dc.contributor.author |
Purba, Nurani Millenia Grace |
|
dc.date.accessioned |
2022-08-23T02:28:13Z |
|
dc.date.available |
2022-08-23T02:28:13Z |
|
dc.date.issued |
2022-04 |
|
dc.identifier.citation |
Perpustakaan |
en_US |
dc.identifier.other |
Elfitra |
|
dc.identifier.uri |
https://repository.unri.ac.id/handle/123456789/10656 |
|
dc.description.abstract |
The increasing population of Indonesia may cause unemployment problems.
Unemployment occur due to an imbalance in the number of workers with the
number of jobs. Based on data from Statistics Agency, one of the cities facing the
problem of unemployment in Indonesia is Batam, which is located in the Riau
Islands Province. Batam has the highest unemployment rate in the Riau Islands,
which is 11,79%. This study aims to classify Batam unemployment using the
Fuzzy K-Neareast Neighbor method. This method is data mining analysis that
determine class label based on the class that has maximum membership value.
The data used is the result of the Batam labor force survey (Sakernas) in August
2020. From the total data of Sakernas, composition of training data and test data is
70% and 30%. Based on the results of the Fuzzy K-NN classification, obtained
the highest accuracy by 82.93% on the test of K, that is when K = 500, with
sensitivity by 11,11% and specificity by 92,23% and m used is 2. |
en_US |
dc.description.provenance |
Submitted by wahyu sari yeni (ayoe32@ymail.com) on 2022-08-23T02:28:13Z
No. of bitstreams: 1
NURANI MILLENIA GRACE PURBA_compressed.pdf: 185098 bytes, checksum: 8acf8c83dcde3f7049e3c4ad79613c56 (MD5) |
en |
dc.description.provenance |
Made available in DSpace on 2022-08-23T02:28:13Z (GMT). No. of bitstreams: 1
NURANI MILLENIA GRACE PURBA_compressed.pdf: 185098 bytes, checksum: 8acf8c83dcde3f7049e3c4ad79613c56 (MD5)
Previous issue date: 2022-04 |
en |
dc.description.sponsorship |
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riau |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elfitra |
en_US |
dc.subject |
Classification |
en_US |
dc.subject |
data mining |
en_US |
dc.subject |
fuzzy k-nearest neighbor |
en_US |
dc.subject |
sakernas |
en_US |
dc.subject |
unemployment |
en_US |
dc.title |
PENERAPAN FUZZY K-NEAREST NEIGHBOR UNTUK KLASIFIKASI PENGANGGURAN TERHADAP PENDUDUK KOTA BATAM |
en_US |
dc.type |
Article |
en_US |
dc.contributor.supervisor |
Adnan, Arisman |
|