111 research outputs found
Memahami Determinan Keberhasilan Sistem: Sebuah Model Hubungan Antara Kualitas, Penggunaan, Dan Kepuasan
Studi ini bertujuan untuk menganalisis dampak kualitas e-learning terhadap keterlibatan dan kepuasan pengguna, serta pengaruhnya terhadap keberhasilan sistem e-learning di lingkungan universitas. Model keberhasilan sistem informasi diadopsi untuk menguji efikasi sistem e-learning. Studi ini didasarkan pada hipotesis bahwa kualitas sistem, instruksi, dan informasi berpengaruh signifikan terhadap penggunaan sistem dan kepuasan pengguna dalam lingkungan e-learning universitas, yang pada gilirannya menentukan keberhasilan sistem. Model persamaan struktural (SEM) digunakan, dengan menerapkan Partial Least Squares (PLS) versi 4.0, untuk menilai dan memvalidasi model pengukuran dan struktural. Sampel penelitian ini terdiri dari 173 mahasiswa dari berbagai lembaga pendidikan tinggi di kota Semarang. Temuan menunjukkan bahwa kualitas sistem, kualitas informasi, dan kualitas instruksi memiliki efek yang signifikan dan positif terhadap penggunaan sistem (menjelaskan 57% varians) dan kepuasan pengguna (47,2% R Square). Selain itu, hasil juga menunjukkan bahwa kualitas sistem memberikan pengaruh terkuat terhadap kepuasan pengguna dan penggunaan sistem, sementara kualitas penggunaan sistem memiliki dampak yang lebih besar terhadap keberhasilan sistem e-learning universitas
Customersā loyalty model in the design of e-commerce recommender systems
Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customersā loyalty, which is an important factor to increase service providersā share of wallet. Therefore, this study aimed to investigate the customersā loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customersā loyalty towards Amazonās recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customersā loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customersā loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customersā loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customersā loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm
Computational Methods for Medical and Cyber Security
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields
Mining microblogs for culture-awareness in web adaptation
Prior studies in sociology and human-computer interaction indicate that persons
from diļ¬erent countries and cultural origins tend to have their preferences in real-life
communication and the usage of web and social media applications. With Twitter
data, statistical and machine learning tools, this study advances our understand
ing of microblogging in respect of cultural diļ¬erences and demonstrates possible
solutions of inferring and exploiting cultural origins for building adaptive web ap
plications. Our ļ¬ndings reveal statistically signiļ¬cant diļ¬erences in Twitter feature
usage in respect of geographic locations of users. These diļ¬erences in microblogger
behaviour and user language deļ¬ned in user proļ¬les enabled us to infer user country
origins with an accuracy of more than 90%. Other user origin predictive solutions
we proposed do not require other data sources and human involvement for training
the models, enabling the high accuracy of user country inference when exploiting
information extracted from a user followersā network, or with data derived from
Twitter proļ¬les. With origin predictive models, we analysed communication and
privacy preferences and built a culture-aware recommender system. Our analysis of
friend responses shows that Twitter users tend to communicate mostly within their
cultural regions. Usage of privacy settings showed that privacy perceptions diļ¬er
across cultures. Finally, we created and evaluated movie recommendation strategies
considering user cultural groups, and addressed a cold-start scenario with a new
user. We believe that the ļ¬ndings discussed give insights into the sociological and
web research, in particular on cultural diļ¬erences in online communication
Representation of virtual choreographies in learning dashboards of interoperable LMS analytics
Learning management systems (LMS) collect a large amount of data from user interaction, and it isn't easy to analyze this data in a reliable and context-independent manner. This research seeks to comprehend how virtual choreographies can be represented in interoperable LMS analytics dashboards. In order to gain a better understanding of the problem, this objective has been divided into three sub-goals: determining which interactions can be gathered from LMS contexts, identifying virtual choreographies from LMS logs, and representing virtual choreographies in learning dashboards. To achieve these objectives, we first conducted a Systematic Literature Review to comprehend the behaviors and interactions other authors have investigated in LMS contexts. Then, by applying these findings to this dissertation's case study, a methodical procedure for extracting valuable choreographies from the logs was outlined. The Design Science Research methodology was then applied to transforming logs into virtual choreographies and their representation in learning dashboards. It was implemented two services: one responsible for identifying virtual choreographies from data logs and transforming the logs into statements, recipes, and choreographies, following xAPI specification elements; and the other translates the information from the backend service into dashboard visualizations, allowing the user to view representations for statements, recipes, choreographies, and various visualizations. These artifacts provide a new flexible and cost-efficient solution for the identification of virtual choreographies, thereby facilitating the widespread adoption of their use
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