2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)最新文献_第5页

Genetic algorithm optimization for extreme learning machine based microalgal growth forecasting of Chlamydomonas sp 基于极限学习机的衣藻微藻生长预测遗传算法优化

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415189

Dwi M. J. Purnomo, S. C. Purbarani, A. Wibisono, Dian Hendrayanti, Anom Bowolaksono, P. Mursanto, D. H. Ramdhan, W. Jatmiko

{"title":"Genetic algorithm optimization for extreme learning machine based microalgal growth forecasting of Chlamydomonas sp","authors":"Dwi M. J. Purnomo, S. C. Purbarani, A. Wibisono, Dian Hendrayanti, Anom Bowolaksono, P. Mursanto, D. H. Ramdhan, W. Jatmiko","doi":"10.1109/ICACSIS.2015.7415189","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415189","url":null,"abstract":"Currently, microalgae cultivation is one of the most promising alternative solutions to alleviate the value of CO2 concentration. Microalgae growth rate is convinced to be the indicator to measure the effectiveness in capturing CO2. In this paper, the microalgal growth behavior by means of various pH concentrations is observed. From the observation data, the growth behavior is modeled by regression graphs using single hidden layer feed-forward network (SLFN). To train and test the data, extreme learning machine (ELM) algorithm is applied. Recently, ELM is approved to be the fastest algorithm to learn an SLFN for regression. ELM is also well-known for its high learning accuracy as various activation functions can be applied in hidden layer. Yet the over-fitting in regression is still an issue in ELM. Thus to alleviate this problem cross-validation method is employed. To optimize the algorithm, ELM is also combined with Genetic Algorithm. The result shows that regression using ELM-GA is more accurate than ELM in various numbers of neurons.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"123 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120891077","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}

引用次数: 6

Optimization process of glycerol esterification using real time adaptive control 实时自适应控制优化甘油酯化过程

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415172

I. A. Soenandi, A. Suryani, Taufik Djatna, Irzaman

{"title":"Optimization process of glycerol esterification using real time adaptive control","authors":"I. A. Soenandi, A. Suryani, Taufik Djatna, Irzaman","doi":"10.1109/ICACSIS.2015.7415172","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415172","url":null,"abstract":"The synthesis reaction used in esterification needs high energy consumption and a precise processing time to get the best yield of target. In this study, a model was formulated to optimize glycerol esterification process by minimizing the time needed for the process and maximizing the yield of Mono-glycerides. This optimization has gained importance for boosting the esterification industry and improving the production efficiency. Optimization through adaptive monitoring and control has provided significant advances in the process efficiency, a lower energy consumption and a better product quality. This paper presents the optimization with a computational algorithm in real time and adaptive control (RTAC), as compared to the conventional (traditional) methods to monitor and control of glycerol esterification processes. The identification of esterification status based on temperature and time are evaluated to strengthen the optimization. An adaptive method as feature selection to select wavelength IR sensors at specified intervals was carried out with Relief algorithm and Adaptive Pillar K-means clustering method to set the parameter control was proposed in this paper. Many combinations were evaluated from real time condition process, to achieve the best optimization results. The experimental results demonstrate that real time adaptive control can be achieved by using three clusters, which are heating up, stabilizing and finishing. In RTAC, each cluster has its own parameter to set the control point by the servo motor that was attached to magnetic stirrer-heater. By using optimization parameter for each cluster, esterification process time can be shortened by 15-20 minutes with a higher yield (7% or more), lower range stirrer rotation (300rpm-450 rpm) and a lower final temperature of 200°C-210°C.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128352732","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}

引用次数: 1

Stock price prediction using linear regression based on sentiment analysis 基于情绪分析的线性回归股票价格预测

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415179

Yahya Eru Cakra, Bayu Distiawan Trisedya

{"title":"Stock price prediction using linear regression based on sentiment analysis","authors":"Yahya Eru Cakra, Bayu Distiawan Trisedya","doi":"10.1109/ICACSIS.2015.7415179","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415179","url":null,"abstract":"Stock price prediction is a difficult task, since it very depending on the demand of the stock, and there is no certain variable that can precisely predict the demand of one stock each day. However, Efficient Market Hypothesis (EMH) said that stock price also depends on new information significantly. One of many information sources is people's opinion in social media. People's opinion about products from certain companies may determine the company's reputation and thus affecting people's decision to buy the stock of the company. When using opinion as primary data, it is necessary to make a suitable analysis of it. A famous example using opinion as data is sentiment analysis. Sentiment analysis is a process to determine emotion/feeling within people opinion about something, in this case products of some companies. There are some researches about sentiment analysis used to predict the stock prices. Bollen on his research concludes that people opinion on social media such as Twitter can predict DJIA value with 87.6% accuracy. This shows that there is a relation between sentiment analysis and stock prices. Our purpose on this research is to predict the Indonesian stock market using simple sentiment analysis. Naive Bayes and Random Forest algorithm are used to classify tweet to calculate sentiment regarding a company. The results of sentiment analysis are used to predict the company stock price. We use linear regression method to build the prediction model. Our experiment shows that prediction models using previous stock price and hybrid feature as predictor gives the best prediction with 0.9989 and 0.9983 coefficient of determination.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130481681","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}

