2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)最新文献

{"title":"Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm","authors":"Praise Setiawan Saragih, Deden Witarsyah, F. Hamami, J. Machado","doi":"10.1109/ICADEIS52521.2021.9701961","DOIUrl":"https://doi.org/10.1109/ICADEIS52521.2021.9701961","url":null,"abstract":"When the Large-Scale Social Restrictions (LSSR or PSBB in Indonesian) policy was implemented it the policy was not entirely obeyed by the community which then reaped various opinions and responses on various social media, especially on Twitter. This study aims to conduct a sentiment analysis to find out the cause or phenomena that occur based on the opinions or views of Twitter. The Tweet data about the implementation of LSSR both part 1 and part 2 in Jakarta were obtained as many as 1080 opinions using the crawling method then the data is manually labelled with two labels, which are positive and negative after labelled the data is cleaned after and the data is processed by being weighted using the Bag of Words and TF-IDF extraction feature. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 then classified using the Support Vector Machines algorithm. The final result of this study shows that the classification accuracy results using the Support Vector Machine algorithm with 90:10 data splitting ratio using the TFIDF extraction feature is superior with an accuracy value of 85.185% and F1-Score 72.413%, which is better when compared to the Bag of words extraction feature which produces an accuracy value of 83.333% and F1-Score 66.666%. As for this study, Twitter users tend to give opinions with negative sentiments, which contain complaints and discomfort regarding the implementation of the LSSR policies, both the first LSSR and the second LSSR. Finally, the results of this research are also expected to be input for the government when making better policies in the future.","PeriodicalId":422702,"journal":{"name":"2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132516690","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}

{"title":"Sentiment Analysis on Movie Review using Ensemble Stacking Model","authors":"Muhammad Khaifa Gifari, K. Lhaksmana, P. Mahendra Dwifebri","doi":"10.1109/ICADEIS52521.2021.9702088","DOIUrl":"https://doi.org/10.1109/ICADEIS52521.2021.9702088","url":null,"abstract":"Movie reviews are very essential to people because they can help viewers get an overview of the movie and provide feedback to the movie directors about their movies based on public sentiment. One way to determine whether the sentiment is positive or negative from an opinion can be solved by sentiment analysis. However, analyzing sentiment manually is difficult due to a large amount of data, so it is necessary to implement automation that can make this easier. In this research, we investigate a machine learning method for classifying movie review sentiment. Then Ensemble Stacking model was chosen as a classification method for this sentiment analysis case. In the classification stage, three algorithms (Naïve Bayes, K-Nearest Neighbors, and Logistic Regression) are implemented as the base-learners, then the Logistic Regression algorithm is implemented as a meta-classifier to improve the performance of the base-learners in the Ensemble Stacking model. Therefore, testing and comparison between single classification such as the base-learners with the classification method using Ensemble Stacking are the main focus of this research. The results of this research show that the accuracy of this stacking model reaches 89.40%, an increase of 0.71% from the highest accuracy on the base-learners.","PeriodicalId":422702,"journal":{"name":"2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132753282","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}

{"title":"Measuring and Analyzing E-readiness at Tourist Places in Alamendah Village in Facing Tourism Digitization Using the Technology Readiness Index (TRI) Method","authors":"M. R. Pasha, Rahmat Yasirandi, Dita Oktaria","doi":"10.1109/ICADEIS52521.2021.9701954","DOIUrl":"https://doi.org/10.1109/ICADEIS52521.2021.9701954","url":null,"abstract":"Humans use the development of technology to digitize the business sector, one of which is in the tourism sector. One of the tourism places that has potential is tourism in the village of Alamendah. Tourism in Alamendah village has many tourist points that have the potential to get many visitors because of its natural beauty that is still maintained. However, tourism management in this village has not been maximized because it is still done manually, tourism information is still challenging to find on the internet. Therefore, tourism digitization will be carried out on tourism in the alamendah village. However, before implementing tourism digitization, it is necessary to first measure the readiness of tourism managers in Alamendah Village. Measurement is carried out by e-readiness to determine the level of readiness of tourism managers in the village of Alamendah in digitizing tourism. The measurement of e-readiness in this study uses the Technology Readiness Index (TRI) developed by parasuraman. The variables measured include optimism, innovativeness, discomfort, and insecurity. The research method used is a literature study, questionnaires to 9 managers of tourist attractions in the village of Alamendah. To process the data, the product Pearson moment correlation was used to test the validity and Cronbach alpha to test the reliability of the questionnaire. After the data is tested for validity and reliability, then the TRI value is calculated. The results obtained in this study were 3.81. Based on the TRI category by Parasuraman in 2014, the level of readiness of tourism management in Alamendah village is in the high readiness index category.","PeriodicalId":422702,"journal":{"name":"2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130723480","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}

