Dewi, Anggraeni Rahma (2015) Pengelompokan Kabupaten/Kota Di Jawa Timur Berdasarkan indikator Kemiskinan Menggunakan Metode C-Means Dan Fuzzy C-Means Clustering. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Permasalahan yang sering dihadapi oleh pemerintah/negara Indonesia
adalah kemiskinan. Jika kemiskinan dapat direduksi secara drastis, maka
secara nasional kemiskinan akan berkurang. Jawa Timur lebih berpeluang
dalam pengurangan jumlah angka kemiskinan. Selama ini BPS dan
BAPPEDA mengelompokkan kabupaten/kota di Jawa Timur berdasarkan 4
kultur wilayah. Oleh karena itu peneliti tertarik untuk mengelompokkan kabupaten/
kota di Jawa Timur berdasarkan indikator kemiskinan menggunakan
metode non-hierarchical yang meliputi c-means (CM) dan fuzzy c-means
(FCM) clustering dengan 2 sampai 5 cluster. Hal ini dikarena adanya
pendugaan kondisi kemiskinan di Jawa Timur yang belum homogen dan
mengingat pengelompokan sebelumnya berjumlah 4. Pada penelitian ini diperoleh
kondisi optimum dengan kedua metode berdasarkan nilai pseudo-f
statistics terbesar sebanyak 2 cluster. Metode CM clustering merupakan
metode yang terbaik dalam kasus ini karena memiliki nilai icdrate dan SSW
terkecil serta SSB terbesar. Melalui pengujian one-way MANOVA menghasilkan
bahwa terjadi perbedaan karakteristik antar cluster terhadap respon
yang dalam hal ini adalah seluruh indikator kemiskinan. Selanjutnya,
pada pengujian one-way ANOVA menghasilkan bahwa terjadi perbedaan
karakteristik terkait variabel lama sekolah, akses sanitasi, jenis lantai rumah,
bahan bakar memasak, dan asset rumah. Sedangkan variabel terkait
partisipasi sekolah, akses listrik, dan akses air minum karakteristik pada
masing-masing cluster sama. ========== Most problems have encountered by government / state of Indonesia
is poverty. If poverty can be reduced drastically, then the national poverty
will be reduced. East Java is more likely in a reduction in the amount of
poverty. During BPS and BAPPEDA classify districts / cities in East Java
by four culture regions. Therefore, researchers are interested to classify
districts / cities in East Java based on poverty indicators using nonhierarchical
methods that include c-means (CM) and fuzzy c-means (FCM)
clustering by 2 to 5 clusters. This is caused by the estimation of poverty in
East Java that has not been homogeneous and considering the previous
grouping amounted to 4. In this research, the optimum conditions by both
methods based on the largest pseudo-f statistics as much as 2 clusters. CM
clustering method is the best method in this case because it has the smallest
icdrate and SSW and largest SSB. Through the one-way MANOVA test has
result in that there is difference characteristic between cluster and response
which is all poverty indicators. Furthermore, the one-way ANOVA test
there is difference characteristic of school period variable, sanitation
access, types of floor’s house, cooking fuel, and assets of the house. While
that have same characteristic in each cluster are school participation,
electricity access, and drinking access.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.53 Dew p |
| Uncontrolled Keywords: | CM clustering, FCM clustering, Icdrate, Indikator Kemiskinan, Pseudo-f statistics, Proverty Indicator |
| Subjects: | Q Science > QA Mathematics > QA278.55 Cluster analysis |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 09 Jul 2019 07:56 |
| Last Modified: | 09 Jul 2019 07:56 |
| URI: | http://repository.its.ac.id/id/eprint/63628 |
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