Analisis Cluster Faktor Penunjang Pendidikan Menggunakan Algoritma K-Means (Studi Kasus: Kabupaten Karawang)

Abstract

Education is one of the means to create and improve the quality of better human resources. This is expected to improve human welfare. Based on data from the Ministry of Education and Culture, there are 4 sub-districts in Karawang Regency that do not have state high schools. This can result in difficulties for students who have financial deficiencies which can ultimately lead to dropping out of school. Then apart from that distance can also be an obstacle. The purpose of this study is to apply the clustering method for the distribution of educational supporting factors in Karawang District so that later this research can improve the quality of education evenly in Karawang District, not only concentrated in certain areas by paying attention to educational supporting factors. The algorithm used in this research is K-Means. The elbow method used in determining the optimal cluster supported by the silhouette method resulted in the best number of clusters being 2 clusters. The results of the clustering evaluation resulted in Davies-Bouldin Index (DBI) value of 0.408 and Silhouette Coefficient value of 0.747 (strong structure). Cluster 1 consists of 7 sub-districts and Cluster 2 consists of 23 sub-districts. Based on the results of clustering analysis, Cluster 1 has the average number of attributes of schools, teachers, classes, laboratories, libraries at all levels of education (SD, SMP, SMA, SMK, and SLB) with state status and the population shows higher results if compared with the average number of each attribute in Cluster 2. So it can be concluded that Cluster 1 is a high category and Cluster 2 is a low category.

Time Series Forecasting of Top 3 Ranking Cryptocurrencies

“…Data Cleansing: the process of cleaning data from empty values, inconsistent, empty attributes such as missing values and noisy data [20]; 2. Data Integration: merging data into one archive [21]; 3. Data Reduction: eliminating unnecessary attributes [22].…”

Optimalisasi Jumlah Cluster Data Sekolah Dasar (Sd) Menggunakan Algoritma K-Means Clustering

“…Metode penelitian ini menggunakan pendekatan analisis data klasterisasi menggunakan algoritma K-Means Clustering berdasarkan nilai Davies Bouldin Index (DBI), dan Measure types Numerical Measures dengan jenis Euclidean Distance, dan Manhattan Distance, kemudian iterasi juga digunakan dalam proses klasterisasi. Algoritma K-Means Clustering dipilih karena mampu mengelompokkan objek berdasarkan kesamaan karakteristik dalam satu cluster [6]. Selanjutnya, dilakukan pengujian akurasi dengan metode Davies Bouldin Index (DBI) untuk mengevaluasi performa terbaik klasterisasi dalam menghasilkan cluster-cluster yang optimal [7].…”

Implementasi Algoritma K-Means Dengan Optimize Parameter Grid Pada Data Kecelakaan Lalu Lintas Di Kota Cirebon

“…Knowledge Discovery in Database (KDD) adalah suatu pendekatan penelitian yang bertujuan untuk memperoleh informasi dari dalam suatu basis data [9]. KDD juga merupakan suatu metode analisa data yang biasa digunakan dalam proses data mining.…”