This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).
{"title":"Revolutionizing Digit Image Recognition: Pushing the Limits with Simple CNN and Challenging Image Augmentation Techniques on MNIST","authors":"Khodijah Hulliyah","doi":"10.47738/jads.v4i3.104","DOIUrl":"https://doi.org/10.47738/jads.v4i3.104","url":null,"abstract":"This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN).","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437955","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}
This study aims to identify the factors influencing users' continuance intention to use FinTech payment applications. An online questionnaire was administered to 361 FinTech users during the pandemic using Google Forms to achieve the objective. The Expectation-Confirmation Model (ECM) was extended to include perceived trust, social influence, and functional benefits and was used to analyze the data obtained from the survey. The study results indicate that prior expectation confirmation and perceived usefulness of the application after use are crucial for increasing users' continuance intention to use the service. Additionally, perceived trust and social influence positively influence users' continuance intention to use the service and can be strengthened through personalized experiences and positive interactions. This study provides valuable insights for researchers and practitioners in the field of FinTech payments.
{"title":"Assessing of The Continuance Intentions to Use Fintech Payments, an Integrating Expectation Confirmation Model","authors":"Tubagus Asep Nurdin","doi":"10.47738/jads.v4i3.105","DOIUrl":"https://doi.org/10.47738/jads.v4i3.105","url":null,"abstract":"This study aims to identify the factors influencing users' continuance intention to use FinTech payment applications. An online questionnaire was administered to 361 FinTech users during the pandemic using Google Forms to achieve the objective. The Expectation-Confirmation Model (ECM) was extended to include perceived trust, social influence, and functional benefits and was used to analyze the data obtained from the survey. The study results indicate that prior expectation confirmation and perceived usefulness of the application after use are crucial for increasing users' continuance intention to use the service. Additionally, perceived trust and social influence positively influence users' continuance intention to use the service and can be strengthened through personalized experiences and positive interactions. This study provides valuable insights for researchers and practitioners in the field of FinTech payments.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135437962","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}
This research endeavors to integrate Augmented Reality (AR) technology into the realm of physical education, with a specific emphasis on improving spatial orientation skills among students with learning disabilities. The study pursues three core objectives: (1) To assess the efficacy of utilizing AR-based instructional tools to enhance spatial orientation abilities; (2) To scrutinize the academic advancements of students with learning disabilities post-AR intervention; (3) To gauge the satisfaction levels of these students with the AR-enhanced learning experience. The study cohort comprises nine students with learning disabilities, drawn from an educational institution situated in Pathum Thani Province, Wat Pathum Nayok school, using a targeted sampling methodology. Data is gathered through immersive AR experiences within the context of physical education, with a focus on spatial awareness. The analytical approach encompasses a diverse array of statistical techniques, including percentages, means, and standard deviations. Furthermore, the t-test is deployed to statistically compare pre and post-learning outcomes, maintaining a significance level of α = 0.05. The research outcomes substantiate that AR-driven educational activities in physical education effectively enhance spatial orientation skills among students (E1/E2: 82.40/81.33). Preceding the intervention, students recorded an average score of 8.80 with a standard deviation of 2.33, which significantly escalated to 16.27 with a standard deviation of 1.48 following AR-assisted learning. The t-test underscores the statistically significant disparity (p < 0.05) in scores prior and subsequent to the AR intervention. Furthermore, students with learning disabilities express considerable satisfaction with the application of AR in physical education, with an average satisfaction rating of 4.51. This research carries substantial implications, particularly within the realm of data science, as it pertains to the collection and analysis of data relating to students' educational achievements and satisfaction levels.
