Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9936006
Suzanna
Augmented reality is developing very rapidly and taking part in human life. Research shows that AR technology provides solutions in many domains ranging from education to military training. In related to folklore, unfortunately there are not many experts or user utilizing AR application in folklore. However, one of the most important aspects of AR in folklore applications is creating the right technique for interaction between users and the content of AR virtual applications. Immersive AR characters can become AR as the perfect tool to tell folklore in 3D and bring to life old stories that happened hundreds of years ago with AR back to the present. There are several different methods of developing folklore, but from the results of previous studies it is known that the 3D Pipeline Production method is the most appropriate method for animation development. Studies in the form of Study Literature Review are also presented in this study. The end result is a step-by-step explanation of the development of folklore in the form of AR using 3D Pipeline Production. This research is expected to provide enthusiasm and motivation for AR developers to create folklore projects with AR so that folklore becomes interesting and does not become extinct over time.
增强现实技术正在迅速发展，逐渐融入到人们的生活中。研究表明，增强现实技术为从教育到军事训练的许多领域提供了解决方案。在民俗方面，遗憾的是，利用AR在民俗中的应用的专家和用户并不多。然而，AR在民间应用中最重要的一个方面是为用户和AR虚拟应用的内容之间的交互创建正确的技术。身临其境的AR角色可以成为AR作为3D讲述民间传说的完美工具，并将数百年前发生的古老故事带回到现在。民间传说有几种不同的开发方法，但从以往的研究结果可知，3D流水线制作方法是最适合动画开发的方法。本研究亦以研究文献回顾(Study Literature Review)的形式进行研究。最终的结果是使用3D管道生产的AR形式的民间传说的发展一步一步的解释。这项研究有望为AR开发者提供热情和动力，用AR创建民俗项目，使民俗变得有趣，不会随着时间的推移而灭绝。
{"title":"Developing Folklore by Utilizing Augmented Reality which Implements The 3D Pipeline Method","authors":"Suzanna","doi":"10.1109/CITSM56380.2022.9936006","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9936006","url":null,"abstract":"Augmented reality is developing very rapidly and taking part in human life. Research shows that AR technology provides solutions in many domains ranging from education to military training. In related to folklore, unfortunately there are not many experts or user utilizing AR application in folklore. However, one of the most important aspects of AR in folklore applications is creating the right technique for interaction between users and the content of AR virtual applications. Immersive AR characters can become AR as the perfect tool to tell folklore in 3D and bring to life old stories that happened hundreds of years ago with AR back to the present. There are several different methods of developing folklore, but from the results of previous studies it is known that the 3D Pipeline Production method is the most appropriate method for animation development. Studies in the form of Study Literature Review are also presented in this study. The end result is a step-by-step explanation of the development of folklore in the form of AR using 3D Pipeline Production. This research is expected to provide enthusiasm and motivation for AR developers to create folklore projects with AR so that folklore becomes interesting and does not become extinct over time.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131237775","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935885
Adrián, Sasmoko, S. R. Manalu, Y. Indrianti
The disruptive era has led to various adaptation efforts so that it has an impact on a shift from the old profession being replaced with a variety of new professions. Therefore, it is necessary to have an instrument that is able to measure the interest and profession of students in accordance with current needs. This study aims to build an e-application based on artificial intelligence to measure Profiling for Aptitude Indicators (P4AI).. The development of e-application is carried out using the waterfall method. The application development model that will be used is Agile with the Scrum model. Scrum is used to develop innovative products or services. The design of this application development will use UML and ERD based on the analyzed features. Algorithm, code, logic, and validation testing of application programs using integration testing This test method will test all modules if combined into one group to see if the program runs well or not. The results of the research are in the form of an e-application that users can use to conduct self-assessment about their interests and professions. Thus, this e-application can help the self-development needed related to the specialization and profession they have.
