Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970735
A. W. Sugiyarto, A. Abadi
At present, the plantation sector is one of the biggest contributors to Indonesia’s Gross Domestic Product (GDP). However, due to fluctuating annual production of Indonesian palm oil, the government is confused in determining palm oil import or export policies. Therefore, a good method is needed to predict Indonesian palm oil production. Soft computing can be used for classification and prediction. Soft computing is a model approach to compute by mimicking the ability of extraordinary human reason to reason and learn in environments that have uncertainty and inaccuracy. Some techniques in soft computing include fuzzy systems, artificial neural networks, evolutionary algorithms, and probabilistic reasoning. One method in Artificial Neural Network is Recurrent Neural Network (RNN). RNN is that the network contains at least one feed-back connection, so the activations can flow round in a loop. In the last few years, the RNN network model has been developed, namely by using the Long Short-Term Memory (LSTM) layer. By using the LSTM layer, the RNN learning process gets better. Therefore, in this study the prediction of palm oil production using the LSTM-RNN method is based on the time series data from 1970 to 2017. The results of this study are found that the LSTM-RNN method is very well used for predictions because it produces MAPE of 2.7098% for training data and 2.9861% for testing data compared to other prediction methods.
{"title":"Prediction of Indonesian Palm Oil Production Using Long Short-Term Memory Recurrent Neural Network (LSTM-RNN)","authors":"A. W. Sugiyarto, A. Abadi","doi":"10.1109/AiDAS47888.2019.8970735","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970735","url":null,"abstract":"At present, the plantation sector is one of the biggest contributors to Indonesia’s Gross Domestic Product (GDP). However, due to fluctuating annual production of Indonesian palm oil, the government is confused in determining palm oil import or export policies. Therefore, a good method is needed to predict Indonesian palm oil production. Soft computing can be used for classification and prediction. Soft computing is a model approach to compute by mimicking the ability of extraordinary human reason to reason and learn in environments that have uncertainty and inaccuracy. Some techniques in soft computing include fuzzy systems, artificial neural networks, evolutionary algorithms, and probabilistic reasoning. One method in Artificial Neural Network is Recurrent Neural Network (RNN). RNN is that the network contains at least one feed-back connection, so the activations can flow round in a loop. In the last few years, the RNN network model has been developed, namely by using the Long Short-Term Memory (LSTM) layer. By using the LSTM layer, the RNN learning process gets better. Therefore, in this study the prediction of palm oil production using the LSTM-RNN method is based on the time series data from 1970 to 2017. The results of this study are found that the LSTM-RNN method is very well used for predictions because it produces MAPE of 2.7098% for training data and 2.9861% for testing data compared to other prediction methods.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125350247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/aidas47888.2019.8970711
{"title":"[Copyright notice]","authors":"","doi":"10.1109/aidas47888.2019.8970711","DOIUrl":"https://doi.org/10.1109/aidas47888.2019.8970711","url":null,"abstract":"","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123399085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970945
A. Shabri, R. Samsudin
The majority of tourism demand time series show patterns in terms of seasonal, cyclical and trend components, leading to low accuracy in medium and long-term data forecasting. In order to solve this problem, this paper presents an improved grey model (IGM) based on a re-shaped time series and a genetically optimized method. The monthly arrivals of tourists to Langkawi Island in Malaysia between January 2004 and December 2016 were used to verify the efficiency of the optimized model in anticipating the demand for tourism. The results show that the proposed model achieves better forecasting accuracy on the data with increasing trend, seasonal and cyclical patterns.
{"title":"Application of Improved GM(1,1) Models in Seasonal Monthly Tourism Demand Forecast","authors":"A. Shabri, R. Samsudin","doi":"10.1109/AiDAS47888.2019.8970945","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970945","url":null,"abstract":"The majority of tourism demand time series show patterns in terms of seasonal, cyclical and trend components, leading to low accuracy in medium and long-term data forecasting. In order to solve this problem, this paper presents an improved grey model (IGM) based on a re-shaped time series and a genetically optimized method. The monthly arrivals of tourists to Langkawi Island in Malaysia between January 2004 and December 2016 were used to verify the efficiency of the optimized model in anticipating the demand for tourism. The results show that the proposed model achieves better forecasting accuracy on the data with increasing trend, seasonal and cyclical patterns.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126510226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/AiDAS47888.2019.8970722
A. Azizan, Nurul Najwa SK Abdul Jamal, M. N. Abdullah, Masurah Mohamad, N. Khairudin
Sentiment analysis is a computational process to identify and classify subjective information such as positive, negative and neutral from the source material. It is able to extract feeling and emotion from a piece of a sentence. This technology has been widely used to extract valuable information from people’s views on social media. Hence, this project aims to classify movie reviews into positives, negatives and neutral polarity using lexicon-based method which used R as the language and development framework. Twitter data is used as the source material. Firstly, tweets were extracted using RStudio and Twitter API. Then data pre-processing was done by removing all the stop words and noises. Next was the tokenization process, which separates the words and matches the separated words with positive and negative words vocabulary. Finally, the result of the sentiment analysis is produced into positive, negative and neutral polarities. The results were evaluated using standard evaluation metrics that are the precision, recall, F1 score and accuracy. After all, it is found that the basic lexicon-based method is able to classify sentiment quite well with 52% accuracy. Apparently, the accuracy value achieved in our experiment is not impressive enough, but it is worth corresponding to the simplicity and minimal cost of development for sentiment analysis on Twitter data for movies.
{"title":"Lexicon-Based Sentiment Analysis for Movie Review Tweets","authors":"A. Azizan, Nurul Najwa SK Abdul Jamal, M. N. Abdullah, Masurah Mohamad, N. Khairudin","doi":"10.1109/AiDAS47888.2019.8970722","DOIUrl":"https://doi.org/10.1109/AiDAS47888.2019.8970722","url":null,"abstract":"Sentiment analysis is a computational process to identify and classify subjective information such as positive, negative and neutral from the source material. It is able to extract feeling and emotion from a piece of a sentence. This technology has been widely used to extract valuable information from people’s views on social media. Hence, this project aims to classify movie reviews into positives, negatives and neutral polarity using lexicon-based method which used R as the language and development framework. Twitter data is used as the source material. Firstly, tweets were extracted using RStudio and Twitter API. Then data pre-processing was done by removing all the stop words and noises. Next was the tokenization process, which separates the words and matches the separated words with positive and negative words vocabulary. Finally, the result of the sentiment analysis is produced into positive, negative and neutral polarities. The results were evaluated using standard evaluation metrics that are the precision, recall, F1 score and accuracy. After all, it is found that the basic lexicon-based method is able to classify sentiment quite well with 52% accuracy. Apparently, the accuracy value achieved in our experiment is not impressive enough, but it is worth corresponding to the simplicity and minimal cost of development for sentiment analysis on Twitter data for movies.","PeriodicalId":227508,"journal":{"name":"2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127406323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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