2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)最新文献_第7页

Missing Value Analysis of Numerical Data using Fractional Hot Deck Imputation 基于分数热甲板法的数值数据缺失值分析

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982412

Samuel Zico Christopher, T. Siswantining, Devvi Sarwinda, Alhadi Bustaman

{"title":"Missing Value Analysis of Numerical Data using Fractional Hot Deck Imputation","authors":"Samuel Zico Christopher, T. Siswantining, Devvi Sarwinda, Alhadi Bustaman","doi":"10.1109/ICICoS48119.2019.8982412","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982412","url":null,"abstract":"One of the solutions of missing value in a survey is imputation. Imputation is a method to replace the missing value with the imputed value from a particular technique, such as mean value, median value, etc. This paper specifically discusses a technique that fuses fractional imputation technique and hot-deck imputation technique. Fractional imputation is popular because this imputation tends to produce lower standard error compared to other methods. Unfortunately, fractional imputation tends to extend the number of observations. Because of the observation extension, sampling becomes a solution to produce less observation. Sampling limits the numbers of imputed values (donor) in the observations by using hot deck imputation nature. The imputation that fuses fractional imputation and hot-deck imputation is known as the fractional hot deck. This paper presents three things about fractional hot deck imputation, first, it shows that the result of fractional hot deck imputation produces fewer donor than fractional imputation, but still has a similar property to fractional imputation that presented in linear regression; Second, it presents an additional information about it's effect on modifying it's k-value in discretization step and the standard error of regression; Third, it presents the comparison of standard errors with fractional imputation, listwise deletion, mean imputation, and median imputation.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"25 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":"125691217","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: 11

Genetic Algorithm-Based Feature Selection and Optimization of Backpropagation Neural Network Parameters for Classification of Breast Cancer Using MicroRNA Profiles 基于遗传算法的特征选择和基于MicroRNA谱的乳腺癌分类反向传播神经网络参数优化

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982530

Amazona Adorada, A. Wibowo

{"title":"Genetic Algorithm-Based Feature Selection and Optimization of Backpropagation Neural Network Parameters for Classification of Breast Cancer Using MicroRNA Profiles","authors":"Amazona Adorada, A. Wibowo","doi":"10.1109/ICICoS48119.2019.8982530","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982530","url":null,"abstract":"Breast cancer is one of the most common types of cancer found in women. Breast cancer mortality increases every year because it has not found an appropriate early detection method. MicroRNA can be used as a potential biomarker, because the profile of the microRNA feature in breast cancer will decrease or increase the value of expression compared to normal conditions. But because of the thousands of types of microRNA that make up breast cancer, a lot of money is needed to detect it entirely. Backpropagation Artificial Neural Network Method has good performance in generalization, so it is suitable to be used as a method for classification with many features. The classification results from the neural network model will be more accurate if the parameters used can be optimized precisely. Genetic algorithms can be used to optimize backpropagation neural network parameters as well as feature selection, because of its global search characteristics. This study aims to compare the performance of backpropagation artificial neural networks optimized parameters as well as feature selection using genetic algorithms (GABPNN_ FS) with backpropagation artificial neural networks optimized using genetic algorithms without feature selection (GABPNN). The results showed that the GABPNN had better results with an error value of 0.016115. But GABPNN_ FS has a faster average process duration of 53.2689 seconds. The best individual chromosome translation results on GABPNN_ FS for breast cancer classification based on microRNA profile are random state = 6098, learning rate = 0.7, number of neuron hidden = 6, and selected features = 707 features that produce accuracy, sensitivity, and specificity ie 97.50 %, 99.00% and 96.00%.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"25 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":"133684997","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

The Effect of Knowledge Management System on Software Development Process with Scrum 知识管理系统对Scrum软件开发过程的影响

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982506

Mochamad Umar Al Hafidz, D. I. Sensuse

{"title":"The Effect of Knowledge Management System on Software Development Process with Scrum","authors":"Mochamad Umar Al Hafidz, D. I. Sensuse","doi":"10.1109/ICICoS48119.2019.8982506","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982506","url":null,"abstract":"Abstraet-Scrum as one of a popular agile frameworks is quite flexible to changes. However, this behavior is accompanied by several shortcomings, especially the documentation section since its dependence on direct communication. Therefore, the use of knowledge management in Scrum seems to be important. Knowledge management in Scrum was influenced by several challenges such as the various locations of the team members, the changes of software developers, the methods for large-scale projects, and the needs for high-quality software. These problems need to be resolved to avoid the amount of knowledge loss. Therefore researchers want to find out more about how knowledge management system influences the development of scrum-based software and its effect on developers' performance. This study uses a quasi-experimental approach with the application in the real world to startup companies. A quasi-experimental is research method used to compare the effect before and after treatment in a group by comparing the performance conditions before and after the implementation of the knowledge management system. The data collected from experiment are scrum artefact, knowledge artefact, and developers' performance (using IWQP). The first experiment for the group lasted for two sprints using a scrum support system. Knowledge management system based scrum used for the second experiment. The findings obtained were an increase in adaptation performance of 8%, contextual performance of 12%, and improvement of knowledge circulation from the development team. The Knowledge Management System is proven to be able to improve and handle the knowledge circulation in Scrum and give impact to software developers.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"21 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":"130532538","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

An Assesment of Knowledge Sharing System: SCeLE Universitas Indonesia 知识共享系统的评估:SCeLE Universitas Indonesia

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982406

Nadya Safitri, N. Pohan, D. I. Sensuse, Deki Satria, Shidiq Al Hakim

引用次数: 0

Application of A Causal Discovery Model to Study The Effect of Iron Supplementation in Children with Iron Deficiency Anemia 应用因果发现模型研究补铁对缺铁性贫血儿童的影响