{"title":"Incorporating Augmented Reality to Enhance Learning for Students with Learning Disabilities: A Focus on Spatial Orientation in Physical","authors":"Navinee Intarapreecha","doi":"10.47738/jads.v4i3.126","DOIUrl":"https://doi.org/10.47738/jads.v4i3.126","url":null,"abstract":"This research endeavors to integrate Augmented Reality (AR) technology into the realm of physical education, with a specific emphasis on improving spatial orientation skills among students with learning disabilities. The study pursues three core objectives: (1) To assess the efficacy of utilizing AR-based instructional tools to enhance spatial orientation abilities; (2) To scrutinize the academic advancements of students with learning disabilities post-AR intervention; (3) To gauge the satisfaction levels of these students with the AR-enhanced learning experience. The study cohort comprises nine students with learning disabilities, drawn from an educational institution situated in Pathum Thani Province, Wat Pathum Nayok school, using a targeted sampling methodology. Data is gathered through immersive AR experiences within the context of physical education, with a focus on spatial awareness. The analytical approach encompasses a diverse array of statistical techniques, including percentages, means, and standard deviations. Furthermore, the t-test is deployed to statistically compare pre and post-learning outcomes, maintaining a significance level of α = 0.05. The research outcomes substantiate that AR-driven educational activities in physical education effectively enhance spatial orientation skills among students (E1/E2: 82.40/81.33). Preceding the intervention, students recorded an average score of 8.80 with a standard deviation of 2.33, which significantly escalated to 16.27 with a standard deviation of 1.48 following AR-assisted learning. The t-test underscores the statistically significant disparity (p < 0.05) in scores prior and subsequent to the AR intervention. Furthermore, students with learning disabilities express considerable satisfaction with the application of AR in physical education, with an average satisfaction rating of 4.51. This research carries substantial implications, particularly within the realm of data science, as it pertains to the collection and analysis of data relating to students' educational achievements and satisfaction levels.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782634","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}
The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma’s teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma’s teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers’ and students’ satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers’ learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between the pre-test and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students’ learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students’ satisfaction gained a very high level with an average of 4.50, SD.=.44.
{"title":"Data Analytics of Online Lessons in Social Studies and Buddhism: Enhancing Dhamma Teaching and Tripitaka Understanding Among Teachers and Students","authors":"Aammuay Luaensutthi","doi":"10.47738/jads.v4i3.125","DOIUrl":"https://doi.org/10.47738/jads.v4i3.125","url":null,"abstract":"The objectives were to (1) determine the effectiveness of online lessons of Social Studies and Buddhism on Dhamma’s teaching regarding Tripitaka for teachers; (2) compare the pre-test and post-test achievements of teachers and primary school 6 (Grade 6) students; 3) examine the satisfaction of teachers and students using online lessons of Social Studies and Buddhism on Dharma’s teachings according to the Tripitaka. The samples were 12 teachers, and 30 students studying primary school 6 (Grade 6) at Wat Proifon School. The instruments were online lessons of the Social Studies and Buddhism course on Buddha's Teaching Tripitaka, pre-test and post-test, and the questionnaire of teachers’ and students’ satisfaction towards studying the online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding the Tripitaka.Statistics used were percentage, mean, standard deviation, and t-test for dependent samples. The findings revealed that the efficiency of online lessons in the Social Studies and Buddhism course on Buddha's teaching regarding Tripitaka was 81.92/80.83 on average based on the criteria. The teachers’ learning achievements after using online lessons in the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test 11.40, SD.=1.51, while the average score of the post-test was 18.17, SD.=1.10, and the t-test between the pre-test and post-tests was 6.77, which were significantly distinctive at the level of .05., and the students’ learning achievements after using online lessons on the Social studies and Buddhism course on Buddha's teaching regarding the Tripitaka was higher than that of the pre-test: 10.40, SD.=1.61, while the average score of the post-test was 16.17, SD.=1.11, and the t-test between the pre-test and post-tests was 5.77, which were significantly distinctive at the level of .05. Teachers' satisfaction was at high level with an average of 4.47, SD.=.55, and the students’ satisfaction gained a very high level with an average of 4.50, SD.=.44.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782622","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}
This study aims to analyze the multifaceted factors influencing the innovative capabilities of data science professionals in China and assess the impact of simulations on their innovative skills. The sample comprises seventeen experts who actively participated in discussions and provided 36 perspectives on the factors affecting their innovation abilities. The research methodology utilized the Delphi method, involving four rounds of questionnaires distributed to 363 data science professionals to evaluate the factors affecting their innovation capacity. The data was rigorously analyzed using mathematical statistics and SPSS, with a strong emphasis on questionnaire validity and reliability. In the reliability analysis, Cronbach's α was found to be 0.98, indicating a high level of internal consistency. The research results yielded an average score of 4.79, SD = 0.39, IQR = 1, reflecting a strong consensus among experts in agreement with the research findings. Exploratory factor analysis was employed for validity assessment, revealing that the 12th factor accounted for a cumulative variance explanation rate of 76.54%, exceeding the threshold of 60%, signifying the robust structural validity of the questionnaire data. The study also utilized AMOS software to simulate sample data and assess the influence coefficients of individual, organizational, and family characteristics on innovation capacity, resulting in values of 0.53, 0.39, and 0.22, respectively, all greater than 0, indicating favorable influence relationships. Building upon these findings, a comprehensive model of creativity abilities among Chinese data science professionals is proposed. This research critically examines the innovation potential of data science professionals in Chinese academia, with the overarching goal of enhancing their creative skills and competitiveness within the data science field. Additionally, it lays the theoretical groundwork for fostering innovation within the university setting.
