Scopus Citation Analysis

CITATION DETAIL:

Journal of Applied Data Science (JADS) articles are cited by 149 articles in SCOPUS database since published in September 2020.

Last update: 20 February 2024;

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 VOLUME 3 NUMBER 1

 

>> F. Riawan, “Knuth Morris Pratt String Matching Algorithm in Searching for Zakat Information and Social Activities,” J. Appl. Data Sci., vol. 3, no. 1, pp. 15–23, 2022, doi: 10.47738/jads.v3i1.49.

Cited by Scopus Document:

[1] C. S. Rao, J. Rajanikanth, C. C. Sekhar, and R. N. Balaka, “Ultrafast parallel genome extractor[Formula presented],” Softw. Impacts, vol. 14, 2022, doi: 10.1016/j.simpa.2022.100420.

>> 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

 

>> Q. Aini, “Classification of Tweets Causing Deadlocks in Jakarta Streets with the Help of Algorithm C4.5,” J. Appl. Data Sci., vol. 2, no. 4, pp. 143–156, 2021, doi: 10.47738/jads.v2i4.43.

Cited by Scopus Document:

[1] Sarmini, A. Alhabeeb, M. M. Abusharhah, T. Hariguna, and A. R. Hananto, “An Investigation into Indonesian Students’ Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis,” Int. J. Informatics Vis., vol. 6, no. 3, pp. 604–609, 2022, doi: 10.30630/joiv.6.3.894.

>> 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.

[2] T. Hariguna, Sarmini, and A. R. Hananto, “E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 303–307. doi: 10.1109/CyberneticsCom55287.2022.9865585.

 

 VOLUME 2 NUMBER 3

 

>> R. Riyanto, “Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia,” J. Appl. Data Sci., vol. 2, no. 3, pp. 102–108, 2021.

Cited by Scopus Document:

[1] Sarmini, A. Alhabeeb, M. M. Abusharhah, T. Hariguna, and A. R. Hananto, “An Investigation into Indonesian Students’ Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis,” Int. J. Informatics Vis., vol. 6, no. 3, pp. 604–609, 2022, doi: 10.30630/joiv.6.3.894.

[2] T. Hariguna, U. Rahardja, and U. Sarmini, “The Role of E-Government Ambidexterity as the Impact of Current Technology and Public Value: An Empirical Study,” Informatics, vol. 9, no. 3, 2022, doi: 10.3390/informatics9030067.

[3] T. Hariguna, Sarmini, and A. R. Hananto, “E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 303–307. doi: 10.1109/CyberneticsCom55287.2022.9865585.

>> 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] 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] T. Hariguna, Sarmini, and A. R. Hananto, “E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 303–307. doi: 10.1109/CyberneticsCom55287.2022.9865585.

[3] 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.

[4] A. Rammal, R. Assaf, A. Goupil, M. Kacim, and V. Vrabie, “Machine learning techniques on homological persistence features for prostate cancer diagnosis,” BMC Bioinformatics, vol. 23, no. 1, 2022, doi: 10.1186/s12859-022-04992-5.

>> 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] T. Hariguna, Sarmini, and A. R. Hananto, “E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 303–307. doi: 10.1109/CyberneticsCom55287.2022.9865585.

[2] 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.

[3] Y.-S. Chen, C.-K. Lin, J. C.-L. Chou, S.-F. Chen, and M.-H. Ting, “Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy,” Sustain., vol. 14, no. 19, 2022, doi: 10.3390/su141912485.

>> J. Prayitno, B. Saputra, and R. Waluyo, “Data Mining Implementation with Algorithm C4.5 for Predicting Graduation Rate College student,” J. Appl. Data Sci., vol. 2, no. 3, pp. 74–83, 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.

 

 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.

[4] V. N. Fathya, V. Viverita, S. R. H. Hati, and R. D. Astuti, “Customer satisfaction with electronic public services: An 18 years of systematic literature review,” Int. Rev. Public Nonprofit Mark., 2022, doi: 10.1007/s12208-022-00350-6.

>> 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.

[2] A. Mavrogiorgou, A. Kiourtis, S. Kleftakis, K. Mavrogiorgos, N. Zafeiropoulos, and D. Kyriazis, “A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions †,” Sensors, vol. 22, no. 22, 2022, doi: 10.3390/s22228615.

[3] M. Daoud, “Topical and Non-Topical Approaches to Measure Similarity between Arabic Questions,” Big Data Cogn. Comput., vol. 6, no. 3, 2022, doi: 10.3390/bdcc6030087.

>> 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] 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.

[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] 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.

[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] G. S. Palupi and M. N. Fakhruzzaman, “Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm,” Int. J. Electr. Comput. Eng., vol. 12, no. 6, pp. 6132–6139, 2022, doi: 10.11591/ijece.v12i6.pp6132-6139.

[8] 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.

>> 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.

[3] Z. Liu, P. Jiang, J. Wang, Z. Du, X. Niu, and L. Zhang, “Hospitality order cancellation prediction from a profit-driven perspective,” Int. J. Contemp. Hosp. Manag., 2022, doi: 10.1108/IJCHM-06-2022-0737.

[4] T. Hariguna, Sarmini, and A. R. Hananto, “E-government Public Complaints Text Classification Using Particle Swarm Optimization in Naive Bayes Algorithm,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom), 2022, pp. 303–307. doi: 10.1109/CyberneticsCom55287.2022.9865585.

>> 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] Sarmini, A. Alhabeeb, M. M. Abusharhah, T. Hariguna, and A. R. Hananto, “An Investigation into Indonesian Students’ Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis,” Int. J. Informatics Vis., vol. 6, no. 3, pp. 604–609, 2022, doi: 10.30630/joiv.6.3.894.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] S. Limboi and L. Diosan, “An unsupervised approach for Twitter Sentiment Analysis of USA 2020 Presidential Election,” 2022. doi: 10.1109/INISTA55318.2022.9894264.

>> 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 opinion 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.

[2] T. Hariguna, U. Rahardja, and U. Sarmini, “The Role of E-Government Ambidexterity as the Impact of Current Technology and Public Value: An Empirical Study,” Informatics, vol. 9, no. 3, 2022, doi: 10.3390/informatics9030067.

>> T. Wahyuningsih, “Problems, Challenges, and Opportunities Visualization on Big Data,” J. Appl. Data Sci., vol. 1, no. 1, pp. 20–28, 2020, doi: 10.47738/jads.v1i1.8.

Cited by Scopus Document:

[1] O. Araque et al., “Making Sense of Language Signals for Monitoring Radicalization,” Appl. Sci., vol. 12, no. 17, 2022, doi: 10.3390/app12178413.