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982503

F. A. Nugroho, T. Ederveen, A. Wibowo, J. Boekhorst, M. D. de Jonge, T. Heskes

{"title":"Application of A Causal Discovery Model to Study The Effect of Iron Supplementation in Children with Iron Deficiency Anemia","authors":"F. A. Nugroho, T. Ederveen, A. Wibowo, J. Boekhorst, M. D. de Jonge, T. Heskes","doi":"10.1109/ICICoS48119.2019.8982503","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982503","url":null,"abstract":"Most clinical studies are descriptive, measuring different parameters that are associated with certain treatment effects, while causal relations cannot be confirmed. Computational models aid researchers to make predictions of causality and help to focus on the most relevant part of the data. This study used a computational model to find a causal link between iron supplementation effectiveness, RTI, and systemic inflammation parameters, and gut microbiome profiles. We used a causal discovery algorithm on a randomized controlled trial dataset (n=72) of 6 month-old infants from Kenya. We also used correlation and partial correlation to determine the causal effect of any causal link. We found that (1) expression of the Transferrin Receptor (TfR) has a positive causal link with the serum TfR-Ferritin ratio, whereasserumFerritin levels have a negative causal link to TfR expression. (2) C-Reactive Protein (CRP) together with IL-8 and IL-1B have a positive causal relation with IL-6. (3) No causal link between iron supplementation and gut microbiome profile. The first and second result is in accordance with the currentbiological research findings. While the third result shows no causality model, the skeleton might give information for future studies on understanding the gut microbiome profile. Computer modeling helped to uncover causality between clinical parameters in iron deficiency anemia children with iron-micronutrient supplementation. This could lead to more focused studies to better understand the iron supplementation practice as well as the biological mechanism of RTI, gut microbiome alteration, and iron supplementation.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"64 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":"121605039","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

Attribute Selection for Detection of Soybean Plant Disease and Pests 大豆病虫害检测的属性选择

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982483

S. Endah, E. Sarwoko, P. S. Sasongko, R. A. Ulfattah, S. R. Juwita

{"title":"Attribute Selection for Detection of Soybean Plant Disease and Pests","authors":"S. Endah, E. Sarwoko, P. S. Sasongko, R. A. Ulfattah, S. R. Juwita","doi":"10.1109/ICICoS48119.2019.8982483","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982483","url":null,"abstract":"Soybean is one of Indonesia's main commodities that is widely used as a secondary food source because of its protein content. This article compares three attribute selection algorithms, namely Backward Elimination, Forward Selection, and Stepwise Regression with Learning Vector Quantization2 (LVQ2) classifier to detect soybean to avoidance the diseases and pests. Attribute selection is needed at the pre-processing phase of soybean disease data. By selecting relevant data attributes, it is expected that detection accuracy can be maximally generated with minimum computation. The selected attributes are then classified using the LVQ2 method which is a variation of the development of LVQ. LVQ2 has the ability to classify several diseases better than LVQ with the existence of two reference vectors for weight update. The experimental results show that the best parameter for feature selection are p 0.25, a-enter 0.095 and a-remove 0.095 which can reduce the attribute up to 20 attributes with LVQ2 classification accuracy reaching 91%. The results of this accuracy can be obtained through all three selection algorithms.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"74 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":"117326853","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

Welcome Speech from General Chair of ICICoS 2019 ICICoS 2019主席欢迎辞

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/icicos48119.2019.8982479

引用次数: 0

Comparative Experimental Study of Multi Label Classification using Single Label Ground Truth with Application to Field Majoring Problem 基于单标签真值的多标签分类与应用于现场重大问题的对比实验研究

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982464

O. Adikhresna, R. Kusumaningrum, B. Warsito

{"title":"Comparative Experimental Study of Multi Label Classification using Single Label Ground Truth with Application to Field Majoring Problem","authors":"O. Adikhresna, R. Kusumaningrum, B. Warsito","doi":"10.1109/ICICoS48119.2019.8982464","DOIUrl":"https://doi.org/10.1109/ICICoS48119.2019.8982464","url":null,"abstract":"Researches on multi label classification methods generally use training data that already have multi label output as ground truth, but there are real-world problems where it is required to produce multi label prediction output but the available training data only have single label as ground truth. This study compared the performance of various multi label classification methods i.e. Ranking Support Vector Machine (Rank-SVM), Backpropagation for Multi Learning (BP-MLL), Multi Label K-Nearest Neighbor (ML-KNN), and Multi Label Radial Basis Function (ML-RBF) that were trained using multi label training data as intended and which were trained using single label training data. The dataset used in this research is an example of real-world problem, namely the personality-aptitude psychological test results is used to predict suitable majors in vocational high school. The results showed that hamming loss between the two was not far adrift so that it can be concluded that in certain problems, multi label classification methods can train single label and still produce multi label predictions with fairly good accuracy.","PeriodicalId":105407,"journal":{"name":"2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)","volume":"39 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":"126302859","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

Workflow Complexity in Constructive Cost Model II 构建成本模型中的工作流复杂性2

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982515

Sholiq Sholiq, R. Sarno, A. Tjahyanto, A. D. Wulandari

引用次数: 2

Trust and Risk for Measuring OnlineTax Application Acceptance 衡量网上税务申请接受程度的信任与风险

2019 3rd International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2019-10-01 DOI: 10.1109/ICICoS48119.2019.8982473

Wulan Tri Lestari, E. Suharto, P. W. Wirawan, Kabul Kurniawan

引用次数: 1