{"title":"Assessing Factors and Simulating Innovation: A Study of Innovative Capacities Among Data Science Professionals in China","authors":"Yongfeng Zhang","doi":"10.47738/jads.v4i3.123","DOIUrl":"https://doi.org/10.47738/jads.v4i3.123","url":null,"abstract":"This study aims to analyze the multifaceted factors influencing the innovative capabilities of data science professionals in China and assess the impact of simulations on their innovative skills. The sample comprises seventeen experts who actively participated in discussions and provided 36 perspectives on the factors affecting their innovation abilities. The research methodology utilized the Delphi method, involving four rounds of questionnaires distributed to 363 data science professionals to evaluate the factors affecting their innovation capacity. The data was rigorously analyzed using mathematical statistics and SPSS, with a strong emphasis on questionnaire validity and reliability. In the reliability analysis, Cronbach's α was found to be 0.98, indicating a high level of internal consistency. The research results yielded an average score of 4.79, SD = 0.39, IQR = 1, reflecting a strong consensus among experts in agreement with the research findings. Exploratory factor analysis was employed for validity assessment, revealing that the 12th factor accounted for a cumulative variance explanation rate of 76.54%, exceeding the threshold of 60%, signifying the robust structural validity of the questionnaire data. The study also utilized AMOS software to simulate sample data and assess the influence coefficients of individual, organizational, and family characteristics on innovation capacity, resulting in values of 0.53, 0.39, and 0.22, respectively, all greater than 0, indicating favorable influence relationships. Building upon these findings, a comprehensive model of creativity abilities among Chinese data science professionals is proposed. This research critically examines the innovation potential of data science professionals in Chinese academia, with the overarching goal of enhancing their creative skills and competitiveness within the data science field. Additionally, it lays the theoretical groundwork for fostering innovation within the university setting.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782626","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}
Ensemble Learning is an algorithm that searches for the best prediction result based on several classifier solutions which are come from different algorithms. Ensemble learning has better accuracy and performance compared to other algorithms because this method uses several learning algorithms to achieve better predictive solutions. There are a lot of data that the scholarship organizer receives and manages. This makes the process take a lot of time to do checking process and makes it inefficient. Accelerating the process while also maintaining the accuracy of the scholarship selection process can certainly improve the selection performance. In this study, we process student data from UIN Jakarta University as a sample. The model will have 2 base classifiers, namely Support Vector Machine (SVM) and Key Nearest Neighbor (KNN). Each of these algorithms already has quite a good accuracy. Using Ensemble Learning improves the model performance because it has the ability to overcome errors that occur in each data prediction. We can exploit the classification application created using "Streamlit" and will determine whether a student is accepted or rejected in the scholarship selection process. Our model and application can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.
