low computational cost, and outperforming the classic method which uses Maximum Likelihood Estimates (MLE). The presented scheme contributes to the development of the NATE detector for Pareto clutter, which uses the knowledge of clutter statistics for improving the stability of the detection, among other applications

Prediksi Gerak Nilai Saham BMRI.JK dengan Metode Artificial Neural Network

Abstrak – Investasi dalam bentuk saham menjadi bentuk investasi yang sedang popular pada saat ini. Saham merupakan salah satu alternatif media investasi yang memiliki potensi rasio keuntungan dan kerugian yang lebih besar dibanding media investasi lainnya. Pada kasus ini, kami mempelajari dan mencari pemecahan masalah dalam menghitung naik-turunnya suatu index nilai saham di BMRI.JK (Bank Mandiri Republik Indonesia). Metode yang kami gunakan adalah Artificial Neural Network. Hasil dari penerapan metode ini  berhasil mendapatkan tingkat akurasi mencapai 80.04%.Kata Kunci – Saham, Artificial Neural Network, Propagation, Hidden Neuron, Hidden Layer, Stock Market, Stock Price, Tradin

A Systematic Study of Stock Markets Using Analytical and AI Techniques

Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies. As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction

The political power of twitter

In June 2016, the British voted by 52 per cent to leave the EU, a club the UK joined in 1973. This paper examines Twitter public and political party discourse surrounding the BREXIT withdrawal agreement. In particular, we focus on tweets from four different BREXIT exit strategies known as “Norway”, “Article 50”, the “Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of December 10th 2018 to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results from the Twitter public sentiment indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx. The BREXIT Political party sentiment for the “No Deal” was indicative of short term dynamics on the GBP/EUR Fx at a marginal rate. Our test concluded that there was no causality on the FTSE 100