A Tourism Arrival Forecasting using Genetic Algorithm based Neural Network

Noersasongko, Edi; Julfia, Fenty Tristanti; Syukur, Abdul; Purwanto, Purwanto; Pramunendar, Ricardus Anggi; Supriyanto, Catur

doi:10.17485/ijst/2016/v9i4/78722

Cited by 25 publications

(14 citation statements)

References 2 publications

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“…30 100 2 32 68 163 35 120 3 36 56 155 30 100 4 36 45 154 25 90 5 35 80 160 35 125 6 28 62 158 30 100 7 32 65 160 32 100 8 35 73 165 33 100 9 30 67 156 33 100 10 36 80 165 35 110 11 36 85 165 35 115 12 28 45 150 25 95 13 30 67 156 30 100 14 32 75 160 30 110 15 29 77 150 35 110 16 28 53 158 27 95 17 30 48 158 25 90 18 33 70 160 30 98 19 30 57 155 28 95 20 34 50 154 30 95 Multivariate data be normalized so that the output results obtained more accurate. Normalization equations used [9]: Normalized data being calculated into the clustering process using the Fuzzy Clustering Mean (FCM) method with the help of Mathlab. The first FCM process is to determine the number of clusters which be produced first, then, to determine the center of the initial centroid cluster in a random way or by paying attention to the maximum and minimum value of the entire sample data.…”

Section: Results and Analysismentioning

confidence: 99%

“…In calculating BMI, it required two parameters, namely weight (kg) and height (m). BMI can be calculated using the formula [ Fat 30-34, 9 Obesity I 35-39, 9 Obesity II ≥40…”

Section: Body Mass Index (Bmi)mentioning

confidence: 99%

“…Repairing the cluster center and membership value of each data repeatedly will show that the cluster center is moving to the right location. The loop is based on the minimization of objective function [8] [9]. Figure 1 is flowchart FCM process:…”

Section: Fuzzy Clustering Mean (Fcm)mentioning

confidence: 99%

See 2 more Smart Citations

Obesity Grouping For Single Pregnancy Case Study (Posyandu Puyoh Village)

Evanita¹

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Obesity sometimes experienced by most pregnant women who always try to meet the nutritional needs for the fetus unconsciously. Large appetite during pregnancy leads to increased weight of pregnant women. Increased weight gain during pregnancy affects the mother and prospective baby to be born. In this study, obesity grouping for pregnant women in single pregnancy using Fuzzy Cluster Means (FCM). Grouping is based on Body Mass Index (BMI) with sample of 20 pregnant women. BMI data of pregnant women in the form of height, weight, upper arm circle and stomach range. The data are grouped into 4 clusters: fat, obesity I, obesity II, and obesity III. It resulted smaller positive objective values until the 25th iteration. It suggests that the grouping results are excellent and deserve as a reference for grouping the types of obesity in a single pregnancy.

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Section: Results and Analysismentioning

confidence: 99%

“…In calculating BMI, it required two parameters, namely weight (kg) and height (m). BMI can be calculated using the formula [ Fat 30-34, 9 Obesity I 35-39, 9 Obesity II ≥40…”

Section: Body Mass Index (Bmi)mentioning

confidence: 99%

See 1 more Smart Citation

Obesity Grouping For Single Pregnancy Case Study (Posyandu Puyoh Village)

Evanita¹

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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show abstract

“…The methodology based on combination of backpropagation and genetic algorithms can improve a forecasting accuracy, and the parameters of back propagation algorithm are optimized to minimize the prediction error. The proposed methodology is used on series dataset to predict the foreign tourist arrivals in three cities of Indonesia [6]. This is an obvious fact that increasing tourist arrivals in country positively affects the rate of employment in both governmental and private sectors of tourism industry.…”

Section: Introductionmentioning

confidence: 99%

Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019

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Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

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“…The main problem of the BP algorithm is slow training; it requires a long learning time to obtain a result and there are several parameters that need to be adjusted manually, with highest saturation training [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

Al_Duais¹,

Mohamad²

2017

JTIT

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Abstract-The main problem of batch back propagation (BBP) algorithm is slow training and there are several parameters need to be adjusted manually, such as learning rate. In addition, the BBP algorithm suffers from saturation training. The objective of this study is to improve the speed up training of the BBP algorithm and to remove the saturation training. The training rate is the most significant parameter for increasing the efficiency of the BBP. In this study, a new dynamic training rate is created to speed the training of the BBP algorithm. The dynamic batch back propagation (DBBPLR) algorithm is presented, which trains with adynamic training rate. This technique was implemented with a sigmoid function. Several data sets were used as benchmarks for testing the effects of the created dynamic training rate that we created. All the experiments were performed on Matlab. From the experimental results, the DBBPLR algorithm provides superior performance in terms of training, faster training with higher accuracy compared to the BBP algorithm and existing works.Keywords-artificial neural network (ANN), batch back propagation algorithm, dynamic training rate, speed up training, accuracy training.

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A Tourism Arrival Forecasting using Genetic Algorithm based Neural Network

Cited by 25 publications

References 2 publications

Obesity Grouping For Single Pregnancy Case Study (Posyandu Puyoh Village)

Obesity Grouping For Single Pregnancy Case Study (Posyandu Puyoh Village)

Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Dynamically-adaptive Weight in Batch Back Propagation Algorithm via Dynamic Training Rate for Speedup and Accuracy Training

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