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Pramunendar, Ricardus Anggi
- A Tourism Arrival Forecasting using Genetic Algorithm based Neural Network
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Authors
Edi Noersasongko
1,
Fenty Tristanti Julfia
1,
Abdul Syukur
1,
Purwanto
1,
Ricardus Anggi Pramunendar
1,
Catur Supriyanto
1
Affiliations
1 Dian Nuswantoro University, Semarang, ID
1 Dian Nuswantoro University, Semarang, ID
Source
Indian Journal of Science and Technology, Vol 9, No 4 (2016), Pagination:Abstract
Background: Tourism industry is very important for a country. Many tourists travel into a country will help and improve its economic growth. Methods: Many researchers used Backpropagation Neural Network (BPNN) for predicting tourist arrivals in a country. As the result, BPNN is proven to give good results, but the accuracy is still less than optimal. This study uses series dataset from the arrival of foreign tourists in the district of Central Java’s: Magelang, Solo, and Wonosobo from 1991 to 2013. We compared the performance of BPNN, K-Nearest Neighbor (KNN) and Multiple Linier Regression (MLR). Genetic Algorithm is used to optimize the parameters of BPNN, such as learning rate, training cycle, and momentum. The performance is measured by Root Mean Square Error (RMSE). Findings: BPNN produces small error of prediction compare to KNN and MLR. KNN performed the worst when used to predict. Improvements: Genetic algorithm proved to be able to optimize the parameters of BPNN. GA is able to minimize the error of the prediction of BPNN.Keywords
Data Mining Algorithm, Forecasting, Genetic Algorithm, Neural Network, Tourism ArrivalFull Text
- Image Segmentation Analysis Based on K-means PSO by Using Three Distance Measures
Abstract Views :202 |
PDF Views:0
Authors
Affiliations
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
1 Informatic Engineering, Universitas Muhammadiyah Purwokerto and Universitas Dian Nuswantoro, ID
2 Informatic Engineering, Universitas Dian Nuswantoro, ID
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1821-1826Abstract
The image segmentation is a technique of image processing which divides image into segments. The many proposed image segmentation techniques, k-Means clustering has been one of the basic image segmentation techniques. The advantages of k-Means are easy calculation, the number of small iteration, and one of the most commonly used clustering algorithm. but, The main problem in this algorithm is sensitive to selection initial cluster center. In this research, we present two approaches method which are used to execute image. It is PSO and k-Means. k-Means integrated with Particle Swarm Optimization (PSO) to improve the accuracy. The purpose of this research to find the effect of PSO towards k-Means in order to get the best selection initial cluster center. This research has been implemented using matlab and taking image dataset from weizzmann institute. The Result of our experiment, we have different result RMSE of k-Means PSO. Euclidean has less RMSE value than Manhattan. The difference RMSE between Euclidean PSO and Manhattan PSO only four point. but if we compare by processing time we have significant difference.Keywords
Vision Computing, Image Processing, Segmentation, Swarm Intelligence, Computer Science.
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