The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Ghufron, Ghufron; Surarso, Bayu; Gernowo, Rahmat

doi:10.21107/kursor.v10i3.232

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…The uses of the K-medoids have been also applied in various research fields, one can see e.g. [10], [11], [16]- [19]. The procedure of PAM algorithm can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

Robust Clustering of Covid-19 Pandemic Worldwide

Wibowo

Nisa

Venelia

et al. 2022

BAREKENG: J. Il. Mat. & Ter.

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COVID-19 pandemic is described as the most challenging crisis that humans have faced since World War II. From December 2019 until August 2021 based on the dataset provided by WHO, globally 219 countries in the world are affected by this virus. There are 205.338.159 cases cumulative total and 4.333.094 death cumulative total caused by this virus. In this paper, the data of 219 countries are analyzed using a robust clustering method namely K-Medoids cluster analysis. Based on the result, 219 countries in the world can be divided into five clusters based on four COVID-19-related variables, i.e. the number of cases cumulative total, death cumulative total, positive cases per capita, and case fatality rate. The distribution of the countries in five clusters was as follows; the first cluster contained 48 countries, the second cluster contained 3 countries, the third and fourth clusters contained 16 and 89 countries respectively, and the last cluster contained 63 countries. The largest cluster is the fourth one, containing countries that form a cluster with a centroid below the world average, and the smallest cluster is the second cluster with the high cases in all attributes, consisting of the USA, India, and Brazil.

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“…The uses of the K-medoids have been also applied in various research fields, one can see e.g. [10], [11], [16]- [19]. The procedure of PAM algorithm can be summarized as follows:…”

Section: Methodsmentioning

confidence: 99%

Robust Clustering of Covid-19 Pandemic Worldwide

Wibowo

Nisa

Venelia

et al. 2022

BAREKENG: J. Il. Mat. & Ter.

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“…Dalam penelitian ini, mereka menggabungkan perhitungan bobot matriks akreditasi perguruan tinggi untuk mengklasifikasikan program studi. Hasilnya menunjukkan bahwa klasifikasi K-Medoids yang digunakan termasuk dalam kategori kluster yang baik, dievaluasi dengan menggunakan Davies Boulding Index dengan nilai evaluasi kluster [16]. Dan yang terakhir penelitian Halimatusakdiah dan rekan-rekan melakukan penerapan Algoritma K-Medoids untuk mengelompokkan balita stunting di Indonesia.…”

Section: Pendahuluanunclassified

Klasterisasi Tingkat Penjualan Kedai Kopi Hallo Burjois Menggunakan Algoritma K-Medoids Sebagai Evaluasi

Pradana Rizki Maulana,

April Lia Hananto,

Agustia Hananto

et al. 2024

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Kedai Hallo Burjois yang sedang mengalami tantangan untuk memperluas jangkauan produk menu produk, penelitian ini menerapkan algoritma K-Medoids Clustering untuk meningkatkan strategi promosi dengan mengidentifikasi menu-menu yang memiliki tingkat minat pembeli rendah. Proses tersebut melibatkan pengolahan data penjualan harian yang diubah menjadi format bulanan, di mana algoritma K-Medoids digunakan untuk membentuk tiga kluster yang mewakili tingkat penjualan tinggi, sedang, dan rendah. Hasil klasterisasi menunjukkan adanya menu-menu dengan penjualan rendah sebanyak 6 item, antara lain Americano, Caffe Latte, Dark Choco Caramel, Dimsum, Hazelnut Latte, dan Pasta Carbonara. Lalu kami mengadopsi prinsip 4P (Product, Price, Place & Promotion) untuk mengevaluasi produk dengan tingkat penjualan terendah. Uji validitas dilakukan menggunakan Davies Boulding Index (DBI), menunjukkan keakuratan dan konsistensi hasil klasteriasasi sebesar 0,95 pada tiga kluster.

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“…Step 2. Compute the sum of squares between clusters (SSB), i.e., separation, between clusters by [16]:…”

Section: Dbimentioning

confidence: 99%

Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm

Dinata¹,

Retno²,

Hasdyna³

2021

RIA

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With k-medoids algorithm, it often takes many iterations to cluster a large dataset, that is, the k-medoids algorithm cannot achieve the optimal performance. Based on cluster validity, this paper tries to optimize the clustering performance of k-medoids algorithm, using the purity algorithm. Specifically, the medoids value was determined by the purity value, and cluster validity was measured with the Davies Bouldin Index (DBI) on the Iris Dataset and the Death/Birth Rate Dataset. The results show that the cluster validity of the proposed purity k-medoids algorithm was better than the conventional k-medoids algorithm. The conventional k-medoids converged in an average of 8.7 iterations on the Death Birth Rate Dataset and 13.2 on the Iris Dataset. By contrast, the purity k-medoids algorithm only needed 2 iterations on either dataset. Therefore, the purity k-medoids algorithm can effectively minimize the number of iterations in the clustering of large datasets.

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The Implementations of K-medoids Clustering for Higher Education Accreditation by Evaluation of Davies Bouldin Index Clustering

Cited by 10 publications

References 17 publications

Robust Clustering of Covid-19 Pandemic Worldwide

Robust Clustering of Covid-19 Pandemic Worldwide

Klasterisasi Tingkat Penjualan Kedai Kopi Hallo Burjois Menggunakan Algoritma K-Medoids Sebagai Evaluasi

Minimization of the Number of Iterations in K-Medoids Clustering with Purity Algorithm

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