{"title":"P4AI: E-Application for Researching Student Interests based on Artificial Intelligence","authors":"Adrián, Sasmoko, S. R. Manalu, Y. Indrianti","doi":"10.1109/CITSM56380.2022.9935885","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935885","url":null,"abstract":"The disruptive era has led to various adaptation efforts so that it has an impact on a shift from the old profession being replaced with a variety of new professions. Therefore, it is necessary to have an instrument that is able to measure the interest and profession of students in accordance with current needs. This study aims to build an e-application based on artificial intelligence to measure Profiling for Aptitude Indicators (P4AI).. The development of e-application is carried out using the waterfall method. The application development model that will be used is Agile with the Scrum model. Scrum is used to develop innovative products or services. The design of this application development will use UML and ERD based on the analyzed features. Algorithm, code, logic, and validation testing of application programs using integration testing This test method will test all modules if combined into one group to see if the program runs well or not. The results of the research are in the form of an e-application that users can use to conduct self-assessment about their interests and professions. Thus, this e-application can help the self-development needed related to the specialization and profession they have.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131297723","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935900
Nur Hidayah, Meinarini Catur Utami, Pajri Al Zukri
Islamic boarding schools which are identical to face-to-face learning must be changed to online learning. The perceived problems are the lack of readiness of the teacher in preparing the material, the difficulty of changing the habits of the students, and the difficulty of networking during learning. This study aims to determine the success and the factors that influence the use of information technology during distance learning in Islamic boarding schools. The research object chosen is the Insan Pratama Islamic boarding school. The researcher used the Delone & Mclean (2003) model as the main model by adding the user characteristics variable. The results of this study indicate that 9 out of 11 hypotheses are accepted. Most of the variables used have an influence on the success of using technology in distance learning, except for the relationship between system quality and user satisfaction with a t-test value of -0.004 and the relationship between information quality and system use with a t-test value of 1.812.
{"title":"Analysis of the Use of Information Technology in Distance Learning During a Pandemic Using the IS Success Model in Islamic Boarding School","authors":"Nur Hidayah, Meinarini Catur Utami, Pajri Al Zukri","doi":"10.1109/CITSM56380.2022.9935900","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935900","url":null,"abstract":"Islamic boarding schools which are identical to face-to-face learning must be changed to online learning. The perceived problems are the lack of readiness of the teacher in preparing the material, the difficulty of changing the habits of the students, and the difficulty of networking during learning. This study aims to determine the success and the factors that influence the use of information technology during distance learning in Islamic boarding schools. The research object chosen is the Insan Pratama Islamic boarding school. The researcher used the Delone & Mclean (2003) model as the main model by adding the user characteristics variable. The results of this study indicate that 9 out of 11 hypotheses are accepted. Most of the variables used have an influence on the success of using technology in distance learning, except for the relationship between system quality and user satisfaction with a t-test value of -0.004 and the relationship between information quality and system use with a t-test value of 1.812.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115795375","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935861
R. Fuadi, D. Maylawati, R. Pratama, Akhdan Musyaffa Firdaus, Dea Puminda Sari, Maulana Hamdani, Novia Nurhanivah
Islam is a religion that upholds the dignity of women. Even Islam teaches that heaven is at the feet of a mother. However, in countries where most of the population is Muslim, gender issues persist. Therefore, this article was created to know the factors that most contribute to gender issues in various countries where most of the population is Muslim. These factors are divided into internal factors and external factors. Internal factors include those that are relevant to oneself. External factors include things other than oneself, such as culture. The author uses the K-Means algorithm as the algorithm used to manage the retrieved data. The author uses a collection of survey data found on the Internet about the reactions of men and women to Islam and gender issues in Muslim-dominated countries.
