Aplikasi Pemetaan Kualitas Pendidikan di Indonesia Menggunakan Metode K-Means

Nugraha, Gibran Satya; Hairani, Hairani

doi:10.30812/matrik.v17i2.84

Cited by 9 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In a comprehensive direction, the clustering process enables us to map the quality of education (Ananda, 2019;Nugraha & Hairani, 2018;Oktavianty & Handayani, 2019;Subanar, 2011). In response to this, every province's management of senior secondary schools is pivotal for in-depth analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Cluster analysis was outlined by performing the Kmeans approach in distributing senior secondary school teachers in Indonesia (Widiyaningtyas et al, 2017). Other studies also exposed the application of cluster analysis in mapping the quality of education in Indonesian school settings (Ananda, 2019;Nugraha & Hairani, 2018;Oktavianty et al, 2019;Prayoga & Zain, 2016;Wijayanto, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

K-means clustering for classifying the quality management of secondary education in Indonesia

Mustakim

Kamal

2021

View full text Add to dashboard Cite

Mapping educational management is critical for improving national education quality. This study aims to classify Indonesian provinces in terms of secondary education management within quality standard parameters. Framed in a quantitative approach with the K-means cluster analysis, this study applied eleven parameters to classify the provinces into three main clusters. These parameters were schools, new entrants, students, repeaters, drop-outs, graduates, teachers, classrooms, laboratories, libraries, and school health services. Secondary data related to secondary education in Indonesia published by the Ministry of Education and Culture were analyzed. With the R software, the clusters were formed based on the shared characteristics of the provinces. This study found that Cluster 1 had twenty-two provinces, Cluster 2 two provinces, and Cluster 3 ten provinces. Cluster 2 was found to be the best cluster, while West Java and East Java shared similar characteristics, hence in the same cluster. Further, 5.88% of the provinces were eligible to be pilot models for the standard quality management of education in Indonesia. There is, therefore, a pressing need for the improvement of education infrastructure to support a better-quality education.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

K-means clustering for classifying the quality management of secondary education in Indonesia

Mustakim

Kamal

2021

View full text Add to dashboard Cite

show abstract

“…Hal tersebut menjadi tantangan utama bagi para pelaku dunia pendidikan untuk terus melakukan perkembangan demi kemajuan pendidikan Indonesia. Pemerataan kualitas pendidikan di setiap daerah merupakan langkah yang dapat dilakukan untuk peningkatan kualitas pendidikan di seluruh Indonesia [2]. Bukan hal yang buruk maupun sulit untuk dilakukan, tetapi hanya saja sistem pendidikan Indonesia belum dilaksanakan secara optimal [3].…”

Section: Pendahuluanunclassified

“…Himpunan data yang telah di bobotkan dilakukan normalisasi untuk meratakan ukuran jarak dan keragaman data pada atribut yang akan digunakan pada perhitungan clustering sehingga menghindari data yang outlier [2]…”

Section: Normalisasi Dataunclassified

Simulasi Rancangan Pemetaan Sekolah dengan Metode Algoritma Machine Learning Menggunakan Software RapidMiner

Aprilia

Aribowo

2022

SST

View full text Add to dashboard Cite

The growth in the number of educational institutions creates competition which encourages each educational institution to have a special strategy to deal with it. The challenge that must also be faced in the world of education is the uneven quality of education in various regions. One way to face the challenge of equal distribution of school quality is to create programs that suit the needs of each school. In this study, solving the problem of mapping schools owned by the XYZ educational foundation was carried out. The results of this study obtained a design of a school mapping indicator instrument to assess the quality of each school. Then obtained a simulation of school mapping design from the results of unsupervised learning with the K-Means and K-Medoids Clustering methods, as well as a simulation of predicting school mapping patterns from the results of supervised learning with the Decision Tree C4.5 method. The results of K-Medoids were selected for the proposed school mapping with a Davies Bouldin index value of 0.112. The model cluster owned by K-Medoids, namely Excellent School, has 44 schools; at Good School, there are 36 schools; and in the Improvement School, there are 20 schools. Meanwhile, the prediction pattern with Decision Tree C4.5 obtained rules with the dominant indicator attributes (IND) 1, 2, 3, and 4. Also, the prediction simulation results using the 80:20 ratio decision tree model show the new testing data with the assumption that the 101st school goes to cluster_1 with an accuracy rate of 95%.Keywords - Decision tree, K-means, K-medoids, School mapping indicator instrument.

show abstract

“…Diantara beberapa teknik yang ada dalam data mining ini adalah Teknik data mining yang lain adalah pengelompokkan, dimana dalam pengelompokkan ini digunakan untuk mencari kelompok data yang memiliki karakteristik tertentu. Pengelompokkan karakteristik data rumah tangga [1], pemetaan kualifikasi pendidikan di Indonesia [2], pengelompokkan karakteristik pelanggan telepon [3] dan pengelompokkan dokumen berbasis teks [4]. Penelitian sebelumnya adalah analisis dua metode dimana hasil pengelompokkan dengan metode K-means lebih baik dari pengelompokkan menggunakan metode Agglomerative Hierarchichal Clustering (AHC).…”

Section: Pendahuluanunclassified

Pengelompokkan Mahasiswa Akademik Keperwatan Berdasarkan Asal Sekolah dan Nilai Akademik Menggunakan Metode Clustering K-Means

Zahrotun

Fajri

Jones

et al. 2021

bits

View full text Add to dashboard Cite

Nursing Academic of Karya Bakti Husada (AKPER KBH) Bantul is one of the academics that opened the 2000 department. Based on an interview with the Director of AKPER KBH, the registration requirements to become a student of the Academy are currently graduates from all majors and all high schools. AKPER KBH has not analyzed student data whether there is a relationship between high school history and passing grades of GPA as an evaluation material in the learning process, although at this time with the variation of new students causing difficulty in learning difficult compared to before, while GPA achievement is very important in finding job after graduation. The purpose of this study is to classify academic data of AKPER students based on data on school origin, GPA scores, and Medical Surgical Nursing II (KMB II), Mental Nursing II (Kep Jiwa II), Child Nursing II (Kep Anak II), Maternity Nursing II (Kep Maternitas II), and Medical Surgical Nursing ( KMB V). The stages in this study include data collection, data search, data selection, data transformation, data grouping using the K-Means method and knowledge representation, the test used in this study is the purity test. From the experiments conducted, the accuracy value is 0.924 with the number of clusters 3

show abstract

Aplikasi Pemetaan Kualitas Pendidikan di Indonesia Menggunakan Metode K-Means

Cited by 9 publications

References 0 publications

K-means clustering for classifying the quality management of secondary education in Indonesia

K-means clustering for classifying the quality management of secondary education in Indonesia

Simulasi Rancangan Pemetaan Sekolah dengan Metode Algoritma Machine Learning Menggunakan Software RapidMiner

Pengelompokkan Mahasiswa Akademik Keperwatan Berdasarkan Asal Sekolah dan Nilai Akademik Menggunakan Metode Clustering K-Means

Contact Info

Product

Resources

About