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All Journal Jurnal Ilmiah Kursor Jurnal Sains Matematika dan Statistika Indonesian Journal of Artificial Intelligence and Data Mining JTAM (Jurnal Teori dan Aplikasi Matematika) JTIK (Jurnal Teknik Informatika Kaputama) JISA (Jurnal Informatika dan Sains) Abdimasku : Jurnal Pengabdian Masyarakat Unnes Journal of Mathematics IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Pengabdian Masyarakat dan aplikasi Teknologi Jurnal Pengabdian Sosial
Putri, Aina Latifa Riyana
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Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : JTIK (Jurnal Teknik Informatika Kaputama)

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CLUSTERING NEGARA BERDASARKAN SKOR PENGENDALIAN KONSUMSI TEMBAKAU MENGGUNAKAN ALGORITMA DBSCAN Joko Riyono; Pujiastuti, Christina Eni; Putri, Aina Latifa Riyana
JTIK (Jurnal Teknik Informatika Kaputama) Vol. 8 No. 1 (2024): Volume 8, Nomor 1, Januari 2024
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jtik.v8i1.342

Abstract

Smoking is an activity that has a detrimental impact on the health of individuals, families, communities and the environment, both directly and indirectly. Therefore, it is necessary to control global tobacco consumption. The World Health Organization (WHO) in the Framework Convention on Tobacco Control (FTCT) developed the MPOWER strategy: a which countries can use to control tobacco consumption. In this study a clustering analysis of tobacco use control will be carried out based on the MPOWER score: a from WHO for each country using the Density Based Spatial Clustering of Applications with Noise (DBScan) algorithm. DBSCAN is a cluster formation algorithm based on the level of distance density between objects in a dataset. Using a density radius of 1.72 with a minimum point of 4 objects obtained from the kNNdisplot function on Rstudio produces 4 clusters, 75 data as noise, and the Davies Bouldin-Index value as the best cluster validity is 1.08118.
Co-Authors Amalia, Raisha Ayu, I Gusti De Bashiran, Himam Bayu Surarso Brian Nugraha Wiyono Christina Eni Pujiastuti Dewanti, Anggun Dody Prayitno Gabriella F Pandiangan Harnoko, Sri Namira Putri Joko Riyono Joko Riyono Kania, Putri Emas Mega Yuliani Ramme Nurkaromah Dwidayati, Nurkaromah Prameswari, Mayesq Pujiastuti, Christina Eni Puspa, Sofia Debi Putri, Dianing Novita Nurmala Setyaningsih, Destina Bekti Silaen, Ferdinan Supriyadi Supriyadi Supriyadi Syahid Nur Khomsyi Syahida, Shafa Tamara, Donna Nur Titi Udjiani SRRM Zahra, Indy Aurellia Az