{"title":"Data Clusterization of Muslim Majority Countries to Find Out the Most Factors Causing Gender Issues Using the K-Means Algorithm","authors":"R. Fuadi, D. Maylawati, R. Pratama, Akhdan Musyaffa Firdaus, Dea Puminda Sari, Maulana Hamdani, Novia Nurhanivah","doi":"10.1109/CITSM56380.2022.9935861","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935861","url":null,"abstract":"Islam is a religion that upholds the dignity of women. Even Islam teaches that heaven is at the feet of a mother. However, in countries where most of the population is Muslim, gender issues persist. Therefore, this article was created to know the factors that most contribute to gender issues in various countries where most of the population is Muslim. These factors are divided into internal factors and external factors. Internal factors include those that are relevant to oneself. External factors include things other than oneself, such as culture. The author uses the K-Means algorithm as the algorithm used to manage the retrieved data. The author uses a collection of survey data found on the Internet about the reactions of men and women to Islam and gender issues in Muslim-dominated countries.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124149406","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935889
Y. A. Gerhana, Melani Nurul Nudyawati, D. R. Ramdania, A. Wahana, N. Lukman
Websites at higher education institutions have a strategic role as a medium for delivering practical information and communication. Evaluation of the UI/UX on the website is one way to maintain the effectiveness of the website. This study provides an overview of the UI/UX evaluation on a faculty website at State Islamic University (UIN) Sunan Gunung Djati Bandung. The heuristic evaluation method is used to dig up information and find errors and successes in a website interface design. The Webuse method is used to measure usability about user satisfaction with a website. The respondents of this study were website users, students, and lecturers, totaling 98 respondents. The results of the study indicate that seven aspects of website assessment are lacking. Based on the evaluation of appearance and usability, 17 proposals for improving the website's appearance on desktop and mobile have been made.
高等教育机构的网站作为传递实用信息和交流的媒介具有战略作用。对网站的UI/UX进行评估是保持网站有效性的一种方法。本研究概述了国立伊斯兰大学(un) Sunan Gunung Djati Bandung的教师网站上的UI/UX评估。在网站界面设计中，运用启发式评价方法挖掘信息，发现错误和成功。Webuse方法是用来衡量用户对网站满意度的可用性。本研究的调查对象为网站使用者、学生和讲师，共98人。研究结果表明，网站评估在七个方面存在不足。在外观和可用性评估的基础上，提出了17条改进桌面端和移动端网站外观的建议。
{"title":"Heuristic and Webuse Method to Evaluate UI/UX of Faculty Website","authors":"Y. A. Gerhana, Melani Nurul Nudyawati, D. R. Ramdania, A. Wahana, N. Lukman","doi":"10.1109/CITSM56380.2022.9935889","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935889","url":null,"abstract":"Websites at higher education institutions have a strategic role as a medium for delivering practical information and communication. Evaluation of the UI/UX on the website is one way to maintain the effectiveness of the website. This study provides an overview of the UI/UX evaluation on a faculty website at State Islamic University (UIN) Sunan Gunung Djati Bandung. The heuristic evaluation method is used to dig up information and find errors and successes in a website interface design. The Webuse method is used to measure usability about user satisfaction with a website. The respondents of this study were website users, students, and lecturers, totaling 98 respondents. The results of the study indicate that seven aspects of website assessment are lacking. Based on the evaluation of appearance and usability, 17 proposals for improving the website's appearance on desktop and mobile have been made.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114644899","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9936008
Karli Eka Setiawan, G. N. Elwirehardja, B. Pardamean
Solar Dryer Dome (SDD), an agricultural facility for drying and preserving agricultural products, needs a smart ability to predict the future indoor climate accurately, including indoor temperature and indoor humidity, in order to optimize electricity usage. To overcome these challenges, deep learning has been a widely adopted method. This research aims to forecast the future indoor climate using time series data by implementing a sequence-to-sequence (seq2seq) architecture, which is mostly used in Natural Language Processing (NLP) tasks. The two proposed seq2seq models, Long Short-Term Memory (LSTM) seq2seq and Gated Recurrent Unit (GRU) seq2seq, have proven to be superior to the adapted LSTM and GRU. The results show that the seq2seq GRU model outperforms the adapted GRU baseline model by an average difference of 0.03013 in MAE and the seq2seq LSTM model outperforms the adapted LSTM baseline model by an average difference of 0.00941 in MAE. To the best of our knowledge, this is the first implementation of seq2seq models for indoor climate forecasting on the Room Climate dataset.
