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All Journal Jurnal Rekayasa Sistem Industri Gema Pendidikan International Journal of Artificial Intelligence Research SMARTICS Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Informatics and Communication Technology (JICT) Golden Ratio of Data in Summary Journal of Informatics and Communication Technology (JICT)
Nurapriani, Fitria
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Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

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Klasterisasi Data Obat dengan Algoritma K-Means (Kasus pada UPTD Puskesmas Curug) kastiawan, Nurhayadi; Huda, Baenil; Novalia, Elfina; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.771

Abstract

Managing drug supplies is very important because it minimizes drug losses in institutions such as health centers, pharmacies and hospitals, so that drugs of any type are in accordance with the quantity needed. The research aims at grouped drug data, where this case study was carried out at the Curug Health Center UPTD which will be used as a guide in submitting a drug import plan at this health center. The data processed in this research is the 2022 annual report, drug needs plan and proposed drug needs (RKO 2023) at the Curug Health Center UPTD. The data in this research was processed by the K-Means algorithm with the rapidminer tool, where this technique data is grouped by collecting data into clusters. The results obtained were that Cluster 0 was a very low cluster which contained 14 drug items, then Cluster 1 with 12 drug items was a low cluster, Cluster 3 with 2 drug items was a high cluster and Cluster 2 was the highest cluster with 2 items drug.
Implementasi Algoritma K-Means Untuk Klasterisasi Data Obat Puskesmas Kotabaru Kurniawan, Muhamad Dicky; Priyatna, Bayu; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.693

Abstract

Drug management is one of the things needed to manage drug supplies. Proper planning of drug needs makes drug procurement efficient and effective so that drugs are available in sufficient types and quantities as needed and easily obtained when needed. The purpose of this study was to classify drug data at the Kotabaru Health Center which can be used as a reference in making decisions in planning and controlling drug needs at the Health Center. The data used in this study are the Kotabaru Health Center annual report data from 2019 to 2021. Data processing in this study uses the K-means clustering method with rapidminer tools which is a data grouping technique by dividing the existing data into one or two forms. more clusters. The results of this study divide the drug data into 4 clusters, namely the first cluster (C0) with very low usage consisting of 27 drugs, the second cluster (C2) with low usage consisting of 6 drugs, the third cluster (C3) with high usage consisting of 1 drug, and the fourth (C2) with the highest usage consisting of 1 drug.
Co-Authors ,, Tukino -, Tukino AA Sudharmawan, AA Ahnaf, Naufal Zubdi April Lia Hananto Arkan Hilman Hakim Hananto, Agustia Hilabi, Shofa Shofiah Huda, Baenil kastiawan, Nurhayadi Kurniawan, Muhamad Dicky Kusumaningrum, Dwi Sulistya Lestari, Renita Lestari, Santi Arum Puspita Novalia, Elfina Nurfauzi, Oka Muhamad Priatna, Bayu Priyatna, Bayu Rahmawati, Adila Ramadan, Mohamad Rizqulloh Sriardi, Mohammad Didit Tukino, Tukino tukino, tukino Yulistina Nur DS