Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment

Authors

  • Wargijono Utomo Universitas Krisnadwipayana
(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i2.215

Keywords:

Clustering, Elbow, Gap Statistics, K-Means

Abstract

In post-pandemic recovery efforts, uncertainty arose due to the unresolved conflict between the Russia-Ukraine war. This conflict impacts world security stability and affects the economic, energy, and food sectors. This conflict also impacts humanity by causing death to civilians and military personnel, including children in Ukraine. The clustering analysis results of the impact of the Russian-Ukrainian war show losses and losses in personnel and war equipment, with three cluster optimization methods used through k-means. Of the two methods that can be recommended, namely elbow and Silhouette, both produce K=3. The profiling results show that losses or losses in Ukrainian personnel and war equipment are categorized into three clusters, with cluster one being the lowest category, cluster two being the very high category, and cluster three being the moderate category. This research is helpful for state agencies, international organizations (NGOs), and other stakeholders.

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References

Chantaramanee, A., Nakagawa, K., Yoshimi, K., Nakane, A., Yamaguchi, K., & Tohara, H. (2022). Comparison of Tongue Characteristics Classified According to Ultrasonographic Features Using a K-Means Clustering Algorithm. Diagnostics, 12(2). https://doi.org/10.3390/diagnostics12020264

Cohn, R., & Holm, E. (2021). Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data. Integrating Materials and Manufacturing Innovation, 10(2), 231–244. https://doi.org/10.1007/s40192-021-00205-8

Darmayadi, A., & Megits, N. (2023). the Impact of the Russia-Ukraine War on the European Union Economy. Journal of Eastern European and Central Asian Research, 10(1), 46–55. https://doi.org/10.15549/jeecar.v10i1.1079

Dmitry, N., & Yerkebulan, B. (2022). Clustering of Dark Patterns in the User Interfaces of Websites and Online Trading Portals (E-Commerce). Mathematics, 10(18). https://doi.org/10.3390/math10183219

Govender, P., & Sivakumar, V. (2020). Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019). In Atmospheric Pollution Research (Vol. 11). Turkish National Committee for Air Pollution Research and Control. https://doi.org/10.1016/j.apr.2019.09.009

Haque, U., Naeem, A., Wang, S., Espinoza, J., Holovanova, I., Gutor, T., … Nguyen, U. S. D. T. (2022). The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days. BMJ Global Health, 7(9), 1–11. https://doi.org/10.1136/bmjgh-2022-009550

Kaparang, D. R., Moningkey, M. J. M., & Sumual, H. (2021). The Distribution Pattern Of New Students Admissions Using The K-Means Clustering Algorithm. International Journal of Information Technology and Business, 3(2), 52–60. Retrieved from https://ejournal.uksw.edu/ijiteb/article/view/4632

Lee, C., & Chung, M. (2016). Digital Forensic for Location Information using Hierarchical Clustering and k-means Algorithm. Journal of Korea Multimedia Society, 19(1), 30–40. https://doi.org/10.9717/kmms.2016.19.1.030

Mailund, T. (2017). Beginning Data Science in R. In Beginning Data Science in R. https://doi.org/10.1007/978-1-4842-2671-1

Nerlinger, M., & Utz, S. (2022). The impact of the Russia-Ukraine conflict on energy firms: A capital market perspective. Finance Research Letters, 50(May), 103243. https://doi.org/10.1016/j.frl.2022.103243

Osokina, O., Silwal, S., Bohdanova, T., Hodes, M., Sourander, A., & Skokauskas, N. (2022). Impact of the Russian Invasion on Mental Health of Adolescents in Ukraine. Journal of the American Academy of Child and Adolescent Psychiatry, 1–9. https://doi.org/10.1016/j.jaac.2022.07.845

Paul, A. (2015). The EU in the South Caucasus and the Impact of the Russia-Ukraine War. International Spectator, 50(3), 30–42. https://doi.org/10.1080/03932729.2015.1054223

Peng, R. D. (2015). R Programming for Data Science. The R Project; R Foundation, 132. https://doi.org/10.1073/pnas.0703993104

Shelly, Z., Burch, R. F. V., Tian, W., Strawderman, L., Piroli, A., & Bichey, C. (2020). Using K-means clustering to create training groups for elite american football student-athletes based on game demands. International Journal of Kinesiology and Sports Science, 8(2), 47–63. https://doi.org/10.7575//aiac.ijkss.v.8n.2p.47

Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796

Yuan, C., & Yang, H. (2019). Research on K-Value Selection Method of K-Means Clustering Algorithm. J, 2(2), 226–235. https://doi.org/10.3390/j2020016

Zhu, C., Idemudia, C. U., & Feng, W. (2019). Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Informatics in Medicine Unlocked, 17(January), 100179. https://doi.org/10.1016/j.imu.2019.100179

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Published

2023-03-25

How to Cite

Utomo, W. (2023). Clustering the Impacts of The Russia-Ukraine War on Personnel and Equipment. Jurnal Riset Informatika, 5(2), 237–244. https://doi.org/10.34288/jri.v5i2.215

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Articles