Susanto, Budi
Program Studi Teknik Industri, Universitas Muhammadiyah Cirebon

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Rekomendasi Daerah Penyalur Tenaga Kesehatan Covid-19 Dengan Menggunakan Skyline Query Vega Purwayoga; Budi Susanto
Fountain of Informatics Journal Vol 7, No 1 (2022): Mei
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v7i1.5720

Abstract

AbstrakTingginya tenaga kesehatan yang terinfeksi dan meninggal memberikan dampak terhadap ketersedian jumlah tenaga kesehatan pada suatu daerah khusunya rumah sakit. Salah satu solusi untuk mengantisipasi ketersediaan tenaga kesehatan yaitu dengan meminta bantuan tenaga kesehatan pada daerah yang minim resiko. Pencarian daerah minim resiko pada penelitian ini dilakukan dengan menggunakan skyline query. Algoritme skyline query yang digunakan dalam penelitian ini adalah Sort Filter Skyline (SFS). SFS melakukan pengurutan terlebih dahulu sebelum melakukan pengujian dominasi. Pengurutan dilakukan berdasarkan nilai entropy. Daerah yang memiliki entropy terbesar adalah Kota Depok dengan nilai entropy 1.93836. Dikarenakan Kota Depok memiliki entropy terbesar maka Kota Depok adalah objek skyline pertama. Setelah objek skyline pertama didapat maka, objek selanjutnya akan dilakukan pengujian dominasi. Bulan dengan daerah pembantu tenaga kesehatan terbanyak adalah Bulan September sebanyak 17 daerah. Pada bulan Oktober dan November daerah yang direkomendasikan sebagai daerah pembantu tenaga kesehatan sebanyak 12. Jumlah objek skyline bergantung pada atribut yang mendekati preferensi. Semakin banyak daerah dengan kasus positif rendah, kasus meninggal rendah dan kasus meninggal tinggi maka semakin banyak daerah priroitas yang menjadi pembantu tenaga kesehatan. Penelitian selanjutnya diharapkan dapat mengidentifikasi daerah yang ada di Indonesia untuk proses pemerataan tenaga kesehatan pada seluruh daerah di Indonesia.Kata kunci: COVID-19, Distribusi Tenaga Kesehatan, Skyline Query, Sort Filter SkylineAbstract[The Recomendation of COVID-19 Health Worker Distribution Areas using Skyline Query] The high number of infected and dead health workers has an impact on the availability of the number of health workers in an area, especially hospitals. One solution to anticipate the availability of health workers is to ask for help from health workers in areas with minimal risk. The search for minimal risk areas in this study was carried out using a skyline query. The skyline query algorithm used in this research is Sort Filter Skyline (SFS). SFS performs sorting first before doing dominance testing. Sorting is done by entropy value. The area that has the largest entropy is Depok City with an entropy value of 1,93836. Because Depok City has the largest entropy, Depok City is the first skyline object. After the first skyline object is obtained, the next object will be tested for dominance. The month with the largest number of regions supporting health workers was September with 17 regions. In October and November the recommended area as a health worker assistant area is 12. The number of skyline objects depends on the attributes that are close to preference. The more areas with low positive cases, low death cases and high death cases, the more priority areas become assistants to health workers. Further research is expected to identify regions in Indonesia for the process of distributing health workers in all regions in Indonesia.Keywords: COVID-19, Distribution Health Worker, Skyline Query, Sort Filter Skyline
PENGELOMPOKKAN DAERAH BERDASARKAN KETERSEDIAAN MASJID MUHAMMADIYAH DENGAN ALGORITME K-MEANS Vega Purwayoga; Budi Susanto
Jurnal Teknologi Vol 13, No 1 (2021): Jurnal Teknologi
Publisher : Fakultas Teknik Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/jurtek.13.1.75-80

Abstract

The importance of the mosque's role in daily life as it is known is for worship facilities. However, the availability of mosques in an area is not yet fully distributed, even there are still areas that do not yet have a mosque. The financial surplus in the Muhammadiyah organization has not been put to good use. One solution is to map and group an area based on the number of mosques in the area. The grouping of regions in this study uses the K-Means algorithm. Characteristics for the process of grouping regions are the name of the district, the name of the sub-district and the number of mosques in a sub-district or branch. The study area in this research is West Java Province. Determination of K or number of clusters in this study is 3. The number of clusters is determined based on 3 categories, namely small, medium and large. There are 4 regions that are categorized with a large number of mosques, 21 regions with moderate numbers and 78 for regions that are classified as few. Evaluation of the results of the grouping showed good results with an SSE value of 90.05%.