{"title":"Sequence to Sequence Deep Learning Architecture for Forecasting Temperature and Humidity inside Closed Space","authors":"Karli Eka Setiawan, G. N. Elwirehardja, B. Pardamean","doi":"10.1109/CITSM56380.2022.9936008","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9936008","url":null,"abstract":"Solar Dryer Dome (SDD), an agricultural facility for drying and preserving agricultural products, needs a smart ability to predict the future indoor climate accurately, including indoor temperature and indoor humidity, in order to optimize electricity usage. To overcome these challenges, deep learning has been a widely adopted method. This research aims to forecast the future indoor climate using time series data by implementing a sequence-to-sequence (seq2seq) architecture, which is mostly used in Natural Language Processing (NLP) tasks. The two proposed seq2seq models, Long Short-Term Memory (LSTM) seq2seq and Gated Recurrent Unit (GRU) seq2seq, have proven to be superior to the adapted LSTM and GRU. The results show that the seq2seq GRU model outperforms the adapted GRU baseline model by an average difference of 0.03013 in MAE and the seq2seq LSTM model outperforms the adapted LSTM baseline model by an average difference of 0.00941 in MAE. To the best of our knowledge, this is the first implementation of seq2seq models for indoor climate forecasting on the Room Climate dataset.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122021874","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935838
Suci Ratnawati, Yusuf Durachman, A. Saputra
Financial Technology is a financial innovation that is growing rapidly in recent years in line with technological developments that continue to advance and provide innovation for the banking sector, one of which is the type of Fintech Payment, such as free interbank money transfer applications, which in practice there are still many problems that make users reluctant to use the innovation. Then, the purpose of this study is to determine the factors that influence user intentions and the use of the Flip free interbank money transfer mobile application by adopting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model and adding two variables, namely Trust and Perceived security. Research data were obtained from 402 users of the Flip free interbank money transfer mobile application in Indonesia through a questionnaire and analyzed using a Structural Equation Model (SEM) approach. Result of this study found that the most influential factors on the intention to use in order from the largest are Price Value, Trust, Performance Expectancy, Social Influence, and Effort Expectancy. While the most influential on its use in order from the largest are Habit, Trust, and Behavioral Intention. The Perceived Security variable was also confirmed to have an influence on Trust on the intention to use and also the use of the Flip free interbank money transfer mobile application. The findings of this study can provide theoretical contributions as well as practical implications for the development of Fintech applications.
{"title":"Analyzing Factors Influencing Intention to Use and Actual Use of Mobile Fintech Applications Free Interbank Money Transfer Flip Using UTAUT 2 Model with Trust and Perceived Security","authors":"Suci Ratnawati, Yusuf Durachman, A. Saputra","doi":"10.1109/CITSM56380.2022.9935838","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935838","url":null,"abstract":"Financial Technology is a financial innovation that is growing rapidly in recent years in line with technological developments that continue to advance and provide innovation for the banking sector, one of which is the type of Fintech Payment, such as free interbank money transfer applications, which in practice there are still many problems that make users reluctant to use the innovation. Then, the purpose of this study is to determine the factors that influence user intentions and the use of the Flip free interbank money transfer mobile application by adopting the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model and adding two variables, namely Trust and Perceived security. Research data were obtained from 402 users of the Flip free interbank money transfer mobile application in Indonesia through a questionnaire and analyzed using a Structural Equation Model (SEM) approach. Result of this study found that the most influential factors on the intention to use in order from the largest are Price Value, Trust, Performance Expectancy, Social Influence, and Effort Expectancy. While the most influential on its use in order from the largest are Habit, Trust, and Behavioral Intention. The Perceived Security variable was also confirmed to have an influence on Trust on the intention to use and also the use of the Flip free interbank money transfer mobile application. The findings of this study can provide theoretical contributions as well as practical implications for the development of Fintech applications.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116838153","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9935887
Nida Hasanati, Qurrotul Aini, Arndini Nuri
Twitter is one of the social media that is widely used where Indonesia occupies the 6th largest Twitter user in the world. This research is a quantitative study on fine-grained sentiment analysis that extracts sentiment with the topic of the covid vaccine from Twitter with the aim of implementing the Support Vector Machine algorithm. The research flow uses the SEMMA method (Sample, Explore, Modify, Model, and Assess). The collection of data sets in the form of tweets crawled from Twitter by utilizing the Twitter API at the sample stage for further exploration of the attributes of the data set at the explore stage. The modify stage is text preprocessing so that the data set is more structured. After that is the model stage which applies the lexicon based method to assign sentiment classes to the data set. Data sets that have labels will be classified using the Naïve Bayes method and the Support Vector Machine. The final stage of the SEMMA method is to assess the method applied using confusion matrix and k-fold Cross Validation. The accuracy results from the Support Vector Machine method, the best parameter results using the CV Grid Search are the rbf kernel with $boldsymbol{C=100}$ and degree = 0.01 resulting in an accuracy of 85%. The accuracy of the implementation of the Support Vector Machine algorithm produces good scores for the Covid-19 vaccine topic, so that the algorithm can be applied to the classification of sentiment analysis on new data.
