COLLEGE ACADEMIC DATA ANALYSIS USING DATA VISUALIZATION

Takdir Zulhaq  Dessiaming; Siska  Anraeni; Suwito  Pomalingo

doi:10.20884/1.jutif.2022.3.5.310

Takdir Zulhaq Dessiaming Universitas Muslim Indonesia, Indonesia
Siska Anraeni Universitas Muslim Indonesia, Indonesia
Suwito Pomalingo Universitas Muslim Indonesia, Indonesia

DOI: https://doi.org/10.20884/1.jutif.2022.3.5.310

Keywords: Academic Data, College, Data Visualization, Comprehensive Stage

Abstract

Data is a collection of information that contains a broad picture related to a situation. The amount of data is not necessarily better, because a large data set makes it difficult to convert data into information in a timely manner, especially in analyzing data which produces meaningful and relevant information which ultimately results in quick and appropriate action. Higher education management in Indonesia requires fast and accurate academic reports so that it can facilitate strategic decision making in order to improve the quality of education. This study aims to carry out a comprehensive process of analyzing academic data at universities to display them into interactive data visualizations, so that they can retrieve the information in it and make strategic decisions. The method used is a data visualization technique, which allows users to easily see the insights or insights contained in the data. The results obtained are data that has passed the preprocessing stage, can prepare data before being analyzed and processed to be used to make data visualization, so that the information obtained is more varied. This information can be used as a reference by academic managers to make strategic decisions.

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References

S. Shen, “What is Data?,” 2020. https://towardsdatascience.com/what-is-data-ade94b37204a (accessed May 18, 2022).

F. Provost and T. Fawcett, “Data Science and its Relationship to Big Data and Data-Driven Decision Making,” Big Data, vol. 1, no. 1, pp. 51–59, 2013, doi: 10.1089/big.2013.1508.

A. Holst, “Volume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025,” Www.Statista.Com, 2021. https://www.statista.com/statistics/871513/worldwide-data-created/ (accessed Oct. 31, 2021).

D. Bumblauskas, H. Nold, P. Bumblauskas, and A. Igou, “Big data analytics: transforming data to action,” Bus. Process Manag. J., vol. 23, no. 3, pp. 703–720, 2017, doi: 10.1108/BPMJ-03-2016-0056.

Greg Bedell, “Dashboards for Higher Education Leaders Can Optimize Decision Supporttle,” 2021. https://www.huronconsultinggroup.com/insights/dashboards-higher-education-leaders-optimize-decision-support (accessed Nov. 04, 2021).

S. A. Indutoro and E. Sudarmilah, “Web-Based Academic Information System of Yapi Sunni Tegalgondo Islamic Boarding School Using Waterfall Method To Manage Students Academic Data,” J. Tek. Inform., vol. 3, no. 1, pp. 49–59, 2022, [Online]. Available: http://jutif.if.unsoed.ac.id/index.php/jurnal/article/view/127.

N. Destiandi and A. Hermawan, “Business Intelligent Method For Academic Dashboard,” bit-Tech, vol. 1, no. 2, pp. 11–20, 2018, doi: 10.32877/bt.v1i2.42.

M. Ropianto, P. Studi, and T. Informatika, “Pemanfaatan Sistem Dashboard Pada Data Akademik Di Sekolah Tinggi Teknik ( STT ) Ibnu Sina Batam,” vol. 2, no. 2, pp. 67–71, 2017.

D. Hartama, P. Studi, and T. Informatika, “ANALISA VISUALISASI DATA AKADEMIK,” no. 3, pp. 46–55, 2018.

C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data,” Front. Energy Res., vol. 9, no. March, pp. 1–17, 2021, doi: 10.3389/fenrg.2021.652801.

D. B. Lomet, “Bulletin of the Technical Committee on Data Engineering,” Bull. Tech. Comm. Data Eng., vol. 24, no. 4, pp. 1–56, 2001, [Online]. Available: papers2://publication/uuid/30073F7F-1B7C-4496-ADA4-94FF4E6EE8F7.

J. Spiess, Y. T’Joens, R. Dragnea, P. Spencer, and L. Philippart, “Using Big Data to Improve Customer Experience and Business Performance,” Bell Labs Tech. J., vol. 18, pp. 3–17, Mar. 2014, doi: 10.1002/bltj.21642.

C. B. Thompson, “Descriptive Data Analysis,” Air Med. J., vol. 28, no. 2, pp. 56–59, 2009, doi: 10.1016/j.amj.2008.12.001.

E. Turban, R. Sharda, and D. Delen, “Decision support and business intelligence systems (required),” Google Sch., 2010, [Online]. Available: https://www.academia.edu/download/31082638/m671w13syllabus.pdf.

S. Pomalingo, B. Sugiantoro, and Y. Prayudi, “Data Visualisasi Sebagai Pendukung Investigasi Media Sosial,” Ilk. J. Ilm., vol. 11, no. 2, pp. 143–151, 2019, doi: 10.33096/ilkom.v11i2.443.143-151.

V. Friedman, “Data Visualization and Infographics,” 2008, [Online]. Available: https://www.smashingmagazine.com/2008/01/monday-inspiration-data-visualization-and-infographics/.

Daniel A. Keim, “Information Visualization and Visual Data Mining,” 2002.