2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)最新文献

Hyperspectral Imaging Feature Selection Using Regression Tree Algorithm: Prediction of Carotenoid Content Velvet Apple Leaf 基于回归树算法的高光谱成像特征选择:天鹅绒苹果叶片类胡萝卜素含量预测

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982490

Maulana Ihsan, A. H. Saputro, W. Handayani

{"title":"Hyperspectral Imaging Feature Selection Using Regression Tree Algorithm: Prediction of Carotenoid Content Velvet Apple Leaf","authors":"Maulana Ihsan, A. H. Saputro, W. Handayani","doi":"10.1109/ICICoS48119.2019.8982490","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982490","url":null,"abstract":"Hyperspectral imaging system is an alternative in measuring biological content, especially in plants. Carotenoid content in leaves is one of the ingredients that can be measured using Vis-NIR hyperspectral camera because carotenoids are pigments that are in that range. The combination of spatial and spectral information produces many advantages; one of them is fast measurement time. Spatial and spectral information is extensive data that must be processed in making prediction systems. Spectral information is the wavelength that becomes features in machine learning. A large number of features results in increased computational costs and general rules of machine learning if too many features are used that will result in overfitting. Therefore, this study aims to increase computational costs and reduce overfitting by reducing features not related to the target. The use of supervised learning in selecting features can maintain wavelength information on carotenoid content which the unsupervised method cannot do. The system predicts carotenoid content with MAE and RMSE values obtained at 21.42 and 39.21 using the random forest model with decision tree feature selection.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127133090","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

Application of Sequential Regression Multivariate Imputation Method on Multivariate Normal Missing Data 序列回归多元插值方法在多元正态缺失数据中的应用

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982423

Nurzaman, T. Siswantining, S. Soemartojo, Devvi Sarwinda

{"title":"Application of Sequential Regression Multivariate Imputation Method on Multivariate Normal Missing Data","authors":"Nurzaman, T. Siswantining, S. Soemartojo, Devvi Sarwinda","doi":"10.1109/ICICoS48119.2019.8982423","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982423","url":null,"abstract":"Missing values means the absence of data items for an observation that can result in the loss of certain information. During surveys, there are often missing values or missing data because there are likely respondents who cannot answer the question or do not want to answer the question. One way to handle missing values can be done by imputation, which is the process of filling or replacing missing values in the dataset with possible values based on information obtained in the dataset. This paper will apply the sequential regression multivariate imputation (SRMI) method for imputation of missing values in normal multivariate data. SRMI is a multiple imputation method whose imputation values are obtained from the sequence of regression model, where each variable containing missing values is regressed against all other variables that do not contain missing values as predictor variables. The way to get the value of imputation is to use an iteration approach to draw values from the predictive posterior distribution of the missing values under each successive regression model. the results of the evaluation of imputation quality on simulation data using Root Mean Square Error (RMSE).","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125083806","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

Social Network Analysis of Health Development in Indonesia 印度尼西亚卫生发展的社会网络分析

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982482

A. Wicaksono, Siti Mariyah

{"title":"Social Network Analysis of Health Development in Indonesia","authors":"A. Wicaksono, Siti Mariyah","doi":"10.1109/ICICoS48119.2019.8982482","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982482","url":null,"abstract":"The regular availability of up to date data for evaluating the government's policy is still lack. One of them is data for evaluating development in the health sector. Data are crucial for decision making and policy evaluation. Public perspective or sentiment to each program initiated by the government is a form of evaluation from the citizen. Therefore, we need a technique that can regularly supply data used for decision-maker to measure the success of development programs. Utilization of news can help overcome the cost and time in carrying out updates on development initiated and performed by the government. In this study, we developed an application which can collect health development-related online news, process and analyze them to reflect the public's perspective to the health development programs. Successfully collected 1,204 news articles where 117 articles from detik.com, 661 articles from kompas.com and 426 articles from tempo. co. A total of 500 from 1,204 news articles are used as training data for making Named Entity Recognition models. Sentiment analysis of research results shows that the sentiments of the public to the issue of equitable quality of health services, financial protection, and distribution of drugs and medical personnel are positive. The results of the study indicate that news can be used as an analytical material for evaluating development, the results of which are quite relevant to the results achieved.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125951630","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

Facial Expression Recognition Using Extreme Learning Machine 基于极限学习机的面部表情识别

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982443

Serenada Salma Shafira, Nadya Ulfa, H. A. Wibawa, Rismiyati

引用次数: 7

Analysis of Reliance Factors in The Text, Images and Videos on Social Media 社交媒体中文字、图片和视频的依赖因素分析

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982531

Surjandy, Erick Fernando, Meyliana, Ferrianto Surya Wijaya, T. Swasti, K. Oktriono

{"title":"Analysis of Reliance Factors in The Text, Images and Videos on Social Media","authors":"Surjandy, Erick Fernando, Meyliana, Ferrianto Surya Wijaya, T. Swasti, K. Oktriono","doi":"10.1109/ICICoS48119.2019.8982531","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982531","url":null,"abstract":"Social Media is vigorously personalized for many purposes, ranging from social information to commercial information such as marketing campaign. However, the message in social media is effortless to fabricate and may cause potential loss of the image of a person, product image, or company reputation. In this line, there are three forms of the social media message, i.e. text message form, audiovisual/video message form, and the visual/picture message form. On this background, this study aims to explore the reliance influence factor on the message that disseminates through social media. The result of this preliminary study contributes to future marketing research. However, the contemporary university student is the most active user of social media in Indonesia. The causal/explanatory research in this study functionates to explain the relationship between university student background and communication device used by the student to trust factor of message form in social media. This study involved 388 respondents and revealed 15 essential relationships and a strong influence of trust factor to message form eventually.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125418444","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

