Scopus Citation Analysis

CITATION DETAIL:

YEAR

Number of Citation

2022

1

2021

29

2020

8

Last update: 15 September 2022; Journal of Applied Data Science is cited articles on Scopus database since published in 2020.

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VOLUME 3 NUMBER 1


>> F. Khasanah, “Application of Hash Sha-256 Algorithm in Website-Based Sales Software Engineering,” J. Appl. Data Sci., vol. 3, no. 1, pp. 24–32, 2022, doi: 10.47738/jads.v3i1.50.

Cited by Scopus Document:

[1] B. S. Rawal, L. S. Kumar, S. Maganti, and V. Godha, “Comparative Study of Sha-256 Optimization Techniques,” in 2022 IEEE World AI IoT Congress, AIIoT 2022, 2022, pp. 387–392. doi: 10.1109/AIIoT54504.2022.9817185.


VOLUME 2 NUMBER 4


>> A. Hananto, “An Ensemble and Filtering-Based System for Predicting Educational Data Mining,” J. Appl. Data Sci., vol. 2, no. 4, pp. 157–173, 2021, doi: 10.47738/jads.v2i4.44.

Cited by Scopus Document:

[1] Y. Li, Y. Zhang, and F. Zhang, “Materialism Predicts College Students’ Entrepreneurial Intention: A Serial Mediation Model,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.864069.


VOLUME 2 NUMBER 3


>> L. Jen and Y.-H. Lin, “A Brief Overview of the Accuracy of Classification Algorithms for Data Prediction in Machine Learning Applications,” J. Appl. Data Sci., vol. 2, no. 3, pp. 84–92, 2021.

Cited by Scopus Document:

[1] S. Hu and Z. Zhu, “Effects of Social Media Usage on Consumers’ Purchase Intention in Social Commerce: A Cross-Cultural Empirical Analysis,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.837752.

[2] Y. Li, Y. Zhang, and F. Zhang, “Materialism Predicts College Students’ Entrepreneurial Intention: A Serial Mediation Model,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.864069.

>> T. Alvarez, “Meta-Analysis of Social Networking Sites for the Purpose of Preventing Privacy Threats in the Digital Age,” J. Appl. Data Sci., vol. 2, no. 3, pp. 64–73, 2021.

Cited by Scopus Document:

[1] R. Abid, M. Rizwan, P. Veselý, A. Basharat, U. Tariq, and A. R. Javed, “Social Networking Security during COVID-19: A Systematic Literature Review,” Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/2975033.

>> R. A. Widyanto, M. Hadi Avizenna, N. A. Prabowo, K. Alfata, and A. Ismanto, “Data Mining Predicts the Need for Immunization Vaccines Using the Naive Bayes Method,” J. Appl. Data Sci., vol. 2, no. 3, pp. 12–15, 2021.

Cited by Scopus Document:

[1] M. A. Arshed, W. Qureshi, M. Rumaan, M. T. Ubaid, A. Qudoos, and M. U. G. Khan, “Comparison of Machine Learning Classifiers for Breast Cancer Diagnosis,” 2021. doi: 10.1109/ICIC53490.2021.9692926.


VOLUME 2 NUMBER 2


>> 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 Scopus 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] V. S. Diwanji, “Improving accessibility and inclusiveness of university websites for international students: a mixed-methods usability assessment,” Technol. Pedagog. Educ., 2022, doi: 10.1080/1475939X.2022.2089724.

>> T. wahyuningsih, “Text Mining an Automatic Short Answer Grading (ASAG), Comparison of Three Methods of Cosine Similarity, Jaccard Similarity and Dice’s Coefficient,” J. Appl. Data Sci., vol. 2, no. 2, pp. 45–54, 2021, doi: 10.47738/jads.v2i2.31.

Cited by Scopus Document:

[1] N. Chamidah, M. M. Santoni, H. N. Irmanda, R. Astriratma, L. M. Tua, and T. Yuniati, “Word Expansion using Synonyms in Indonesian Short Essay Auto Scoring,” in Proceedings - 3rd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021, 2021, pp. 296–300. doi: 10.1109/ICIMCIS53775.2021.9699374.

>> 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 Scopus 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.


VOLUME 2 NUMBER 1


>> 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 Scopus 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.

[4] R. H. Khan, D. F. Dofadar, and M. G. Rabiul Alam, “Explainable Customer Segmentation Using K-means Clustering,” in 2021 IEEE 12th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2021, 2021, pp. 639–643, doi: 10.1109/UEMCON53757.2021.9666609.

[5] S. Hu and Z. Zhu, “Effects of Social Media Usage on Consumers’ Purchase Intention in Social Commerce: A Cross-Cultural Empirical Analysis,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.837752.

[6] M. Yang, I. Tjuawinata, and K.-Y. Lam, “K-Means Clustering With Local d-Privacy for Privacy-Preserving Data Analysis,” IEEE Trans. Inf. Forensics Secur., vol. 17, pp. 2524–2537, 2022, doi: 10.1109/TIFS.2022.3189532.