{"title":"Mediated Solidarity and Community Resilience on Twitter During Covid-19 Pandemic in Indonesia","authors":"Abdul Fadli Kalaloi, Alila Primayanti, Indria Angga Dianita, Gayes Mahestu, Pradipta Dirgantara","doi":"10.1109/ICADEIS52521.2021.9702089","DOIUrl":"https://doi.org/10.1109/ICADEIS52521.2021.9702089","url":null,"abstract":"The COVID-19 pandemic makes it difficult for people to carry out their activities, especially those who work in the informal sector. The lower middle class has experienced obstacles in carrying out work and lost income. Social media is one of the media that mediates solidarity movements between communities in each region in Indonesia. This study aims to explain how Twitter mediates the social solidarity movement amid the COVID-19 pandemic in Indonesia. This research is conducted through Twitter analytics processed using machine learning. The authors collected data between March 1 – December 1 of 2020 and analyzed them using machine learning, including polarity sentiment, emotion sentiment, topic in the word cloud, and social network analysis. The findings show that conversations on Twitter concerning solidarity are not just regular conversations. Mediated solidarity conversations on Twitter can influence another solidarity movement within the same hashtag or word cloud topic that reflects society emotions in supporting each other. A positive sentiment regarding these conversations is also relevant with the SNA, showing no contradictions. All these conversations inspired each other to be strong and unify. These public conversations on Twitter indicate the Indonesian community resilience in facing emergency conditions.","PeriodicalId":422702,"journal":{"name":"2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132935794","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}

{"title":"Design of Sustainable Supply Chain Performance Monitoring System for Construction Material Management: Sustainable Balanced Scorecard – SCOR – ISO 14001 Model","authors":"Made Arya Teguh Dvaipayana, A. Ridwan, B. Santosa","doi":"10.1109/ICADEIS52521.2021.9702023","DOIUrl":"https://doi.org/10.1109/ICADEIS52521.2021.9702023","url":null,"abstract":"The construction sector carries out various business activities, ranging from procurement of construction materials, production of building materials and materials, transportation and distribution of materials and materials, as well as the design of building projects or other infrastructure facilities. Of the various activities in the construction sector, each activity can have an impact on the environment. Sustainability supply chain (SSCM) is a concept in the supply chain process that pays attention to three aspects, namely social, economic, and environmental in carrying out supply chain activities. This SSCM concept integrates these three aspects to obtain a sustainable supply chain process. Evaluation of material management activities, especially social and environmental aspects in the supply chain, can be measured through a supply chain performance sustainability approach. This study uses the Sustainable Balanced Scorecard model to map the supply chain strategy and the Supply Chain Operational Reference model to map business processes and determine metrics and ISO 14001 as a clause for the work environment. Based on the integration of the two models, a performance measurement metric is obtained which is used as a Key Performance Indicator (KPI) in measuring a sustainable supply chain. Based on the perspective of the BSC model, the research in this research uses three BSC perspectives, namely the financial perspective, the internal business process perspective, and the learning and growth perspective. These three perspectives were chosen based on the company’s material management business processes. All company strategies that have been mapped into the previous BSC perspective are remapped with an overview of the relationship between strategies across perspectives. The priority vector shows the weight of each criterion from the matrix. In the financial perspective indicators, weighting is not carried out because the indicators in the financial perspective are not compared to the PCJM questionnaire. The criteria assessment is said to be consistent if the value of the Consistency Ratio is the Consistency Index divided by the Random Index 0.1. Based on the results of the consistency test, it is known that the value of the importance scale is fairly consistent because the entire consistency ratio value is less than 0.1. This research obtains 20 KPIs, which are divided into financial, internal business process and learning & growth perspectives. Based on data collection and processing, the metrics/indicators used are one (1) indicator from the financial perspective, sixteen (16) indicators from the Internal Business Process perspective and three (3) indicators from the Learning & Growth perspective.","PeriodicalId":422702,"journal":{"name":"2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132891667","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}