{"title":"Implementation of Support Vector Machine with Lexicon Based for Sentiment Analysis on Twitter","authors":"Nida Hasanati, Qurrotul Aini, Arndini Nuri","doi":"10.1109/CITSM56380.2022.9935887","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935887","url":null,"abstract":"Twitter is one of the social media that is widely used where Indonesia occupies the 6th largest Twitter user in the world. This research is a quantitative study on fine-grained sentiment analysis that extracts sentiment with the topic of the covid vaccine from Twitter with the aim of implementing the Support Vector Machine algorithm. The research flow uses the SEMMA method (Sample, Explore, Modify, Model, and Assess). The collection of data sets in the form of tweets crawled from Twitter by utilizing the Twitter API at the sample stage for further exploration of the attributes of the data set at the explore stage. The modify stage is text preprocessing so that the data set is more structured. After that is the model stage which applies the lexicon based method to assign sentiment classes to the data set. Data sets that have labels will be classified using the Naïve Bayes method and the Support Vector Machine. The final stage of the SEMMA method is to assess the method applied using confusion matrix and k-fold Cross Validation. The accuracy results from the Support Vector Machine method, the best parameter results using the CV Grid Search are the rbf kernel with $boldsymbol{C=100}$ and degree = 0.01 resulting in an accuracy of 85%. The accuracy of the implementation of the Support Vector Machine algorithm produces good scores for the Covid-19 vaccine topic, so that the algorithm can be applied to the classification of sentiment analysis on new data.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129576340","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}
Pub Date : 2022-09-20DOI: 10.1109/CITSM56380.2022.9936000
I. Subchi, Zulfa Indira Wahyuni, Amelia Zakiyyatun Nufus, Ilham Maulana Amyn, Maulidya Mafaza, R. Adelina
Problems with mental disorders in Indonesia have a percentage of 1 in 5 Indonesian population such as mental disorders, depressive disorders, anxiety disorders, schizophrenia, etc. Research also shows that as many as 1,800 people per year commit suicide in their teens and productive years. However, human resources in dealing with mental health in Indonesia are only 1,053, meaning that 1 professional handles 250 thousand people. This percentage shows that there is a burden on mental health professionals. Today's technological advances, it has helped health workers to treat patients with mental disorders with ease. The riliv application is an online meditation and counseling application with a rate of 4.6/5; more than 500 thousand have downloaded the riliv application. In looking at the effectiveness of this real application, the researcher wants to evaluate the real application using the PIECES. from the results of data processing obtained, real applications have very good quality application development companies that can provide and create quality applications and provide the information needed by users with results on aspects of performance 4.19, information 4.4, economics 4.46, control 4.51, efficiency 4.34, service 4.35.
