Application of Data Mining to Measure Student Intelligence Level Using The K-Means Method
Abstract
Intelligence is an individual's ability to understand, learn, and think to solve complex problems. Human intelligence includes intellectual, emotional, spiritual, and multiple intelligences. Asically, a person has different intelligence in many fields of discipline. This study focuses on measuring the level of intellectual intelligence of students at Takhassus Abu Hurairah Sukorejo School using the K-Means Clustering method implemented using the Rapidminer application. The K-Means method was chosen because the process is simple and relatively easy to apply to large datasets. Students are grouped into three clusters based on intelligence levels based on the closest distance to the centroid. The results show that the cluster with the closest centroid distance has the highest intelligence characteristics, while the cluster with the furthest centroid distance shows lower intelligence. With a Davies-Bouldin value of 0.599, it shows that the grouping applied is quite effective and optimal. The results of this study can be a reference for schools to determine the right learning strategy for each group of students that can increase the effectiveness of learning in schools.
References
[2] A. Wasik et al., “Implementasi data mining untuk memprediksi penjualan accessoris handphone dan handphone terlaris menggunakan metode k-nearest neighbor (k-nn) 1,” vol. 1, no. 2, pp. 469–479, 2024.
[3] Y. Ardilla et al., DATA MINING DAN APLIKASINYA. Penerbit Widina, 2021. [Online]. Available: https://books.google.co.id/books?id=53FXEAAAQBAJ
[4] M. S. Iskandar and Z. Fatah, “Gudang Jurnal Multidisiplin Ilmu Implementasi Metode Algoritma K-Means Clustering Untuk Menentukan Penerima Program Indonesia Pintar ( PIP ),” vol. 2, no. November, pp. 1–8, 2024.
[5] A. W. Aranski and K. Handoko, “Data Mining Dalam Pengelompokan Nilai Iq Siswa,” J. Teknol. Dan Open Source, vol. 2, no. 2, pp. 13–22, 2019, doi: 10.36378/jtos.v2i2.347.
[6] M. Norshahlan, H. Jaya, and R. Kustini, “Penerapan Metode Clustering Dengan Algoritma K-means Pada Pengelompokan Data Calon Siswa Baru,” J. Sist. Inf. Triguna Dharma (JURSI TGD), vol. 2, no. 6, p. 1042, 2023, doi: 10.53513/jursi.v2i6.9148.
[7] D. Jollyta, A. Hajjah, E. Haerani, and M. Siddik, Algoritma Klasifikasi untuk Pemula Solusi Python dan RapidMiner. Deepublish, 2023. [Online]. Available: https://books.google.co.id/books?id=y84TEQAAQBAJ
[8] Z. Setiawan et al., BUKU AJAR DATA MINING. PT. Sonpedia Publishing Indonesia, 2023. [Online]. Available: https://books.google.co.id/books?id=1nLVEAAAQBAJ
[9] P. W. Rahayu et al., Buku Ajar Data Mining. PT. Sonpedia Publishing Indonesia, 2024. [Online]. Available: https://books.google.co.id/books?id=vCruEAAAQBAJ
[10] PEMODELAN K- MEANS ALGORITMA DAN BIG DATA ANALYSIS (PEMETAAN DATA MUSTAHIQ). Pascal Books, 2022. [Online]. Available: https://books.google.co.id/books?id=_bJmEAAAQBAJ
[11] E. A. Novia, W. I. Rahayu, and C. Prianto, SISTEM PERBANDINGAN ALGORITMA K-MEANS DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRIORITAS PEMBAYARAN TAGIHAN RUMAH SAKIT BERDASARKAN TINGKAT KEPENTINGAN. Kreatif. [Online]. Available: https://books.google.co.id/books?id=MND9DwAAQBAJ
[12] D. Davies and D. Bouldin, “A Cluster Separation Measure,” Pattern Anal. Mach. Intell. IEEE Trans., vol. PAMI-1, pp. 224–227, 1979, doi: 10.1109/TPAMI.1979.4766909.
[13] S. T. M. K. Yahya, Data Mining. CV Jejak (Jejak Publisher), 2022. [Online]. Available: https://books.google.co.id/books?id=0J2mEAAAQBAJ
