2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)最新文献

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Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset 网络钓鱼分类数据不平衡类处理的混合重采样
Yoga Pristyanto, Akhmad Dahlan
{"title":"Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset","authors":"Yoga Pristyanto, Akhmad Dahlan","doi":"10.1109/ICITISEE48480.2019.9003803","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003803","url":null,"abstract":"From the previous work related to web phishing, the researchers overlook the imbalanced class problem on the dataset. theoretically, the majority of classification methods would assume that the nature of the class distribution is balanced. It caused the classification’s performance of the method will be declining. Therefore, the mechanism of imbalanced class handling is severely needed. In our study, One SidedSelection and Synthetic Minority Over-Sampling Technique are used to handle the imbalanced class condition. Those algorithms work to balancing the class distribution of the dataset so that the accuracy and the gmean score of the classification will be enhanced. Based on the result, the combination of those methods (OSS and SMOTE) can enhance the classification’s result significantly either on binary type class and multiclass type dataset. Hence, the combination of OSS and SMOTE can be a plausible option to handle the imbalanced class problem on the web phishing classification either on binary class and multiclass datasets.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115819664","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: 7
Hate Speech Detection on Twitter Using Long Short-Term Memory (LSTM) Method 基于长短期记忆(LSTM)方法的Twitter仇恨言论检测
Syahrul Syafaat Syam, Budhi Irawan, C. Setianingsih
{"title":"Hate Speech Detection on Twitter Using Long Short-Term Memory (LSTM) Method","authors":"Syahrul Syafaat Syam, Budhi Irawan, C. Setianingsih","doi":"10.1109/ICITISEE48480.2019.9003992","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003992","url":null,"abstract":"Along with the development of the times, the use of social media, especially Twitter, is increasingly being used. Of course this makes more people communicate on social media. Due to communication, it is possible that there will be utterances of hate speech delivered to certain parties,, especially before presidential election in 2019. The number of certain parties spread hatred in social media especially on Twitter. Therefore, as technology develops, we create a system that can detect a tweet based on the search for hashtag on Twitter whether it is classified as hate speech or not using the LSTM method as a classifier. The result of this system is to provide a label in the form of “hate speech” or “non-hate speech” on every tweet that becomes an input on this system.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133157341","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: 9
What do Customers Really Need in Ride-Hailing Applications? : Signaling Electronic Service Quality via E-CRM Features 客户对网约车应用的真正需求是什么?:通过E-CRM功能传递电子服务质量
Tifanny Nabarian, Y. G. Sucahyo, Arfive Gandhi, Y. Ruldeviyani
{"title":"What do Customers Really Need in Ride-Hailing Applications? : Signaling Electronic Service Quality via E-CRM Features","authors":"Tifanny Nabarian, Y. G. Sucahyo, Arfive Gandhi, Y. Ruldeviyani","doi":"10.1109/ICITISEE48480.2019.9003778","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003778","url":null,"abstract":"Fierce competition on ride-hailing applications in Indonesia encourages the development of the application that is not only reliable but also has to be customer-oriented. In the context of gig economy, customers consist of clients and gig workers. Lack of customer engagement can reduce competitiveness, images, and profit. In 2017, Indonesian Consumers Foundation exposed that 13 percent ride-hailing customer’s disappointment was caused by the application. This research evaluated Electronic Customer Relationship Management (E-CRM) features in ride-hailing applications, and then found out if those affect electronic service quality (ESERVQUAL); in other words, it is about customers’ perception. Data analysis was conducted using Partial Least Square Structural Equation Modelling (PLS-SEM) method with the total number of valid responses processed was 204 respondents. The results implied that navigation, privacy and security, online community, and customer service are the E-CRM features that are really needed by the customers. By knowing the results, the researchers want to show the asymmetry signal between the providers and customers’ perception. Thus, it can lead providers to develop better E-CRM features in ride-hailing application and conquering the customers’ dissatisfactions in the context of gig economy.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"77 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123269569","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
Mining Student Feedback to Improve the Quality of Higher Education through Multi Label Classification, Sentiment Analysis, and Trend Topic 通过多标签分类、情感分析和趋势话题挖掘学生反馈以提高高等教育质量
Calandra Alencia Hariyani, Achmad Nizar Hidayanto, Nur Fitriah, Z. Abidin, Theresia Wati
{"title":"Mining Student Feedback to Improve the Quality of Higher Education through Multi Label Classification, Sentiment Analysis, and Trend Topic","authors":"Calandra Alencia Hariyani, Achmad Nizar Hidayanto, Nur Fitriah, Z. Abidin, Theresia Wati","doi":"10.1109/ICITISEE48480.2019.9003818","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003818","url":null,"abstract":"This research carried out the label aspect classification, sentiment analysis, and topic trends on the Open-Ended Question (OEQ) section for Student Feedback Questionnaire (SFQ). Multi-Class aspect label classification for SFQ will choose the best classification model by comparing the results of the evaluation of accuracy, precision, recall, and Flscore for each feature combination and comparison of four classification algorithms namely Decision Tree (DT), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results of this research are Classification Techniques using a combination of features of TFIDF, Unigranb and Bigram with the SVM algorithm which is the best Multi-Class classification model for labeling SFQ aspects. In addition, the SentiStrenghtID algorithm used to get sentiments and also the LDA (Latent Dirichlet Allocation) used to get annual topic trends on each survey aspect label. The findings can help Higher Education to support decision making in taking proactive actions towards improvement for self-evaluation and quality.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114836073","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
Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process 提高网络钓鱼检测过程分类性能的元算法
A. F. Nugraha, Luthfia Rahman
