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Analisa Visualisasi Data Covid – 19 Di Indonesia Menggunakan Tableau Big Data Pangestu, Prayoga; Hartama, Dedy; Suhendro, Dedi; Damanik, Abdi Rahim; Oktaviani, Selli
BEES: Bulletin of Electrical and Electronics Engineering Vol 4 No 2 (2023): November 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bees.v4i2.4634

This study explains the benefits of data analysis by visualization of Big data inoptimizing cases of the spread of COVID-19 in Indonesia. The data used is the COVID-19 Indonesia time series data from the Kaggle website. In this study, the author used tableau tools to analyze data based on worksheets of covid-19 distribution maps, COVID-19 statistics, details of active cases per province, death cases per province, active cases, death cases, confirmed cases and produced a dashboard of COVID-19 data. The results of the analysis obtained using visualizations in the form of graphs are very fast and optimize data processing so that they can find out which provinces are affected by active cases and deaths of COVID-19 from the highest to the lowest in Indonesia and can be used in decision and policy making for the Indonesian government.

Penerapan Algoritma K-Means Dalam Mengelompokkan Jumlah Penerimaan Sinyal Telepon Seluler Di Sumatera Utara Sitompul, Wati Rizky Pebrianti; Solikhun, Solikhun; Saputra, Widodo; Oktaviani, Selli; windarto, agus perdana
Bulletin of Artificial Intelligence Vol 2 No 2 (2023): October 2023
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v2i2.137

The purpose of this study was to cluster the number of cell phone signal reception in North Sumatra. The source of the data used is obtained from BPS. The variable used is the number of cell phone reception signals in North Sumatra. This research uses Data Mining Technique with K-means algorithm. It is hoped that the results of this study can provide input to the North Sumatra Province in order to determine the reception of cellular telephone signals, so as to increase the growth and development of telephone signal reception in North Sumatra. And 4G/LTE data obtained that there are 4 high clusters, namely (Mandailing Natal, Simalungun, Deli Serdang, Padang Lawas), 17 medium clusters, namely (Nias, South Tapanuli, Labuan Batu, Humbang Hasundutan, West Pakpak, Samosir, Labuhan Batu, South , Labuhan Batu Utara, North Nias, West Nias, Sibolga, Tanjung Balai, Pematangsiantar, Tebing Tinggi, Binjai, Padang Sidempuan, Gunung Sitoli), and there are 2 low clusters (Central Tapanuli, North Tapanuli, Toba, Asahan, Dairi, Karo , Langkat, South Nias, Serdang Bedagai, Batu Bara, North Padang Lawas, Medan).

OPTIMIZATION OF K-MEANS AND K-MEDOIDS CLUSTERING USING DBI SILHOUETTE ELBOW ON STUDENT DATA Hartama, Dedy; Oktaviani, Selli
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 2 (2025): Maret 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3531

Abstract: Clustering methods such as K-Means and K-Medoids are often used to analyze data, including student data, due to their efficiency. However, this method has weaknesses, such as sensitivity to selecting cluster centers (centroids) and cluster results that depend on medoid data. Clustering, an essential technique in data analysis, aims to reveal the natural structure of the data, even in the absence of labeled information. The study, conducted with complete objectivity, compared the performance of two popular clustering methods, K-Means, and K-Medoids, on student data. Three evaluation metrics, namely the Davies-Bouldin Index (DBI), silhouette score, and elbow method, were used to compare clustering and determine the ideal number of clusters for the two algorithms. The data taken in this study are in the form of names, attendance, assignments, formative, midterm exams, final exams, and quality numbers. Based on the existing optimization results, it can be concluded that the K-Means method excels in grouping Student Data. The best results were obtained from the K-Means Algorithm with the Silhouette Coefficient Method with a value of 0.7509 in cluster 2, and the Elbow Method with a value of 1428076.08 in cluster 2, DBI K-Medoids with a value of 0.7413 in cluster 3. So, the best cluster lies in 3 clusters. Keywords: clustering; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score; Abstrak : Metode clustering seperti K-Means dan K-Medoids sering digunakan untuk menganalisis data, termasuk data siswa, karena efisiensinya. Namun, metode ini memiliki kelemahan, seperti sensitivitas terhadap pemilihan pusat klaster (centroids) dan hasil klaster yang bergantung pada data medoid. Clustering, sebuah teknik penting dalam analisis data, bertujuan untuk mengungkapkan struktur alami dari data, bahkan tanpa adanya informasi berlabel. Penelitian ini, yang dilakukan dengan objektivitas penuh, membandingkan kinerja dua metode clustering populer, yaitu K-Means dan K-Medoids, pada data mahasiswa. Tiga metrik evaluasi, yaitu Davies-Bouldin Index (D.B.I.), silhouette score, dan metode elbow, digunakan untuk membandingkan clustering dan menentukan jumlah cluster yang ideal untuk kedua algoritma tersebut. data yang diambil dalam penelitian ini berupa nama, kehadiran, tugas, formatif, ujian tengah semester, ujian akhir semester, angka mutu. Berdasarkan hasil optimasi yang ada, dapat disimpulkan bahwasannya metode K-Means unggul dalam pengelompokkan Data Mahasiswa. Sehingga di peroleh hasil terbaik dari Algoritma K-Means dengan Metode Silhouette Coefficient dengan nilai 0,7509 di cluster 2, dan Elbow Method dengan nilai 1428076,08 di cluster 2, DBI K-Medoids dengan nilai 0,7413 di cluster 3. Sehingga cluster terbaik terletak pada 3 cluster. Kata kunci: klasterisasi; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;

