CLUSTERISASI KELAYAKAN PENERIMA BANTUAN COVID-19 MENGGUNAKAN ALGORITMA K-MEANS BERBASIS WEB
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Institusion
STMIK Bumi Gora
Author
Kurnianingsih, Dwi
Subject
T Technology (General)
Datestamp
2021-08-30 02:09:22
Abstract :
The government provides assistance during this pandemic which is channeled through the agencies of each sub-district throughout the province of Indonesia, one of which is the West Pagesangan Village. So far, the assistance provided by the West Pagesangan Village is still manual, so it is possible that errors in data collection and distribution of aid will occur. To overcome this problem, the author applies one of the data mining algorithms, namely the K-Means algorithm to determine the eligibility of recipients of Covid-19 assistance, which is done by collecting population data according to predetermined attributes. Then the population data is weighted to simplify calculations using the K-Means algorithm. after that, build a system for the implementation of the K-Means algorithm and conduct tests using the RapidMiner Studio application for comparison. The result of this system is information on the eligibility of recipients of Covid-19 assistance with 3 clusters, namely eligible (C0), considered (C1) and not eligible (C2). The population data used is 964 which is used in 2020. Using 7 attributes, namely, Marital Status, Employment, Income, Child Allowance, Domicile, Other Aid Recipients, and Disabilities.
The government provides assistance during this pandemic which is channeled through the agencies of each sub-district throughout the province of Indonesia, one of which is the West Pagesangan Village. So far, the assistance provided by the West Pagesangan Village is still manual, so it is possible that errors in data collection and distribution of aid will occur. To overcome this problem, the author applies one of the data mining algorithms, namely the K-Means algorithm to determine the eligibility of recipients of Covid-19 assistance, which is done by collecting population data according to predetermined attributes. Then the population data is weighted to simplify calculations using the K-Means algorithm. after that, build a system for the implementation of the K-Means algorithm and conduct tests using the RapidMiner Studio application for comparison. The result of this system is information on the eligibility of recipients of Covid-19 assistance with 3 clusters, namely eligible (C0), considered (C1) and not eligible (C2). The population data used is 964 which is used in 2020. Using 7 attributes, namely, Marital Status, Employment, Income, Child Allowance, Domicile, Other Aid Recipients, and Disabilities.
File :
COVER.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-BAB V.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-SKRIPSI.pdf
1710520189-Dwi Kurnianingsih-S1Ilkom-Jurnal.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-DAFTAR REFERENSI.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-Abstrak.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-BAB I.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-BAB II.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-BAB III.pdf
1710520189-Dwi Kurnianingsih-S1 Ilkom-BAB IV.pdf
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