{"title":"Evaluation of Mental Health Consultation Application Performance using PIECES Method","authors":"I. Subchi, Zulfa Indira Wahyuni, Amelia Zakiyyatun Nufus, Ilham Maulana Amyn, Maulidya Mafaza, R. Adelina","doi":"10.1109/CITSM56380.2022.9936000","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9936000","url":null,"abstract":"Problems with mental disorders in Indonesia have a percentage of 1 in 5 Indonesian population such as mental disorders, depressive disorders, anxiety disorders, schizophrenia, etc. Research also shows that as many as 1,800 people per year commit suicide in their teens and productive years. However, human resources in dealing with mental health in Indonesia are only 1,053, meaning that 1 professional handles 250 thousand people. This percentage shows that there is a burden on mental health professionals. Today's technological advances, it has helped health workers to treat patients with mental disorders with ease. The riliv application is an online meditation and counseling application with a rate of 4.6/5; more than 500 thousand have downloaded the riliv application. In looking at the effectiveness of this real application, the researcher wants to evaluate the real application using the PIECES. from the results of data processing obtained, real applications have very good quality application development companies that can provide and create quality applications and provide the information needed by users with results on aspects of performance 4.19, information 4.4, economics 4.46, control 4.51, efficiency 4.34, service 4.35.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093188","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}
Machine Learning is a technology that is able to study existing data and perform certain tasks according to what data it learns, either text, video, images, or numerical data using supervised learning and unsupversied learning techniques. The Pre-Employment Cards (Kartu Prakerja) is one of the government programs that aims to provide assistance to the Indonesian people, especially those who do not have a job. Based on data from the Central Statistics Agency for 2020–2021 recipients of the pre-employment program as many as 11.4 million recipients, the pre-employment card program received responses from various communities, whether recipients or not, where these opinions included pro-contra opinions on the Pre-Employment Cards (Kartu Prakerja) program. The purpose of this study is to classify the response (sentiment) of the community by using machine learning on pre-employment cards. Sentiment analysis is used to obtain information in the form of opinions (sentiments) based on textual data to determine the public's view of news, service satisfaction, and government policies. The sentiment analysis process is divided into several stages, namely: crawling, data preprocessing, classification, and data visualization. In this study, preprocessing consists of several stages, namely: cleaning, lemmization, stemming, tokenizing, and stopword removal. The method used in this research is a combination of unsupervised learning: Lexicon Based and supervised learning: Support Vector Machine. Textual data weighting is based on data matching against the normalized Lexicon Sentiment with scaling to determine the positive, neutral, and negative sentiment classes. The results of the classification of 940 tweet data obtained 330 positive tweets (35%), 302 negative tweets (32%), and 308 neutral tweets (33%). From the test results on the classification accuracy with the Support Vector Machine, the results obtained an average accuracy of 98.75%, precision 0.98, recall 0.98, and f-measure 0.98 with the conditions for selecting the Cost value in SVM using the help of 10-fold cross validation.
{"title":"Support Vector Machine and Lexicon based Sentiment Analysis on Kartu Prakerja (Indonesia Pre-Employment Cards Government Initiatives)","authors":"Bayu Waspodo, Qurrotul Aini, Fikri Rama Singgih, Rinda Hesti Kusumaningtyas, Elvi Fetrina","doi":"10.1109/CITSM56380.2022.9935990","DOIUrl":"https://doi.org/10.1109/CITSM56380.2022.9935990","url":null,"abstract":"Machine Learning is a technology that is able to study existing data and perform certain tasks according to what data it learns, either text, video, images, or numerical data using supervised learning and unsupversied learning techniques. The Pre-Employment Cards (Kartu Prakerja) is one of the government programs that aims to provide assistance to the Indonesian people, especially those who do not have a job. Based on data from the Central Statistics Agency for 2020–2021 recipients of the pre-employment program as many as 11.4 million recipients, the pre-employment card program received responses from various communities, whether recipients or not, where these opinions included pro-contra opinions on the Pre-Employment Cards (Kartu Prakerja) program. The purpose of this study is to classify the response (sentiment) of the community by using machine learning on pre-employment cards. Sentiment analysis is used to obtain information in the form of opinions (sentiments) based on textual data to determine the public's view of news, service satisfaction, and government policies. The sentiment analysis process is divided into several stages, namely: crawling, data preprocessing, classification, and data visualization. In this study, preprocessing consists of several stages, namely: cleaning, lemmization, stemming, tokenizing, and stopword removal. The method used in this research is a combination of unsupervised learning: Lexicon Based and supervised learning: Support Vector Machine. Textual data weighting is based on data matching against the normalized Lexicon Sentiment with scaling to determine the positive, neutral, and negative sentiment classes. The results of the classification of 940 tweet data obtained 330 positive tweets (35%), 302 negative tweets (32%), and 308 neutral tweets (33%). From the test results on the classification accuracy with the Support Vector Machine, the results obtained an average accuracy of 98.75%, precision 0.98, recall 0.98, and f-measure 0.98 with the conditions for selecting the Cost value in SVM using the help of 10-fold cross validation.","PeriodicalId":342813,"journal":{"name":"2022 10th International Conference on Cyber and IT Service Management (CITSM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126923490","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：