{"title":"Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process","authors":"A. F. Nugraha, Luthfia Rahman","doi":"10.1109/ICITISEE48480.2019.9003952","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003952","url":null,"abstract":"Web phishing is one of the many crimes that occur in cyberspace and often threatens internet users around the world. Web phishing works by tricking the victim into a website page that has been designed to resemble the original page and then directing the target to submit the important information they have. Web phishing detection system needs to be developed to minimize attacks and theft of information using the website. Research related to web phishing detection system has been carried out by many researchers, one of them using data mining techniques, but still uses a single classification algorithm. Therefore, the addition of meta-algorithm is proposed to support the improvement of classification performance for the development of various web phishing detection systems. From the testing phase that conducted using Web Phishing dataset from UCI Machine Learning Repository, an increase in accuracy value of 97.1% is obtained by the addition of the bagging process, 97.3% by using the boosting process, and 97.5% by the addition of the stacking process. With the resulting improved performance, it is hoped that the model can be used as a reference in perfecting the development of various phishing web detection systems.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130811646","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
ICITISEE 2019 Sponsors ICITISEE 2019赞助商
{"title":"ICITISEE 2019 Sponsors","authors":"","doi":"10.1109/icitisee48480.2019.9003811","DOIUrl":"https://doi.org/10.1109/icitisee48480.2019.9003811","url":null,"abstract":"","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129732302","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
Implementation of 2DPCA and SOM Algorithms to Determine Sex According to Lip Shapes 根据唇形确定性别的2DPCA和SOM算法的实现
M.Kom Norhikmah, S.Kom Haris Angriawan
{"title":"Implementation of 2DPCA and SOM Algorithms to Determine Sex According to Lip Shapes","authors":"M.Kom Norhikmah, S.Kom Haris Angriawan","doi":"10.1109/ICITISEE48480.2019.9003820","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003820","url":null,"abstract":"Lips are part of body parts in facial area which are unique in shapes and may have different patterns. Pattern or shape of lips can be used as an individual identity, either for a live or deceased person. Forensic science is a branch of medical science which utilizes lip shape to determine the identity of a person, including their sex. Determination of sex using image of lip shape using 2DPCA and SOM algorithms was successful in classifying the sex of a person, as male or female, using desktop-based Java programming language. This study employed 90 lip images, consisting of 60 training data (30 of male and 30 of female) and 30 testing data of male and female. The testing result showed accuracy rate of 76,66% with a rate of 0,9 and maximum iteration of 10000.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"319 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124522013","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
User Satisfaction Levels Sentiment Analysis Toward Goods Delivery Service On Twitter Using Support Vector Machine Algorithm (SVM) 基于支持向量机算法的推特用户送货服务满意度情绪分析
A. Mayasari, A. D. Hartanto
{"title":"User Satisfaction Levels Sentiment Analysis Toward Goods Delivery Service On Twitter Using Support Vector Machine Algorithm (SVM)","authors":"A. Mayasari, A. D. Hartanto","doi":"10.1109/ICITISEE48480.2019.9003835","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003835","url":null,"abstract":"The data amount is experiencing rapid growth in this era. Data can be in the form of text, images, sound or video. Social media has become one of the factors of data growth. Every person can express their feelings on social media from these opinions can be analyzed. In this study sentiment analysis uses the support vector machine algorithm. The first step is crawling data using the Twitter API with keywords. After collecting data, the preprocessing process is carried out, after the preprocessing process the feature is retrieved for each tweet, the features obtained are then collected into a feature list. The feature list is then transformed into a feature vector in binary form and then transformed using the TF-IDF method. The dataset consists of 2 data, namely training and testing. The training is labelled manually. To test the performance of the algorithm used the K-Fold Cross Validation method. The test results are obtained an average accuracy of 98% with the composition of training data and testing data. From these results, the Support Vector Machine method can be used for sentiment classification of JNE twitter data.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126552856","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
ICITISEE 2019 Author Index icitissee 2019作者索引
{"title":"ICITISEE 2019 Author Index","authors":"","doi":"10.1109/icitisee48480.2019.9004013","DOIUrl":"https://doi.org/10.1109/icitisee48480.2019.9004013","url":null,"abstract":"","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132186128","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
Job Seeker Profile Classification of Twitter Data Using the Naïve Bayes Classifier Algorithm Based on the DISC Method 基于DISC方法的Naïve Bayes分类器对Twitter数据求职者简介进行分类
A. D. Hartanto, Ema Utami, Sumarni Adi, Harish Setyo Hudnanto
{"title":"Job Seeker Profile Classification of Twitter Data Using the Naïve Bayes Classifier Algorithm Based on the DISC Method","authors":"A. D. Hartanto, Ema Utami, Sumarni Adi, Harish Setyo Hudnanto","doi":"10.1109/ICITISEE48480.2019.9003963","DOIUrl":"https://doi.org/10.1109/ICITISEE48480.2019.9003963","url":null,"abstract":"Human resource staff in a company is a person in charge of finding new workers. To get a qualified new workforce, a human resource staff must be selective toward the appliance in terms of ability and personality. This study provides an alternative perspective for a human resource in getting one’s personality data through their tweets on a Twitter account. This study uses the Naive Bayes Classifier algorithm with W-IDF (Weighted-Inverse Document Frequency) weighting to classify the personality of recruits into one of DISC’s personality theories, namely Dominance, Influence, Steadiness, and Compliance. By using training data and test data as many as 120 personal Twitter accounts and labelling of words that have been verified by psychologists, obtained personality distribution. The classification of the tweet data is Dominance 90 accounts, Influence 10 accounts, Steadiness 8 accounts and Compliance 12 accounts. Evaluation of the accuracy level of 36.67%.","PeriodicalId":380472,"journal":{"name":"2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127923023","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
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