[7] N. Gautam and N. Kumar, “Customer segmentation using k-means clustering for developing sustainable marketing strategies,” Bus. Informatics, vol. 16, no. 1, pp. 72–82, 2022, doi: 10.17323/2587-814X.2022.1.72.82.

>> P. H. Saputro and H. Nanang, “Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets,” J. Appl. Data Sci., vol. 2, no. 1, pp. 40–56, 2021.

Cited by Scopus Document:

[1] Y. Li, Y. Zhang, and F. Zhang, “Materialism Predicts College Students’ Entrepreneurial Intention: A Serial Mediation Model,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.864069.

[2] M. V Rakesh, S. P. Kumar, and R. Aishwarya., “Hotel Booking Cancelation Prediction using ML algorithms,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 466–471. doi: 10.1109/ICAIS53314.2022.9742843.

>> 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 Scopus 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.

[4] A. Razzaq et al., “Extraction of Psychological Effects of COVID-19 Pandemic through Topic-Level Sentiment Dynamics,” Complexity, vol. 2022, 2022, doi: 10.1155/2022/9914224.

[5] S. Singh, S. Singhania, V. Pandya, A. Singal, and A. Biwalkar, “East Meets West: Sentiment Analysis for Election Prediction,” in Studies in Computational Intelligence, vol. 1027, 2022, pp. 9–20. doi: 10.1007/978-3-030-96634-8_2.

>> S. Sugiyanto, “Predict High School Students Final Grades Using Basic Machine Learning,” J. Appl. Data Sci., vol. 2, no. 1, pp. 26–39, 2021, doi: 10.47738/jads.v2i1.19.

[1] Y. Li, Y. Zhang, and F. Zhang, “Materialism Predicts College Students’ Entrepreneurial Intention: A Serial Mediation Model,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.864069.

>> N. Dzakiyullah, “Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders,” J. Appl. Data Sci., vol. 2, no. 1, pp. 1–7, 2021, doi: 10.47738/jads.v2i1.16.

[1] A. Mniai and K. Jebari, “Credit Card Fraud Detection by Improved SVDD,” in Lecture Notes in Engineering and Computer Science, 2022, vol. 2244, pp. 32–37. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137107494&partnerID=40&md5=4e6b4caa7f8909c08033dcfd1d394e1e

[2] C. Mabani, N. Christou, and S. Katkov, “Detection of Credit Card Frauds with Machine Learning Solutions: An Experimental Approach,” Lecture Notes in Networks and Systems, vol. 506 LNNS. pp. 715–722, 2022. doi: 10.1007/978-3-031-10461-9_49.


VOLUME 1 NUMBER 2


>> E. Mohammed and A. Qhal, “Knowledge Management Strategy by Means of Virtualization in Covid-19,” J. Appl. Data Sci., vol. 1, no. 2, pp. 41–53, 2020.

Cited by Scopus Document:

[1] S. Hu and Z. Zhu, “Effects of Social Media Usage on Consumers’ Purchase Intention in Social Commerce: A Cross-Cultural Empirical Analysis,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.837752.


VOLUME 1 NUMBER 1


>> 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 Scopus 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.

[2] V. L. Narayana, S. Sirisha, G. Divya, N. L. S. Pooja, and S. A. Nouf, “Mall Customer Segmentation Using Machine Learning,” in Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, 2022, vol. 2022-January, pp. 1280–1288. doi: 10.1109/ICEARS53579.2022.9752447.

>> A. S. Mujali Al-Rawahnaa and A. Y. Bader Al Hadid, “Data mining for Education Sector, a proposed concept,” J. Appl. Data Sci., vol. 1, no. 1, pp. 1–10, Aug. 2020, doi: 10.47738/jads.v1i1.6.

Cited by Scopus Document:

[1] M. Mawarni, F. Utaminingrum, and W. F. Mahmudy, “The Effect of Feature Selection on Gray Level Co-Occurrence Matrix (GLCM) for the Four Breast Cancer Classifications,” J. Biomimetics, Biomater. Biomed. Eng., vol. 55, pp. 168–179, 2022, doi: 10.4028/p-09g3n8.

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

Cited by Scopus 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.

>> T. Hariguna, H. T. Sukmana, and J. Il Kim, “Survey oponion using Sentiment Analysis,” J. Appl. Data Sci., vol. 1, no. 1, pp. 35–40, 2020, doi: 10.47738/jads.v1i1.10.

Cited by Scopus Document:

[1] S. Hu and Z. Zhu, “Effects of Social Media Usage on Consumers’ Purchase Intention in Social Commerce: A Cross-Cultural Empirical Analysis,” Front. Psychol., vol. 13, 2022, doi: 10.3389/fpsyg.2022.837752.