Penggunaan Internet Dikalangan Siswa SD di Kota Ternate: Suatu Survey, Penerapan Algoritma Clustering dan Validasi DBI

Tempola, Firman; Muhammad, Miftah; Mubarak, Abdul

doi:10.25126/jtiik.2020722370

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…K-means is one part of the clustering algorithm, including on unsupervised learning (Tempola et al, 2020). K-Means is an algorithm that groups objects with the same characteristics into a cluster, which is determined repeatedly by the value of k (Amalia et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.

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Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

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“…Davies Bouldin Index (DBI) digunakan untuk mengevaluasi terhadap hasil klaster yang sudah terbentuk. DBI merupakan teknik mengukur tingkat validitas secara internal yang dilakukan dengan cara mengukur seberapa baik tingkat klasterisasi yang di hasilkan dengan menghitung jumlah banyaknya kriteria atau fitur lainnya dari dataset yang ada [6]. Tujuan dari pendekatan dalam pengukuran ini adalah untuk memaksimalkan jarak antara objek klaster yang satu dengan yang objek data pada klaster lainnya dan juga secara bersamaan meminimalkan jarak antara objek dalam sebuah klaster.…”

Section: Pendahuluanunclassified

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

Supriyadi

Triayudi

Sholihati

2021

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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Armada kendaraan truk merupakan salah satu aset utama dalam bisnis di bidang jasa transportasi. Evaluasi kinerja amada secara cepat dan akurat diperlukan untuk mendukung tercapainya produktivitas armada secara maksimal sehingga target perusahaan dapat mudah tercapai. Proses evaluasi kinerja armada yang masih dilakukan secara manual menyebabkan rumitnya proses pengolahan dan kurang akurat nya hasil evaluasi yang diperoleh, sehingga diperlukan suatu teknik pengolahan data secara cepat dan lebih akurat salah satunya dengan namun menerapkan teknik data mining menggunakan metode clustering. Metode Clustering akan digunakan untuk mengelompokkan setiap armada kendaraan berdasarkan produktivitas kinerjanya. Penelitian ini membandingkan penerapan Algoritma K-Means dan K-Medoids, yang kemudian dilakukan uji validitas terhadap hasil cluster yang terbentuk. Davies Bouldin Index sebagai metode dalam analisis klaster menghasilkan nilai validitas sebesar 0,67 untuk K-Means clustering dan 1,78 untuk K-Medoids. Berdasarkan nilai validitas yang dihasilkan Algoritma K-Means dipilih untuk diimplementasikan pada pembuatan aplikasi clustering armada kendaraan berbasis web karena paling relevan dengan nilai validitas DBI yang lebih rendah dari pada K-Medoids. Pengujian yang telah dilakukan terhadap hasil clustering pada aplikasi web didapatkan persentase kesesuaian sebesar 97 % baik dengan tool Rapidminer maupun dengan perhitungan secara manual

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“…The smaller the DBI value, the better the cluster. DBI value is formulated as follows (Tempola, Muhammad, & Mubarak, 2020):…”

Section: Evaluation and Validitymentioning

confidence: 99%

Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback

Aditya

Fitrianah

2021

JRisInf

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The customer is a stakeholder for a business, to maintain and increase customer enthusiasm and develop it for the company's performance, it is necessary to do customer segmentation which aims to find out potential customers. This study uses purchase transaction data from Brand Limback customers in the period 2020. The use of RFM (Recency, Frecuency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The datasets generated from RFM are grouped using the Fuzzy C-Means and K-Means algorithms, the two algorithms will compare the quality in the formation of clusters using the Silhoutte Coefficient and Davies-Bouldin Index methods. Customer segmentation from the RFM dataset that has been clustered produces 7 optimal clusters, namely Cluster 0 is a bronze customer. Cluster 1 is a silver customer. Cluster 2 is a gold customer. Cluster 3 is a platinum customer. Cluster 4 is a diamond customer. Cluster 5 is a super customer, and cluster 6 is a superstar customer. The cluster validation of k-means using the silhouette coefficient produces a value of 0.934 while the Davies bouldin index produces a value of 0.155 and the validation results of the fuzzy c-means algorithm using the silhouette coefficient produces a value of 0.921 while the Davies bouldin index produces a value of 0.145.

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Penggunaan Internet Dikalangan Siswa SD di Kota Ternate: Suatu Survey, Penerapan Algoritma Clustering dan Validasi DBI

Cited by 13 publications

References 4 publications

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Perbandingan Algoritma K-Means Dengan K-Medoids Pada Pengelompokan Armada Kendaraan Truk Berdasarkan Produktivitas

Comparative Study of Fuzzy C-Means and K-Means Algorithm for Grouping Customer Potential in Brand Limback

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