Abstract-Data originating from some specific fields, for instance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.
Decision Support System is a system that can assist managers in making decisions that are based on the criteria set by the company. Decision Support System helps in the assessment process so it does not happen to be subjective judgment in decision making. Ratings are based on the criteria that have been determined are expected to determine the employee is entitled to a promotion. Many methods can be used in making a decision support system. One method that can be used in making a decision support system that the method Profile Matching. Profile Matching is the process of comparing the profiles of employees with occupational profiles so that the known value of the gap. The smaller the value gap is generated, then the weight value will be even greater gap so that employees the opportunity to get a promotion will be even greater. The results of the calculation with profile matching method in the form of value which ranked based on the largest value. The results of this ranking value will be used as a reference in helping managers to make decisions. Software used in the manufacture of these decision support systems applications with Microsoft Visual Basic 6.0 with MySQL databases.
Bali is one of the favorite tourist attractions in Indonesia, where the number of foreign tourists visiting Bali is around 4 million over 2015 (Dispar Bali). The number of tourists visiting is spread in various regions and tourist attractions that are located in Bali. Although tourist visits to Bali can be said to be large, the visit was not evenly distributed, there were significant fluctuations in tourist visits. Forecasting or forecasting techniques can find out the pattern of tourist visits. Forecasting technique aims to predict the previous data pattern so that the next data pattern can be known. In this study using the technique of recurrent neural network in predicting the level of tourist visits. One of the techniques for a recurrent neural network (RNN) used in this study is Long Short-Term Memory (LSTM). This model is better than a simple RNN model. In this study predicting the level of tourist visits using the LSTM algorithm, the data used is data on tourist visits to one of the attractions in Bali. The results obtained using the LSTM model amounted to 15,962. The measured value is an error value, with the MAPE technique. The LSTM architecture used consists of 16 units of neuron units in the hidden layer, a learning rate of 0.01, windows size of 3, and the number of hidden layers is 1.
The development of technology and information at this time is very fast, one of which is the internet. The ease of digging information through a website encourages state institutions to use the website as a medium of information to the public. The purpose of this research is to build and design E-Gov media information in the DPRD of the Province of Bali which is used as a medium of information and discussion that can be utilized by the community and the council. Website content only displays information about the board such as board fittings with the following sub-sections; Leadership of the Board, Commissions, Bamus, Banggar, Baleg, Honorary Board. As well as allowing website visitors to be able to express aspirations to the board
Investments shares on the Indonesia Stock Exchange is one of the investment with a high rate of return. Stock investment profit greatly influenced by the selection of the right stocks in a portfolio. Analyzing the uncertainty of a stock investor can involve the process of stock selection in group decision which includes investors, investment bankers, analysts, and brokers. Stock selection as a group can produce a stock portfolio with a higher rate of profit than the results of individual decision-making. Implementation of stock selection in group decision support systems (GDSS) used two economic approaches, namely fundamental analysis, and technical analysis. Fundamental analysis uses data financial ratios which have a significant influence on the development of a company's stock. Technical analysis is a stock valuation based on stock movement data time series. This research using AHP, PROMETHEE, and Borda to accommodate the results of shares in group decision making. This research resulted in ranking stocks as a group that can serve as recommendations for investors stock picking.
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