Improving the School Type Clustering Model on the Foundation Using the K-Means Algorithm (Case Study: Kebon Kelapa Al-Ma'rifah, Cirebon Regency)

Hanifah Nur Aulia; Martanto; Arif Rinaldi Dikananda; Mulyawan

doi:10.59934/jaiea.v4i2.739

Authors

Hanifah Nur Aulia STMIK IKMI Cirebon
Martanto STMIK IKMI Cirebon
Arif Rinaldi Dikananda STMIK IKMI Cirebon
Mulyawan STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.739

Keywords:

Grouping, K-Means, Davies-Bouldin Index, Kebon Kelapa Al-Ma'rifah Foundation

Abstract

This study aims to improve the school type grouping model at the Kebon Kelapa Al-Ma'rifah Foundation, Cirebon Regency, using the K-Means algorithm. Data-based grouping is very important in supporting efficient education management, especially in environments that have various types of schools such as Madrasah Aliyah (MA), Vocational High School (SMK), Madrasah Tsanawiyah (MTs), and Madrasah Ibtidaiyah (MI). The data used comes from the New Student Registration (PPDB) dataset for the 2023–2024 school year, with demographic attributes such as name, place of birth, gender, and time of school entry. The evaluation of clustering quality was carried out using the Davies-Bouldin Index (DBI) to determine the optimal number of clusters. The results show that the optimal number of clusters is K=5 with the lowest DBI value of 0.201, which results in compact and well-separated clusters. The implementation of the K-Means algorithm helps the foundation understand the distribution pattern of students based on attributes such as gender, region, and entry time. This research provides practical benefits, including more targeted resource allocation, improved quality of education, and efficiency in school management. In addition, this research contributes to the development of data mining models in the education sector and opens up opportunities for the exploration of additional attributes such as academic achievement and socioeconomic conditions. Further research is suggested to use alternative algorithms such as K-Medoids or DBSCAN.

Downloads

Download data is not yet available.

References

R. Fauziah and A. I. Purnamasari, "Implementation of K-Means Algorithm in Cases of Violence against Children and Women Based on Age," Hello World J. Computation., vol. 2, no. 1, pp. 34–41, 2023, doi: 10.56211/helloworld.v2i1.232.

E. M. Fitri, R. R. Suryono, and A. Wantoro, "Clustering Sales Data by Region Using the K-Means Method on Pt XYZ," J. Computing, vol. 11, no. 2, pp. 157–168, 2023, doi: 10.23960/computing.v11i2.12582.

R. D. Romadhona and A. A. Rofiq, "K-Means Clustering Algorithm as a Determination of PIP Scholarship at SMPN 9 Blitar City," JASIEK (Apl. Science, Information, Electron. and Computers), vol. 5, no. 1, pp. 25–30, 2023, doi: 10.26905/jasiek.v5i1.10162.

I. D. Murti Suyoto, T. Rachmadi, and L. T. Parulian, "Determining the Right Cluster with K-Means in the Context of Measuring the Effectiveness of Budget Implementation at the Ministry of Agrarian and Spatial Planning/Land Agency," Infotech J. Technol. Inf., vol. 8, no. 1, pp. 13–22, 2022, doi: 10.37365/jti.v8i1.126.

Pelsri Ramadar Noor Saputra and A. Chusyairi, "Comparison of Clustering Methods in Grouping Puskesmas Data on Complete Basic Immunization Coverage," J. RESTI (Engineering, Sis. and Technol. Information), vol. 4, no. 6, pp. 5–12, 2020, doi: 10.29207/resti.v4i6.2556.

W. W. Kristianto and C. Rudianto, "Application of Data Mining in Product Sales Using the K-Means Clustering Method (Case Study of Footwear Stores)," no. 5, pp. 90–98, 2022.

K. Jukandika, D. Hartama, R. Dewi, S. R. Andani, and Irawan, "Determining Classes Using the K-Means Method Based on Student IQ Scores at Mora College Pematangsiantar Tutoring," JUKI J. Komput. and Inform.Vol. 3 No. 2 pp. 69–75, 2021, doi:10.53842/zuki.v3i2.65.

D. Riana et al., "Identification of Pap Smear Images of RepoMedUNM Using K-Means Clustering and GLCM," J. RESTI (Engineering, Sis. and Technol. Information), vol. 6, no. 1, pp. 1–8, 2022, doi: 10.29207/resti.v6i1.3495.