Analysis of GPGPU-Based Brute-Force and Dictionary Attack on SHA-1 Password Hash 基于gpgpu的SHA-1密码哈希暴力破解和字典攻击分析

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982390

Laatansa, R. Saputra, B. Noranita

引用次数: 9

Gratification Sought in Gamification on Mobile Payment 游戏化在移动支付中的应用

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982424

Mutia Fadhila Putri, A. Hidayanto, E. S. Negara, N. Budi, P. Utari, Z. Abidin

引用次数: 8

Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data 基于预测均值匹配的数值缺失数据多重插值

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982510

Emha Fathul Akmam, T. Siswantining, S. Soemartojo, Devvi Sarwinda

{"title":"Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data","authors":"Emha Fathul Akmam, T. Siswantining, S. Soemartojo, Devvi Sarwinda","doi":"10.1109/ICICoS48119.2019.8982510","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982510","url":null,"abstract":"Missing data are condition when there are some missing values or empty entries on several observations on data. It could inhibit statistical analysis process and might give a bias conclusion from the analysis if couldn't be handled properly. This problem can be found on some linear regression analysis. One way to handle this problem is using multiple imputation (MI) method named Predictive Mean Matching (PMM). PMM will matching the predictive mean distance of incomplete observations with the complete observations. To get the multiple imputation concept, the predictive mean of incomplete observations were estimated by Bayesian approach while the complete observations were estimated with ordinary least square. Thus, the complete observation that has the closest distance will be a donor value for the incomplete one. Simulation data with two variable (x and y), univariate missing data pattern (on y), and MAR mechanism is used to analyzed the effectiveness of PMM based on relative efficiency estimation result of missing covariate data. Regression analysis used x as independent variable and y as dependent variable. The result showed that PMM give a significant coefficient regression parameter at 5% level of significance and only loss 1 % of relative efficiency.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130019604","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: 8

Prioritizing Determinants of Internet of Things (IoT) Technology Adoption: Case Study of Agribusiness PT. XYZ 物联网(IoT)技术采用的优先决定因素:农业综合企业案例研究

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982442

Sonia Helena Ladasi, M. R. Shihab, A. Hidayanto, N. Budi

{"title":"Prioritizing Determinants of Internet of Things (IoT) Technology Adoption: Case Study of Agribusiness PT. XYZ","authors":"Sonia Helena Ladasi, M. R. Shihab, A. Hidayanto, N. Budi","doi":"10.1109/ICICoS48119.2019.8982442","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982442","url":null,"abstract":"At present, manufacturing industry has the challenge of being able to manage the production chain to be responsive and quickly. In doing so, industry has tried to meet this need by adopting the latest technology of Internet of Things (IoT). This study aims to prioritize the determinants that influence the decision to adopt the Internet of Things technology in one of the agribusiness industries in Indonesia, namely PT. XYZ. This research model was built by combining two theories of information technology adoption, namely technology-organization-environment (TOE) and human-organization-technology (HOT -fit). The research model consists of four main criteria, namely human, technological, organizational, and environmental criteria with 20 factors spread across each criterion. Data collection was done using a questionnaire given to 12 decision makers of PT. XYZ. The data was processed by using Decision-Making Trial and Evaluation and Laboratory (DEMATEL) technique. The conclusion obtained from this study is that human factor becomes the most important criteria when compared to other main criteria. According to human factor, the innovation attitude of the leaders and technical skills of IT staff are the most important factors when compared to other factors. From the technological perspective, the IS / IT infrastructure as well as data security and privacy factors are the most important factors when compared to other factors. On the other side, top management support and perceived technology adoption costs are the most important factors from the perspective of organizational criteria. Finally, the perceived mimetic pressure factor and perceived coercive pressure are the most important factors in environmental perspective.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131747431","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: 4

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982432

Nahda Rosa Ramadhanti, Siti Mariyah

{"title":"Document Similarity Detection Using Indonesian Language Word2vec Model","authors":"Nahda Rosa Ramadhanti, Siti Mariyah","doi":"10.1109/ICICoS48119.2019.8982432","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982432","url":null,"abstract":"Most researches on text duplication in Bahasa uses the TF-IDF method. In this method, each word will have a different weight. The more frequencies the word appears, the greater the weight. This study aims to detect the similarity of documents by calculating cosine similarity from word vectors. The corpus was built from a collection of Indonesian Wikipedia articles. This study proposes two techniques to calculate the similarity which is simultaneous and partial comparison. Simultaneous comparison is direct comparison without dividing documents into several chapters, while partial comparison divides documents into several chapters before calculating the similarity. Similarity result from partial comparison is more accurate than simultaneous comparison. This study uses Unicheck application TF-IDF method as a benchmark. Similarity result from Unicheck and this study are different, due to the different method applied. Similarity result using TF -IDF method is smaller than using Word2vec, this is because TF-IDF can't detect paraphrase. The limitation in this study is that the Unicheck application used as a benchmark does not use the same method as the method used in this study other than that the determination of expected value is still subjective.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131772869","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: 4