{"title":"Ensemble learning techniques to improve the accuracy of predictive model performance in the scholarship selection process","authors":"Nurhayati Buslim","doi":"10.47738/jads.v4i3.112","DOIUrl":"https://doi.org/10.47738/jads.v4i3.112","url":null,"abstract":"Ensemble Learning is an algorithm that searches for the best prediction result based on several classifier solutions which are come from different algorithms. Ensemble learning has better accuracy and performance compared to other algorithms because this method uses several learning algorithms to achieve better predictive solutions. There are a lot of data that the scholarship organizer receives and manages. This makes the process take a lot of time to do checking process and makes it inefficient. Accelerating the process while also maintaining the accuracy of the scholarship selection process can certainly improve the selection performance. In this study, we process student data from UIN Jakarta University as a sample. The model will have 2 base classifiers, namely Support Vector Machine (SVM) and Key Nearest Neighbor (KNN). Each of these algorithms already has quite a good accuracy. Using Ensemble Learning improves the model performance because it has the ability to overcome errors that occur in each data prediction. We can exploit the classification application created using \"Streamlit\" and will determine whether a student is accepted or rejected in the scholarship selection process. Our model and application can be used by academics as a Decision Support System (DSS) for determining scholarship recipients. This model can also be used as a development for institutions in the academic field.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782630","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}
This study explores the potential of text analysis and classification techniques to improve the operational efficiency and effectiveness of e-government, particularly within law enforcement agencies. It aims to automate the analysis of textual crime reports and deliver timely decision support to policymakers. Given the increasing volume of anonymous and digitized crime reports, conventional crime analysts encounter challenges in efficiently processing these reports, which often lack the filtering or guidance found in detective-led interviews, resulting in a surplus of irrelevant information. Our research involves the development of a Decision Support System (DSS) that integrates Natural Language Processing (NLP) methods, similarity metrics, and machine learning, specifically the Naïve Bayes' classifier, to facilitate crime analysis and categorize reports as pertaining to the same or different crimes. We present a crucial algorithm within the DSS and its evaluation through two studies featuring both small and large datasets, comparing our system's performance with that of a human expert. In the first study, which encompasses ten sets of crime reports covering 2 to 5 crimes each, the binary logistic regression yielded the highest algorithm accuracy at 89%, with the Naive Bayes' classifier trailing slightly at 87%. Notably, the human expert achieved superior performance at 96% when provided with sufficient time. In the second study, featuring two datasets comprising 40 and 60 crime reports discussing 16 distinct crime types for each dataset, our system exhibited the highest classification accuracy at 94.82%, surpassing the crime analyst's accuracy of 93.74%. These findings underscore the potential of our system to augment human analysts' capabilities and enhance the efficiency of law enforcement agencies in the processing and categorization of crime reports.
{"title":"Adaptive Decision-Support System Model for Automated Analysis and Classification of Crime Reports for E-Government","authors":"Taqwa Hariguna","doi":"10.47738/jads.v4i3.127","DOIUrl":"https://doi.org/10.47738/jads.v4i3.127","url":null,"abstract":"This study explores the potential of text analysis and classification techniques to improve the operational efficiency and effectiveness of e-government, particularly within law enforcement agencies. It aims to automate the analysis of textual crime reports and deliver timely decision support to policymakers. Given the increasing volume of anonymous and digitized crime reports, conventional crime analysts encounter challenges in efficiently processing these reports, which often lack the filtering or guidance found in detective-led interviews, resulting in a surplus of irrelevant information. Our research involves the development of a Decision Support System (DSS) that integrates Natural Language Processing (NLP) methods, similarity metrics, and machine learning, specifically the Naïve Bayes' classifier, to facilitate crime analysis and categorize reports as pertaining to the same or different crimes. We present a crucial algorithm within the DSS and its evaluation through two studies featuring both small and large datasets, comparing our system's performance with that of a human expert. In the first study, which encompasses ten sets of crime reports covering 2 to 5 crimes each, the binary logistic regression yielded the highest algorithm accuracy at 89%, with the Naive Bayes' classifier trailing slightly at 87%. Notably, the human expert achieved superior performance at 96% when provided with sufficient time. In the second study, featuring two datasets comprising 40 and 60 crime reports discussing 16 distinct crime types for each dataset, our system exhibited the highest classification accuracy at 94.82%, surpassing the crime analyst's accuracy of 93.74%. These findings underscore the potential of our system to augment human analysts' capabilities and enhance the efficiency of law enforcement agencies in the processing and categorization of crime reports.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782499","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}
The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.
