Analisis Efektivitas Berbagai Jenis Visualisasi Grafik terhadap Pemahaman Audiens pada Presentasi Statistik Berbahasa Indonesia

Authors

  • Naomi Febrina Sitompul Universitas Negeri Medan
  • Angel Patricia Insani Gultom Universitas Negeri Medan
  • Angelica Carolina Tambunan Universitas Negeri Medan
  • Aura Patresia Br Naibaho Universitas Negeri Medan
  • Lirana Sapriani Gulo Universitas Negeri Medan
  • Yoga Nawarisa Pinem Universitas Negeri Medan
  • Hendra Kurnia Pulungan Universitas Negeri Medan

DOI:

https://doi.org/10.58192/insdun.v5i2.4427

Keywords:

Audience Understanding, Data Visualization, Statistical Graphs, Statistical Literacy, Statistics

Abstract

This study aims to analyze the effectiveness of various types of graphic visualizations in improving audience understanding of statistical presentations in Indonesian. Using a descriptive quantitative approach, data were collected through an online questionnaire from 30 college students. Five types of graphs (bar, pie, line, scatter plot, and horizontal bar) were tested for their ease of understanding and interpretation accuracy. The results showed an average interpretation accuracy of 80.7%, indicating the high effectiveness of Indonesian-language graphs. The bar chart was rated the easiest to understand (mean 4.10) with 86.7% accuracy, while the horizontal bar chart recorded the highest accuracy (90.0%). Conversely, the scatter plot was perceived as the most difficult (mean 3.37), although its objective accuracy remained good (80.0%). The clear use of the Indonesian language and graphic design quality proved to be highly positive in aiding audience understanding. In conclusion, Indonesian-language graphic visualization effectively communicates statistical data, with bar charts being the most accessible medium.

References

Abukmail, E., Bakhit, M., Jones, M., Del Mar, C., & Hoffmann, T. (2023). Effect of different visual presentations on the public's comprehension of prognostic information using acute and chronic condition scenarios: Two online randomised controlled trials. BMJ Open, 13(6), e067624. https://doi.org/10.1136/bmjopen-2022-067624

Fachrizal, Budiansyah, A., Romadona, M. R., Suryadi, Akbar, M., & Maulana, S. (2025). Analisis literasi data dan informasi pada mahasiswa vokasi di Indonesia. Prosiding Seminar Nasional Sains dan Teknologi Seri III, 2(1), 591–596.

Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 32(2), 124–158. https://doi.org/10.2307/749671

Hasanah, U., Putrawangsa, S., Setiawati, F. A., & Purwanta, E. (2024). Statistical literacy in primary education: An analysis of Indonesian fifth-graders’ data interpretation and analysis skills. Journal on Mathematics Education, 15(4), 1335-1356.

Hehman, E., & Xie, S. Y. (2021). Doing better data visualization. Advances in Methods and Practices in Psychological Science, 4(4). https://doi.org/10.1177/25152459211045334

Hood, J. C., Graber, C., & Brase, G. L. (2020). Comparing the efficacy of static and dynamic graph types in communicating complex statistical relationships. Frontiers in Psychology, 10, 2986. https://doi.org/10.3389/fpsyg.2019.02986

Iba, Z., & Wardhana, A. (2023). Metode penelitian. CV. Eureka Media Aksara.

Kurnia, A. B., Lowrie, T., & Patahuddin, S. M. (2024). The development of high school students’ statistical literacy across grade level. Mathematics Education Research Journal, 36(Suppl 1), 7-35.

Lenaini, I. (2021). Teknik pengambilan sampel purposive dan snowball sampling. HISTORIS: Jurnal Kajian, Penelitian & Pengembangan Pendidikan Sejarah, 6(1), 33–39. TEKNIK PENGAMBILAN SAMPEL PURPOSIVE DAN SNOWBALL SAMPLING | Lenaini | Historis : Jurnal Kajian, Penelitian dan Pengembangan Pendidikan Sejarah

Lizana, H. I. N., & Ridho, F. (2021, November). Implementasi dan Evaluasi Visualisasi Data Interaktif pada Publikasi Laporan Bulanan Data Sosial Ekonomi Indonesia. In Seminar Nasional Official Statistics (Vol. 2021, No. 1, pp. 947-957).

Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.Office for Statistics Regulation. (2023). Statistical literacy: Research. Office for Statistics Regulation, UK Statistics Authority. https://osr.statisticsauthority.gov.uk/publication/statistical-literacy-research/

Rahmawati, A. D., Ardianzah, F., & Novitasari, P. (2024). Penerapan teori beban kognitif dalam pengajaran matematika dalam mengurangi beban kognitif tak esensial. Jurnal Jendela Pendidikan, 4(04), 463–472.

Sari, F. F. (2022). Pembelajaran Dasar-Dasar Statistik Mengacu Pada Teori Beban Kognitif (Cognitive Load Theory) Untuk Meningkatkan Hasil Belajar. Media Pendidikan Matematika, 10 (2), 155–166.

Subali, B., Negoro, R. A., Dwijananti, P., Anandita, A. S., & Setyaningsih, N. E. (2025). Technology-enhanced learning for statistical graph interpretation: An item response theory analysis of learning outcomes. REID (Research and Evaluation in Education), 11(2), 142-157.

Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4

Trisnawati, R. A., & Mahmudi, A. (2024). Assessing data literacy competencies in mathematics among junior high school students in Indonesia. Al-Ishlah: Jurnal Pendidikan, 16(3), 3073–3088.

Tumini, & Minatania, A. (2023). Visualisasi data COVID-19 tahun 2021 di Jawa Barat menggunakan Google Data Studio. Jurnal Informasi dan Komputer, 11(1), 44–51.

Wiebels, K., & Moreau, D. (2023). Dynamic data visualizations to enhance insight and communication across the life cycle of a scientific project. Advances in Methods and Practices in Psychological Science, 6(2). https://doi.org/10.1177/25152459231160103

Yoghourdjian, V., Yang, Y., Dwyer, T., Lawrence, L., Wybrow, M., & Marriott, K. (2020). Scalability of network visualisation from a cognitive load perspective. IEEE transactions on visualization and computer graphics, 27(2), 1677-1687.

Zhao, F. (2022). Graph schema and best graph type to compare discrete groups: Bar, line, andpie. Frontiers in Psychology, 13, 991420. https://doi.org/10.3389/fpsyg.2022.991420

Downloads

Published

2026-05-29

How to Cite

Naomi Febrina Sitompul, Angel Patricia Insani Gultom, Angelica Carolina Tambunan, Aura Patresia Br Naibaho, Lirana Sapriani Gulo, Yoga Nawarisa Pinem, & Hendra Kurnia Pulungan. (2026). Analisis Efektivitas Berbagai Jenis Visualisasi Grafik terhadap Pemahaman Audiens pada Presentasi Statistik Berbahasa Indonesia. Inspirasi Dunia: Jurnal Riset Pendidikan Dan Bahasa, 5(2), 188–200. https://doi.org/10.58192/insdun.v5i2.4427