PENGGUNAAN SELF ORGANIZING MAP DALAM PENGELOMPOKAN TINGKAT KESEJAHTERAAN MASYARAKAT

IRWAN IRWAN, ASTRI YUNI HASHARI, HISYAM IHSAN, AHMAD ZAKI

Abstract


Self Organizing Map (SOM) is one of the topology forms of Unsupervised Neural Network where in the learning process does not require output target. Clusters in this research consist of one or more regency/city areas that have certain characteristics based on the variables. Each cluster had to be validated by using the Davies Bouldin Index value to get the best cluster formation from the SOM algorithm learning process. The best cluster model is the cluster model that has the smallest Davies Bouldin Index value. This research used 30 variables that refer to the key statistics of South Sulawesi Province People's Prosperity in 2018 by BPS of South Sulawesi Province. In this research, four cluster formation models were formed which began by forming 2 cluster model to form 5 cluster. Based on the Davies Bouldin Index value, it was found that the  5 cluster model have minimum value of 0.17.

Keywords


Self Organizing Map; Unsupervised Artificial Neural Network; Davies Bouldin Index; people’s Prosperity

Full Text:

PDF

References


Anis, Yunus, dan Isnanto R.R. 2014. Penerapan Metode Self-Organizing Map (SOM) untuk Visualisasi Data Geospasial Pada Informasi Sebaran Data Pemilih Tetap (DPT). Jurnal Sistem Informasi Bisnis. Vol.01.

Annas, S., Kanai, T., & Koyama, S. 2007. Principal Component Analysis and Self-Organizing Map dor Visualizing and Classifying Fire Risks in Forest Regions. Jurnal Agricultural Information Reseach. Vol.16, No.2. Halaman: 44-51.

Badan Pusat Statistik Provinsi Sulawesi Selatan, 2018. Statistik Kesejahteraan Rakyat Provinsi Sulawesi Selatan Tahun 2018. Badan Pusat Statistik Provinsi Sulawesi Selatan.

Cahyani, A. D., Putro, S. S., Rahmawati, L. 2013. Pemandaatan Metode Cluster SOM-IDB sebagai Analisa Pengelompokan Penerima Beasiswa. https://docplayer.info/29552003-Pemanfaatan-metode-cluster-som-idb-sebagai-analisa-pengelompokan-penerima-beasiswa.html. diakses pada tanggal 16 Maret 2019.

Davies, D., & Bouldin, D. 1979. A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2):224 - 227

Hariadi. 2007. Klasifikasi Kekerapan Kunjungan Lokasi Berbasis Location Based Service (LBS) menggunakan Self-Organizing Map (SOM). Tesis. Institut Teknologi Sepuluh Nopember.

Irwan. 2010. Penggunaan Analisis Diskriminan dan Self-Organizing Maps (SOM) dalam Pengelompokan Objek (Studi Penglompokan Desa Miskin di Kabupaten Wajo). Skripsi UNM. Makassar

Shieh, S., & Liao, I. 2012. A New Approach for Data Clsutering and Visualization Using Self-Organizing Map. International Journal of Expert System with Application. Vol 39, issue 15.

Siang, J. 2009. Jaringan Syaraf Tiruan dan Pemogramannya Menggunakan Matlab (Ed.II). Yogyakarta: Andi Offset.

Supranto, J. 2004. Analisis Multivariat Arti dan Interpretasi Edisi Pertama. Jakarta: PR Rineka Cipta.




DOI: https://doi.org/10.34312/jjps.v1i2.7266

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Jambura Journal of Probability and Statistics

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Editorial Office of Jambura Journal of Probability and Statistics:
 
Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo
Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
Telp: +6285398740008 (Call/SMS/WA)
E-mail: redaksi.jjps@ung.ac.id