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All Journal AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer The IJICS (International Journal of Informatics and Computer Science) JISA (Jurnal Informatika dan Sains) KLIK: Kajian Ilmiah Informatika dan Komputer Brilliance: Research of Artificial Intelligence Jurnal Informatika Teknologi dan Sains (Jinteks) Hexagon
M. Julkarnain
Program Studi Informatika Universitas Teknologi Sumbawa
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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Penerapan Algoritma K-Means Clustering Untuk Data Obat Tis Asy Aria; Yuliadi Yuliadi; M Julkarnain; Fahri Hamdani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1117

Abstract

In the process of planning and controlling the supply of UPT drug stock. Unter Iwes Sumbawa Community Health Center is not yet optimal because the problem of inaccuracy in analyzing drug needs affects the amount needed and demand for medicine. In this study, drug data analysis was conducted by applying the K-Means Clustering algorithm. Drug data is grouped based on usage levels C1, C2, and C3 (low, medium, high) with attributes namely Drug Name, Unit, Supply, Demand and Usage. The results of this study can provide a strategy as a reference for future drug planning and needs. From the results of drug data testing from January to December 2022 with a total of 4,642 data and training data totaling 4,622. The results of the performance of grouping drugs using the Davies Bouldin method of 0.513 clusters of C1/C0 (low) data were 4433 items with 4000 uses. C2/C3 cluster (medium) is 184 items with 10,850 drug use and C3/C2 cluster (high) is 5 items with
Application of the Support Vector Machine (SVM) Algorithm for the Diagnosis of Diabetic Retinopathy Yuliadi; Fadhli Dzil Ikram; M. Julkarnain; Fahri Hamdan; Halid Nuryadi
Brilliance: Research of Artificial Intelligence Vol. 3 No. 2 (2023): Brilliance: Research of Artificial Intelligence, Article Research November 2023
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v3i2.3436

Abstract

Diabetic Retinopathy (DR) is a disease whose main cause is complications of diabetes mellitus. High levels of sugar in the blood (glucose) are caused by the pancreas' inability to produce insulin. Prevention of diabetic retinopathy and blindness by carrying out examinations at an early stage and doing them regularly. Currently, doctors still carry out examinations manually so they are prone to errors in examinations. This research aims to build an application to diagnose Diabetic Retinopathy in order to facilitate the work of the medical team and doctors at the eye clinic. In the application creation process, MATLAB is used, while feature extraction uses GLCM and for classification, SVM is used. The results of the research are that doctors and medical teams are helped in carrying out manual patient diagnoses and reduce the occurrence of human error.
Co-Authors Aldela Jabi Afahar Apriana Wulandari Brenny Prasetyo Waty Cahyono, Tomy Dwi Dedy Sofian MZ Eri Sasmita Susanto Fadhli Dzil Ikram Fahri Hamdan Fahri Hamdani Fahrul Irwansyah Halid Nuryadi Herfandi Herfandi I Made Widiarta Jati Imanulloh Karisma, Yana Kiki Rizki Ananda Miftahul Haq Mohammad Taufan Asri Zaen Muhammad Dian Fajri Muhammad Hanif Muhammad Rifqi Fawzan Mulyanto, Yudi Nawassyarif Rina Susilowati Rodianto, Rodianto Sahmad Saiful Akhyar Lubis Shinta Esabella Sri Lis Apriliani Tis Asy Aria Yudi Pratama Yuliadi Yuliadi Yuniar Andriani