WoS Citation Analysis

 

CITATION DETAIL:

Journal of Applied Data Science (JADS) articles are cited by 29 articles in Web of Science database since published in September 2020.

Last update: 20 February 2024; 

>> T.-H. Cheng, “The Empirical Study of Usability and Credibility on Intention Usage of Government-to-Citizen Services,” J. Appl. Data Sci., vol. 2, no. 2, pp. 36–44, 2021, doi: 10.47738/jads.v2i2.30.

Cited by WoS Document:

[1] Berlilana, T. Noparumpa, A. Ruangkanjanases, T. Hariguna, and Sarmini, “Organization benefit as an outcome of organizational security adoption: The role of cyber security readiness and technology readiness,” Sustain., vol. 13, no. 24, 2021, doi: 10.3390/su132413761.

[2] U. Rahardja, T. Hongsuchon, T. Hariguna, and A. Ruangkanjanases, “Understanding impact sustainable intention of s‐commerce activities: The role of customer experiences, perceived value, and mediation of relationship quality,” Sustain., vol. 13, no. 20, 2021, doi: 10.3390/su132011492.

>> M. G. Pradana and H. T. Ha, “Maximizing Strategy Improvement in Mall Customer Segmentation using K-means Clustering,” J. Appl. Data Sci., vol. 2, no. 1, pp. 19–25, 2021.

Cited by WoS Document:

[1] Berlilana, T. Noparumpa, A. Ruangkanjanases, T. Hariguna, and Sarmini, “Organization benefit as an outcome of organizational security adoption: The role of cyber security readiness and technology readiness,” Sustain., vol. 13, no. 24, 2021, doi: 10.3390/su132413761.

[2] U. Rahardja, T. Hongsuchon, T. Hariguna, and A. Ruangkanjanases, “Understanding impact sustainable intention of s‐commerce activities: The role of customer experiences, perceived value, and mediation of relationship quality,” Sustain., vol. 13, no. 20, 2021, doi: 10.3390/su132011492.

[3] F. P. Rachman, H. Santoso, and A. Djajadi, “Machine Learning Mini Batch K-means and Business Intelligence Utilization for Credit Card Customer Segmentation,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 10, pp. 218–227, 2021, doi: 10.14569/IJACSA.2021.0121024.

>> R. Endsuy, “Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets,” J. Appl. Data Sci., vol. 2, no. 1, pp. 8–18, 2021, doi: 10.47738/jads.v2i1.17.

Cited by WoS Document:

[1] V. Umarani, A. Julian, and J. Deepa, “Sentiment Analysis using various Machine Learning and Deep Learning Techniques,” J. Niger. Soc. Phys. Sci., vol. 3, no. 4, pp. 385–394, 2021, doi: 10.46481/jnsps.2021.308.

[2] K. Fahd, S. Parvin, and A. D. Souza-Daw, “A Framework for Real-time Sentiment Analysis of Big Data Generated by Social Media Platforms,” in 2021 31st International Telecommunication Networks and Applications Conference, ITNAC 2021, 2021, pp. 30–33, doi: 10.1109/ITNAC53136.2021.9652148.

[3] H. N. Chaudhry et al., “Sentiment analysis of before and after elections: Twitter data of U.S. election 2020,” Electron., vol. 10, no. 17, 2021, doi: 10.3390/electronics10172082.

>> I. Umami, “Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on Marketing Dataset,” J. Appl. Data Sci., vol. 2, no. 2, pp. 14–25, 2021, doi: 10.47738/jads.v2i2.28.

Cited by WoS Document:

[1] Berlilana, T. Noparumpa, A. Ruangkanjanases, T. Hariguna, and Sarmini, “Organization benefit as an outcome of organizational security adoption: The role of cyber security readiness and technology readiness,” Sustain., vol. 13, no. 24, 2021, doi: 10.3390/su132413761.

[2] Y. G. Kim and C.-J. Wu, “AutoFL: Enabling heterogeneity-aware energy efficient federated learning,” in Proceedings of the Annual International Symposium on Microarchitecture, MICRO, 2021, pp. 183–198, doi: 10.1145/3466752.3480129.

>> Akmal, “Predicting Dropout on E-learning Using Machine Learning,” J. Appl. Data Sci., vol. 1, no. 1, pp. 29–34, 2020, doi: 10.47738/jads.v1i1.9.

Cited by WoS Document:

[1] V. Senthil Kumaran and B. Malar, “Distributed ensemble based iterative classification for churn analysis and prediction of dropout ratio in e-learning,” Interact. Learn. Environ., 2021, doi: 10.1080/10494820.2021.1956547.

>> N. H. Trang, “Limitations of Big Data Partitions Technology,” J. Appl. Data Sci., vol. 1, no. 1, pp. 11–19, 2020.

Cited by WoS Document:

[1] S. Wibowo, R. Hidayat, Y. Suryana, D. Sari, and U. Kaltum, “Measuring the Effect of Advertising Value and Brand Awareness on Purchase Intention through the Flow Experience Method on Facebook’s Social Media Marketing Big Data,” 2020, doi: 10.1109/CITSM50537.2020.9268812.