2020 4th International Conference on Informatics and Computational Sciences (ICICoS)最新文献_第5页

Usability Testing of Multifinance Mobile Application for End-Customer (Case Study: PT.XYZ) 面向终端用户的多功能金融移动应用的可用性测试(案例研究:PT.XYZ)

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9298977

Zharfan Akbar Andriawan, Ramadhan Pratama, Khadijah

{"title":"Usability Testing of Multifinance Mobile Application for End-Customer (Case Study: PT.XYZ)","authors":"Zharfan Akbar Andriawan, Ramadhan Pratama, Khadijah","doi":"10.1109/ICICoS51170.2020.9298977","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9298977","url":null,"abstract":"Population growth in Indonesia must be accompanied by an increased level of public consumption. With the high level of public consumption, people also need facilities to meet their lifestyle, one of which is vehicle financing or credit facility. One company that can provide this facility is PT. XYZ. To facilitate customers in knowing information related to services provided, PT. XYZ has a mobile application that contains credit simulations, credit application information, etc. In order to ensured that the application is usable for end user, this study conducted usability testing on PT.XYZ mobile application. There are three aspects evaluated in usability testing, that is effectiveness, efficiency, and satisfaction. The first two aspects are measured by Performance-based evaluation, while the third aspect is measured by Questionnaire-based assessment. Usability testing results in this study acquired an application effectiveness score of 95% and an efficiency score of 81.51%. It was also acquired that the satisfaction score was 6.28 on a Likert scale of 7 so we can say that respondents satisfied when using PT. XYZ mobile application.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489741","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

Code-Mixed Sentiment Analysis Using Machine Learning Approach – A Systematic Literature Review 使用机器学习方法的代码混合情感分析-系统文献综述

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299004

C. Tho, H. Warnars, B. Soewito, F. Gaol

{"title":"Code-Mixed Sentiment Analysis Using Machine Learning Approach – A Systematic Literature Review","authors":"C. Tho, H. Warnars, B. Soewito, F. Gaol","doi":"10.1109/ICICoS51170.2020.9299004","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299004","url":null,"abstract":"Code-mixed language is ubiquitous. Having been commonly practiced among bilingual communities, code-mixed language has emerged as a common language among social media users. Despite its popularity, the analysis of a code-mixed text is challenging as the text does not typically comply with the monolingual grammar. Therefore, the popularity of social media in the past ten years has raised wide attention to develop methods for analyzing code-mixed text such as extracting popularity sentiment from the text. Machine learning-based classifier such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression have been widely used to analyze the sentiment. This paper intends to further explore machine learning classifiers, their performances, variables, and most common classifiers for the code-mixed sentiment analysis. Prisma Methodology was used in this paper, extracting 12 from 230 papers that met predefined required criteria, including publication year within the last 5 years. Our findings suggested that the most common classifiers found in the papers were Support Vector Machine, Naïve Bayes, and Logistic Regression. By using the accuracy and F1 as the performance measures, the Support Vector Machine exhibited a better performance compared to Naïve Bayes and Logistic Regression. Thus, this study supported the use of Support Vector Machine, Naïve Bayes and Logistic Regression as the main classifiers for the code-mixed sentiment analysis.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134486035","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: 3

ICICoS 2020 Breaker Page ICICoS 2020断路器页面

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/icicos51170.2020.9298976

引用次数: 0

The Comparison of Feature Selection Methods in Software Defect Prediction 软件缺陷预测中特征选择方法的比较

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299022

Khadijah, Amazona Adorada, P. W. Wirawan, Kabul Kurniawan

{"title":"The Comparison of Feature Selection Methods in Software Defect Prediction","authors":"Khadijah, Amazona Adorada, P. W. Wirawan, Kabul Kurniawan","doi":"10.1109/ICICoS51170.2020.9299022","DOIUrl":"https://doi.org/10.1109/ICICoS51170.2020.9299022","url":null,"abstract":"One of the goal in software testing is to discover software defects before the software is used by customer. Successful software testing leads to high quality software. However, exposing a defect in software testing is very resources consuming. Therefore, an automated software defect prediction is needed. In order to build accurate model for prediction, a relevant subset of features must be carefully determined as an input to the classifier. Therefore, this research compares the performance of feature selection method between a kind of filter method, namely ReliefF and a kind of embedded method, namely SVM-RFE (Support Vector Machine – Recursive Feature Elimination). Those methods are free from the assumption of conditional independence among features. Then, SVM is applied as classification algorithm. Previously, SMOTE (Synthetic Minority Oversampling Technique) is used to balance the training data. The experiments are run on benchmark public dataset, NASA MDP dataset. The experiment results show that SVM-RFE perform better than ReliefF in term of g-mean, while ReliefF perform better SVM-RFE in term of accuracy. However, when using SVM-RFE feature selection, the best classifier performance can be achieved with smaller number of features as compared to ReliefF. Future research may explore ensemble feature selection method as an attempt to improve performance of the resulting classifier, both in g-mean and accuracy.","PeriodicalId":122803,"journal":{"name":"2020 4th International Conference on Informatics and Computational Sciences (ICICoS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133499188","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: 3

Continuance Intention to Use (CIU) on Technology Acceptance Model (TAM) for m-payment (Case Study: TIX ID) 移动支付技术接受模型(TAM)的持续使用意向(CIU)(案例研究:TIX ID)

2020 4th International Conference on Informatics and Computational Sciences (ICICoS) Pub Date : 2020-11-10 DOI: 10.1109/ICICoS51170.2020.9299100

D. Nugraheni, Amabel Hadisoewono, B. Noranita

引用次数: 2