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All Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Teknik Informatika C.I.T. Medicom
Muhammad Adam Aslamsyah
Institut Teknologi Sumatera
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Visualisasi Data Progres Program Vaksinasi COVID-19 Internasional Berbasis Tableau Andika Setiawan; Meida Cahyo Untoro; Ahmad Agung Zefi Syahputra; Muhammad Alfarizi Tazkia; Anastasia Puteri Dewi; Muhammad Adam Aslamsyah; Muhammad Zulfarhan
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 4 No 1 (2022): Volume 4, Nomor 1, April 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v4i1.441

Abstract

Vaksinasi COVID-19 merupakan program pemerintah global yang dilakukan oleh seluruh negara di dunia. Vaksinasi COVID-19 dapat membuat tubuh menimbulkan kekebalan tubuh yang dapat menyerang virus COVID-19, sehingga dalam kasus positif COVID-19 dapat mengalami penurunan dan dunia segera pulih dari wabah virus ini. Progres vaksinasi yang ada berupa data dan hanya dapat dimengerti oleh beberapa orang. Kami melakukan penelitian dengan mengolah data progres vaksinasi global melalui pendekatan kuantitatif, hasil yang kami dapatkan dari penelitian ini yaitu mengetahui informasi jenis vaksin yang paling banyak digunakan yaitu Oxford/AstraZeneca sebanyak 165 negara, Pfizer/BioNTech sebanyak 101 negara dan Moderna dan Sinopharm sebanyak 47 negara. Berdasarkan jumlah penduduk yang ada di dunia, negara China merupakan negara dengan jumlah penduduk terbanyak dan negara dengan jumlah penduduk terbanyak yang telah melakukan vaksinasi di dunia. Namun untuk hasil persentase progres vaksinasi yang dilakukan oleh dunia, China tidak masuk kedalam 10(sepuluh) besar negara dengan persentase vaksinasi terbanyak. 10(Sepuluh) negara dengan persentase jumlah penduduk terbanyak yaitu Gibraltar, Oman, Falkland Island, Isle of Man, Seychelles, Nauru, San Marino, Malta, Bhutan, dan Cayman Islands.
Pengelompokan Harga Ponsel Pintar berdasarkan Spesifikasi menggunakan Metode K-Means Clustering Ahmad Agung Zefi Syahputra; Annisa Dwi Atika; Muhammad Adam Aslamsyah; Meida Cahyo Untoro; Winda Yulita
Jurnal Teknik Informatika C.I.T Medicom Vol 13 No 2 (2021): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol13.2021.98.pp59-68

Abstract

The use of smartphones in the industrial era 4.0 had become more frequent and widespread in various circles of Indonesian society. In addition, the COVID-19 pandemic that had not end yet also made high school and college students obliged to carry out online learning. This research aimed to cluster the price from smartphones using the specifications of the smartphone. K-Means Clustering was used as a method in this research. This algorithm was a data mining algorithm with unsupervised learning as data grouping and could group the price of a smartphone into several clusters based on the similarity of the characteristics by one data with other data, which is memory_size and best_price. The results of this research indicated that the right clustering of smartphone prices was within 3 different clusters, which was cluster 0 has centroid of Rp2.000.000,00, cluster 1 has centroid of Rp18.000.000,00, and cluster 2 has centroid of Rp9.000.000,00. The results of the evaluation used a confusion matrix, summary of prediction result, indicated that the clustering process had 100% of accuracy that could be seen on the table which showed the results of clustering. The conclusion from this research was that K-Means Clustering could form clusters in determining the price of a smartphone in relation to the specifications used as the attribute determining the price cluster for a smartphone.
Co-Authors Ahmad Agung Zefi Syahputra Anastasia Puteri Dewi Andika Setiawan Annisa Dwi Atika Muhammad Alfarizi Tazkia Muhammad Zulfarhan Untoro, Meida Cahyo Winda Yulita