引用次数: 90

A two-stage emotion detection on Indonesian tweets 印尼推文的两阶段情绪检测

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415174

Johanes Effendi The, A. Wicaksono, M. Adriani

引用次数: 14

Robust kurtosis projection for multivariate outlier labeling 多变量离群标记的鲁棒峰度投影

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415151

D. Herwindiati, Rahmat Sagara, J. Hendryli

引用次数: 1

Mangifera indica real-time quality classifications using codebook segmentation and mass-size correlation equations 基于码本分割和质量大小相关方程的芒果质量实时分类

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415175

Timotius Devin, Muhammand Asyhar Agmalaro

{"title":"Mangifera indica real-time quality classifications using codebook segmentation and mass-size correlation equations","authors":"Timotius Devin, Muhammand Asyhar Agmalaro","doi":"10.1109/ICACSIS.2015.7415175","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415175","url":null,"abstract":"Indonesia as a tropical country, has high rate production of mangoes and it's potentially rise up the country income. Unfortunately, the Indonesian mangoes export rate are poor. One of the reason is damage caused by mechanical post-production sortation. The digital sortation using codebook segmentation model and mass-size correlation equations are able to reduce the damage that caused by mechanical sortation. This research purposes are to do codebook segmentation model and mass-size correlation equations and digitally sort the mango by its mass approximation. The data that used in this research are the maximum length, width, and thickness of mango. Those measured values are required to approximate values of mango mass using the mass-size correlation equations. The approximate mass will be classified into three classes according to the Indonesian National Standard. This program able to classified the mangoes with 95.83% of average accuracy, but the MSE value of each classified mangoes (Class 3 and 4 based on SNI) are 1091.619344 and 61.75204226. Overall, the codebook algorithm is able to recognize the object and count the desirable measure even though the program still recognize shadow as an object.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129392485","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}

引用次数: 0

A monsoon onset and offset prediction model using backpropagation and moron method: A case in drought region 基于反向传播和莫伦法的季风开始和偏移预测模型——以干旱地区为例

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415164

Syeiva Nurul Desylvia, Taufik Djatna, A. Buono

{"title":"A monsoon onset and offset prediction model using backpropagation and moron method: A case in drought region","authors":"Syeiva Nurul Desylvia, Taufik Djatna, A. Buono","doi":"10.1109/ICACSIS.2015.7415164","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415164","url":null,"abstract":"First day (onset) and last day (offset) of monsoon are nature phenomena which are important elements at cultivation stages in agriculture. These 2 sets of time value influent harvest performance but it is difficult to predict onset and offset at drought region. One of technique that can be used to solve mentioned problem is prediction technique which is one of data mining task. In this research, Feed Forward Backpropagation (BPNN) were combined with Moron method to predict onset and offset at drought region. Data used were daily rainfall data from 1983 to 2013. This experiment used 2 kind of BPNN models and they used S different values for learning rate (alpha) from range 0.01 to 0.2. Root Mean Square Error (RMSE) is used to evaluate resulted prediction models along with correlation value and standard deviation of error for better understanding. For BPNN onset model, lowest RMSE value at alpha 0.15 is 32,0546 and lowest RMSE value for BPNN offset is 26,6977 at alpha 0.05. Developed model has been able to use for prediction, but the result was still not close enough to actual data. In order to achieve a better model with lower RMSE, it is neccesary to improve model architecture and to specify some methods to obtain certain number of input layer based on Southern Oscillation Index (SOI) data.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129681155","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}

引用次数: 2

Genetic algorithm based multi-objective optimization of wheat flour supply chain considering raw material substitution 考虑原料替代的基于遗传算法的面粉供应链多目标优化

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415158

Trisna, Marimin Marimin, Y. Arkeman, T. Sunarti

引用次数: 3

Leaf vein segmentation of medicinal plant using Hessian matrix 基于Hessian矩阵的药用植物叶脉分割

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415152

Adzkia Salima, Y. Herdiyeni, S. Douady

引用次数: 14

Dynamics of NYSE correlation structure during global crisis in 2008: Evidence from complex network analysis 2008年全球金融危机中纽交所关联结构的动态:来自复杂网络分析的证据

2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS) Pub Date : 2015-10-01 DOI: 10.1109/ICACSIS.2015.7415196

M. A. Djauhari, G. Lee

引用次数: 1