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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOMNIKA: Journal of Computer Science and Applied Informatics
Anastasia Puteri Dewi
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.
Application of Data Mining with the K-Means Clustering Method and Davies Bouldin Index for Grouping IMDB Movies Ilham Firman Ashari; Romantika Banjarnahor; Dede Rodhatul Farida; Sicilia Putri Aisyah; Anastasia Puteri Dewi; Nuril Humaya
Journal of Applied Informatics and Computing Vol 6 No 1 (2022): July 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i1.3485

Abstract

Along with the development of technology, the film industry continues to increase, this can be seen from the number of films that appear both in cinemas and tv shows. The Internet Movie Database (IMDb) is a website that provides information about films from around the world, including the people involved in the films. Information contained on IMDB such as actor/actress, director, writer, to the soundtrack used. In addition, IMDb is the most popular and trusted source of information for movies, TV, and other celebrity content. In this case, the researcher will conduct research on the film with what title is the most popular among the public by looking at some of the parameters contained in IMDB such as the number on the rating, score, certificate, and votes obtained from the audience. The data used comes from the Kaggle.com website. The data mining method used is the K-Means clustering method. To find out the optimal cluster value, the Davies Bouldin index is used. The K-Means algorithm will group the data based on the centroid. The parameters used for clustering are runtime, IMDB rating, meta score, number of votes, and gross. The results of the study obtained that the average calculation of the highest attributes was 48.74 and the number of clusters formed was 4 clusters. The results of the evaluation using the confusion matrix obtained an accuracy value of 100%.
Co-Authors Ahmad Agung Zefi Syahputra Andika Setiawan Dede Rodhatul Farida Ilham Firman Ashari Muhammad Adam Aslamsyah Muhammad Alfarizi Tazkia Muhammad Zulfarhan Nuril Humaya Romantika Banjarnahor Sicilia Putri Aisyah Untoro, Meida Cahyo