Zobrazeno 1 - 10 of 14 pro vyhledávání: '"Sulis Sandiwarno"'
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Akademický článek
Empirical lecturers’ and students’ satisfaction assessment in e-learning systems based on the usage metrics
Autor: Sulis Sandiwarno
Publikováno v: REID (Research and Evaluation in Education), Vol 7, Iss 2, Pp 118-131 (2021)
Nowadays, in the pandemic of COVID-19, e-learning systems have been widely used to facilitate teaching and learning processes between lecturers and students. Assessing lecturers’ and students’ satisfaction with e-learning systems has become essen
Externí odkaz: https://doaj.org/article/72fec2d6e2f54582bd7dd453744d31de
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3
Identifying rename refactoring opportunities based on feature requests
Autor: Khadidja Bakhti, Sulis Sandiwarno, Ally S. Nyamawe
Publikováno v: International Journal of Computers and Applications. 44:770-778
Refactoring is a widespread practice of improving the quality of software systems by applying changes on their internal structures without affecting their observable behaviors. Rename is one of the...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0dbbf1c9952c5efb74b3728e32546c82
https://doi.org/10.1080/1206212x.2021.1922151
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4
Users' Emotions Analysis based on Hybrid Feature Extraction Techniques
Autor: Sulis Sandiwarno
Publikováno v: International Journal of Scientific Research in Computer Science, Engineering and Information Technology. :291-296
In order to solve some problems of importance of words and missing relations of semantic between words in the emotional analysis of e-learning systems, the TF-IWF algorithm weighted Word2vec algorithm model was proposed as a feature extraction algori
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::922b04486944a0b26729b3bc68ba999b
https://doi.org/10.32628/cseit206658
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5
Deep learning techniques for rating prediction: a survey of the state-of-the-art
Autor: Zahid Younas Khan, Zhendong Niu, Sulis Sandiwarno, Rukundo Prince
Publikováno v: Artificial Intelligence Review. 54:95-135
With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome the information overload problem. Recommender systems utiliz
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f748385d1a1f3c288c9c7a00d9feefc7
https://doi.org/10.1007/s10462-020-09892-9
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