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Big data implementation in Tesla using classification with rapid miner | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 2057-2066 اصل مقاله (979.79 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.6016 | ||
| نویسندگان | ||
| Johanes Fernandes Andry1؛ Julia Gunadi1؛ Glisina Dwinoor Rembulan2؛ Hendy Tannady* 3 | ||
| 1Information Systems Department, Universitas Bunda Mulia, Jakarta, Indonesia | ||
| 2Industrial Engineering Department, Universitas Bunda Mulia, Jakarta, Indonesia | ||
| 3Management Department, Kalbis Institute, Jakarta, Indonesia | ||
| تاریخ دریافت: 13 شهریور 1401، تاریخ بازنگری: 22 مهر 1401، تاریخ پذیرش: 05 آذر 1401 | ||
| چکیده | ||
| In this study, we will analyze how big data is implemented in TESLA Company, in this case, we will use sales data. With the growth of big data and the need for its use in companies, nowadays big data is everywhere. TESLA is an American automobile and energy storage company founded by engineers Martin Eberhard and Marc Tarpenning in July 2003 under the name Tesla Motors. The company name is a tribute to inventor and electrical engineer Nikola Tesla. Eberhard said that he wanted to build an automobile manufacturer and also a technology company whose core technology is batteries, computer software and proprietary electric motors. As the amount of data that companies must process today continues to increase, companies must keep up with the times by using big data. Big data can be used to move, contain, and access large amounts of unstructured and disparate data in a timely manner. it is good. The method we use is quantitative data. This calculation will use the Rapid Miner software. The result of this study is the data is 2,146 units, total volume from 118,500 to 47,065,000 based on the number of existing sales, and classification results are from 2621300 to 18766300. | ||
| کلیدواژهها | ||
| Big data؛ Classification؛ Data؛ TESLA | ||
| مراجع | ||
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[1] N.E.V. Anna and E.F. Mannan, Big data adoption in academic libraries: a literature review, Libr. Hi Tech. News 37(4) (2020) 1–5. [2] J. Arunadevi, S. Ramya and M.R. Raja, A study of classification algorithms using Rapidminer, Int. J. Pure Appl. Math. 119(12) (2018) 15977–15988. [3] N. Bharadwaj, Strategic decision making in an information-rich environment: A synthesis and an organizing framework for innovation research, Rev. Mark. Res. 15 (2018) 3–30. [4] K. Ebner, T. B¨uhnen and N. Urbach, Think big with big data: Identifying suitable big data strategies in corporate environments, Proc. Annu. Hawaii Int. Conf. Syst. Sci. (2014) 3748–3757. [5] F. Gorunescu, Data Mining: Concepts, Models and Techniques, Springer Science & Business Media, 2011. [6] S. Green, J.E. McKinney, K. Heppard and L. Garcia, Article information: Big data, digital demand, and decisionmaking, Int. J. Account. Inf. Manag. 26(4) (2017) 541–555. [7] A. Intezari and S. Gressel, Information and reformation in KM systems: big data and strategic decision-making, J. Knowl. Manag. 21(1) (2017) 71–91. [8] H. Ko¨scielniak and A. Puto, Big data in decision making processes of enterprises, Procedia Comput. Sci. 65(Iccmit) (2015) 1052–1058. [9] T. Loya and G. Carden, Higher Education Strategy and Planning: A Profesional Guide, Business Intelligence and Analytics, 2018. [10] E.D. Madyatmadja, L. Liliana, J.F. Andry and H. Tannady, Risk analysis of human resource information systems using Cobit 5, J. Theor. Appl. Inf. Technol. 98(21) (2020) 3357–3367. [11] E.D. Madyatmadja, M. Marvel, J.F. Andry, H. Tannady and A. Chakir, Implementation of big data in hospital using cluster analytics, Int. Conf. Inf. Manag. Technol. (2021) 496–500. [12] E.D. Madyatmadja, A. Rianto, J.F. Andry, H. Tannady and A. Chakir, Analysis of big data in healthcare using decision tree algorithm, Proc. 2021 1st Int. Conf. Comput. Sci. Artific. Intell. (2021) 313–317. [13] E.D. Madyatmadja, D.J.M. Sembiring, S.M.B.P. Angin, D. Ferdy and J.F. Andry, Big data in educational institutions using RapidMiner to predict learning effectiveness, J. Comput. Sci. 17(4) (2021) 403–413. [14] S. Neelamegam and E. Ramaraj, Classification algorithm in data mining: An overview, Int. J. P2P Netw. Trends Technol. 4(8) (2013) 369–374. [15] F. Provost and T. Fawcett, Data science and its relationship to big data and data-driven decision making, Big Data 1(1) (2013) 51–59.[16] V. Rajeswari and K. Arunesh, Analysing soil data using data mining classification techniques, Indian J. Sci. Technol. 9(19) (2016) 1–4. [17] L. Ruoxi, W. Hao, X. Jiayi, X. Xiaowei and X. Yanting, Capital structure analysis of Tesla Motors, Inc, Department of Economics and Finance City University of Hong Kong, 2014. [18] Y.F. Safri, R. Arifudin and M.A. Muslim, K-nearest neighbor and naive bayes classifier algorithm in determining the classification of healthy card Indonesia giving to the poor, Sci. J. Inf. 5(1) (2018). [19] P.K. Sari and A. Purwadinata, Analysis characteristics of car sales in E-commerce data using clustering model, J. Data Sci. Appl. 2(1) (2019) 68–77. [20] K. Vassakis, E. Petrakis and I. Kopanakis, Mobile Big Data, A Roadmap from Models to Technologies, Springer, Cham, 2018. | ||
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