OPTIMIZATION OF K-MEANS AND K-MEDOIDS CLUSTERING USING DBI SILHOUETTE ELBOW ON STUDENT DATA Hartama, Dedy; Oktaviani, Selli
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 2 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3531

Abstract: Clustering methods such as K-Means and K-Medoids are often used to analyze data, including student data, due to their efficiency. However, this method has weaknesses, such as sensitivity to selecting cluster centers (centroids) and cluster results that depend on medoid data. Clustering, an essential technique in data analysis, aims to reveal the natural structure of the data, even in the absence of labeled information. The study, conducted with complete objectivity, compared the performance of two popular clustering methods, K-Means, and K-Medoids, on student data. Three evaluation metrics, namely the Davies-Bouldin Index (DBI), silhouette score, and elbow method, were used to compare clustering and determine the ideal number of clusters for the two algorithms. The data taken in this study are in the form of names, attendance, assignments, formative, midterm exams, final exams, and quality numbers. Based on the existing optimization results, it can be concluded that the K-Means method excels in grouping Student Data. The best results were obtained from the K-Means Algorithm with the Silhouette Coefficient Method with a value of 0.7509 in cluster 2, and the Elbow Method with a value of 1428076.08 in cluster 2, DBI K-Medoids with a value of 0.7413 in cluster 3. So, the best cluster lies in 3 clusters. Keywords: clustering; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score; Abstrak : Metode clustering seperti K-Means dan K-Medoids sering digunakan untuk menganalisis data, termasuk data siswa, karena efisiensinya. Namun, metode ini memiliki kelemahan, seperti sensitivitas terhadap pemilihan pusat klaster (centroids) dan hasil klaster yang bergantung pada data medoid. Clustering, sebuah teknik penting dalam analisis data, bertujuan untuk mengungkapkan struktur alami dari data, bahkan tanpa adanya informasi berlabel. Penelitian ini, yang dilakukan dengan objektivitas penuh, membandingkan kinerja dua metode clustering populer, yaitu K-Means dan K-Medoids, pada data mahasiswa. Tiga metrik evaluasi, yaitu Davies-Bouldin Index (D.B.I.), silhouette score, dan metode elbow, digunakan untuk membandingkan clustering dan menentukan jumlah cluster yang ideal untuk kedua algoritma tersebut. data yang diambil dalam penelitian ini berupa nama, kehadiran, tugas, formatif, ujian tengah semester, ujian akhir semester, angka mutu. Berdasarkan hasil optimasi yang ada, dapat disimpulkan bahwasannya metode K-Means unggul dalam pengelompokkan Data Mahasiswa. Sehingga di peroleh hasil terbaik dari Algoritma K-Means dengan Metode Silhouette Coefficient dengan nilai 0,7509 di cluster 2, dan Elbow Method dengan nilai 1428076,08 di cluster 2, DBI K-Medoids dengan nilai 0,7413 di cluster 3. Sehingga cluster terbaik terletak pada 3 cluster. Kata kunci: klasterisasi; davies-bouldin indek; elbow method; k-means; k-medoids; silhouette score;

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