Perbandingan Backproagation Neural Network Dan Learning vector Quantization : Studi Kasus Klasifikasi Daerah tertinggal di Indonesia Tahun 2015

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Kemajuan pembangunan daerah dan peningkatan kesejahteraan rakyat di
Indonesia tidak selalu sama dan merata. Perbedaan kondisi geografis, sumber
daya alam, infrastruktur, sosial budaya dan kapasitas sumber daya manusia
menyebabkan masih adanya kesenjangan an tar wilayah. Tantangan
pengembangan wilayah di Indonesia adalah percepatan pembangunan
kabupaten tertinggal dan mengurangi ketimpangan antar wilayah. Penetapan
daerah tcrtinggal atau tidak tertinggal merupakan salah satu kasus klasifikasi.
Salah satu metode untuk klasifikasi adalah Neural Network. Penelitian ini
membandingkan ketepatan klasifikasi antara Back Propagation Neural
Network (BPNN) dan Learning Vector Quantization (L VQ). Hasil penelitian
ini menunjukkan bahwa LVQ menghasilkan ketepatan yang lebih besar dalam
menentukan daerah tertinggaI dibandingkan metode BPNN.

4. Abstract

The progress of regional development and the improvement of people's
welfare in Indonesia is not always same and even. The differences in
geography, natural resources, infrastructures, social cultures, and the
capacity of human resources causing disparities between regions . The
challenges of regional development is to accelerate the development of
underdeveloped regency, and to reduce regional disparities. Determining a
regency as underdeveloped or developed regency is one of the classification
cases. One of the main methods for classification is Neural Network. The aim
of this study was to compare the accuracy of classification between Back
Propagation Neural Network (BPNN) and Learning Vector Quantization
(LVQ). The result of this study shows that LVQ produce more accuracy in
clasiffying underdeveloped regency compares to BPNN method.

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Detail Information

Series Title

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Call Number

519.5 ELI P/R.14.27

Publisher

Bandung : Magister Statistika Terapan., 2016

Collation

xi,; 68 hlm,;29 cm

Language

Indonesia

ISBN/ISSN

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Classification

5195

Content Type

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Media Type

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Carrier Type

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Edition

2016

Subject(s)

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Specific Detail Info

Tesis

Statement of Responsibility

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