{"title":"Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology","authors":"Trianggoro Wiradinata","doi":"10.47738/jads.v4i3.121","DOIUrl":"https://doi.org/10.47738/jads.v4i3.121","url":null,"abstract":"The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782170","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}
This study combines learning process theories within the context of data science education in Sichuan Province, China, and develops a customized instructional model for the self-regulated International Higher Education (IHE) Model. In collaboration with 17 experts, selected through purposive sampling, and involving 100 instructors within Sichuan, China, this research explores an instructional model designed to foster self-regulated learning in the field of data science. The Delphi data collection method is employed to investigate the relevance of various learning theories within international higher education in Sichuan Province, China, with a specific emphasis on the data science discipline. The Self-Regulated Learning in International Higher Education (SLR-IHE) model, informed by survey questionnaires, addresses pertinent challenges encountered in data science education, including issues related to English language proficiency, faculty training, curriculum development, faculty mobility, cross-border regulations, and funding constraints. The findings of this study lead to the development of an International Higher Education (IHE) Model for Sichuan Province, China, using the Delphi Technique, which consists of four distinct instructional modules. Through a linear regression analysis of the SLR-IHE model, it becomes evident that the self-regulated learning process in data science education comprises four essential stages, each contributing to the acquisition of distinct goals. These stages include: (1) activating prior knowledge; (2) fostering idea exchange and iterative improvement; (3) building organizational knowledge through understanding, memorization, analysis, and transfer; and (4) generating innovative ideas through reflexive thinking and initiating creative thought processes. These stages collectively support the achievement of specific goals associated with Self-Managed Learning (SML), Self-Regulated Learning (SRL), Self-Paced Learning (SPL), and Self-Directed Learning (SDL) in the context of data science education. This comprehensive instructional model for data science education within the framework of international higher education development in Sichuan Province, China, emphasizes globalization, collaborative efforts, and economic growth as key drivers for enhancing the quality of education in the field of data science.
{"title":"Utilizing the Delphi Technique to Develop a Self-Regulated Learning Model","authors":"Yongmei Li","doi":"10.47738/jads.v4i3.124","DOIUrl":"https://doi.org/10.47738/jads.v4i3.124","url":null,"abstract":"This study combines learning process theories within the context of data science education in Sichuan Province, China, and develops a customized instructional model for the self-regulated International Higher Education (IHE) Model. In collaboration with 17 experts, selected through purposive sampling, and involving 100 instructors within Sichuan, China, this research explores an instructional model designed to foster self-regulated learning in the field of data science. The Delphi data collection method is employed to investigate the relevance of various learning theories within international higher education in Sichuan Province, China, with a specific emphasis on the data science discipline. The Self-Regulated Learning in International Higher Education (SLR-IHE) model, informed by survey questionnaires, addresses pertinent challenges encountered in data science education, including issues related to English language proficiency, faculty training, curriculum development, faculty mobility, cross-border regulations, and funding constraints. The findings of this study lead to the development of an International Higher Education (IHE) Model for Sichuan Province, China, using the Delphi Technique, which consists of four distinct instructional modules. Through a linear regression analysis of the SLR-IHE model, it becomes evident that the self-regulated learning process in data science education comprises four essential stages, each contributing to the acquisition of distinct goals. These stages include: (1) activating prior knowledge; (2) fostering idea exchange and iterative improvement; (3) building organizational knowledge through understanding, memorization, analysis, and transfer; and (4) generating innovative ideas through reflexive thinking and initiating creative thought processes. These stages collectively support the achievement of specific goals associated with Self-Managed Learning (SML), Self-Regulated Learning (SRL), Self-Paced Learning (SPL), and Self-Directed Learning (SDL) in the context of data science education. This comprehensive instructional model for data science education within the framework of international higher education development in Sichuan Province, China, emphasizes globalization, collaborative efforts, and economic growth as key drivers for enhancing the quality of education in the field of data science.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"2605 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782638","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}
Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.
{"title":"Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework","authors":"Adi Suryaputra Paramita","doi":"10.47738/jads.v4i3.117","DOIUrl":"https://doi.org/10.47738/jads.v4i3.117","url":null,"abstract":"Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.","PeriodicalId":479720,"journal":{"name":"Journal of Applied Data Sciences","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782809","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}
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