Pemetaan Pemerataan Pendidikan Menggunakan Self Organizing Maps (SOM) Terintegrasi Sistem Informasi Geografis

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Syarli Syarli
Akhmad Qashlim

Abstract

Pemetaan Pendidikan disuatu Daerah dilakukan untuk mengetahui tingkat kondisi pendidikan pada suatu wilayah. Informasi yang dihasilkan dapat membantu pemerintah dalam menentukan wilayah prioritas pembangunan. Pemetaan kondisi pendidikan dilakukan menggunakan metode Self Organizing Maps (SOM) dengan sembilan indikator pemerataan pendidikan. Pada pengelolaan data preprocessing menggunakan metode Min-Max Normalization untuk menjaga agar range antara variable satu dengan yang lain berada pada range 0-1. Hasil pemetaan SOM dengan menggunakan data latih tahun 2010 – 2013 sebanyak 64 Data dan data uji tahun 2014 sebanyak 16 data menghasilkan akurasi 81,25 %. Keakuratan hasil sangat bergantung pada model data latih, jumlah data latih dan jumlah iterasi. Tingkat akurasi yang dihasilkan setelah dilakukan perbandingan dengan data aktual, menunjukkan bahwa SOM dapat digunakan sebagai metode untuk melakukan pemetaan

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References

[1] Demuth, H., dan Beale, M., 2003, Neural Network Toolbox For Use with MATLAB, USA.
[2] Ehsani, A.H., dan Quiel, F., 2008, Geomorphometric feature analysis using morphometric parameterization and artificial neural networks. Geomorphology (99), 1-12.
[3] Han, J., dan Kamber, M., 2001, Data Mining : Concepts and Techniques, USA.
[4] He, Z., Lin, S., Deng, Y., Li, X., dan Qian, Q., 2014, A rough membership neural network approach for fault classification in transmission lines, Electrical Power and Energy Systems, (61), 429 - 439.
[5] Jain, A.K., Mao, J., and Mohiuddin, K.M., 1996, Artificial Neural Networks: A Tutorial, IEEE Computer.
[6] Jimenez, F., Serradill, F., Roman, A., dan Naranjo, J.E., 2014, Bus line classification using neural networks, Transportations research part D, (30), 32 - 37.
[7] Kalteh, A.M., Hjorth, P., dan Berndtsson, R., 2008, Review of the Self organizing maps approach in water resources : Analysis, modeling and application, Environmental modeling and software (23), 835-845.

[8] Koc, L., Mazzuchi, T.A., dan Sarkani, S., 2012, A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier, Expert system with applications (39), 13492-13500.
[9] Kohonen, T., 2001, Self-organizing maps, Springer Verlag.
[10] Kristanto, A., 2004, Jaringan Syaraf Tiruan, Gava Media, Yogyakarta.
[11] Larose, D.T., 2004, Discovering Knowledge in Data : An Introduction to Data Mining, John Wiley & Sons,Inc.
[12] Lohr, S.C., Grigorescu, M., Hodgkinson, J.H., Cox, M.E., dan Fraser, S.J., 2010, Iron occurrence in soils and sediments of a coastal catchment A multivariate approach using self organising maps, Geoderma (156), 253-266.
[13] Pandit, Y.P., Badhe, Y.P., Sharma, B.K., Tambe, S.S., dan Kulkarni, B.D., 2011, Classification of Indian power coals using K-means clustering and Self Organizing Map neural network, Fuel (90), 339-347.
[14] Pisati, M., Whelan, C.T., Lucchini, M., dan Maitre, B., 2010, Mapping patterns of multiple deprivation using self-organising maps : An application to EU-SILC data for Ireland, Social Science Research (39), 405-418.
[15] Permendagri nomor 54., 2010, Tata cara pengolahan data dan informasi perencanan pembangunan daerah, Kementrian dalam negeri, Indonesia.
[16] Prahasta, E., 2006, Membangun aplikasi web based GIS dengan map server, Informatika, Bandung.
[17] Prasetyo, E., 2012, Data mining, konsep dan aplikasi menggunakan matlab, Andi, Yogyakarta.
[18] Samarasinghe, S., 2006. Neural networks for applied sciences and engineering, Auerbach publication, Francis.
[19] Smith, M., 1996, Neural Networks for Statistical Modeling, International Thompson Computer Press, London.
















[20] Sugiyono., 2009. Metode Penelitian Kuantitatif Kualitatif dan R&D, Alfabeta Bandung.
[21] Turban, B., dan Bener, A., 2009, Analysis of Naive Bayes’ assumptions on software fault data : An empirical study, Data & Knowledge Engineering (68), 278-290.
[22] Undang-undang nomor 20., 2003, Standar sistem pelayanan pendidikan, Indonesia.
[23] [22] Wang, D., dan Gao, M., 2013, Educational equality or social mobility : The value conflict between preservice teachers and the Free Teacher Education Program in China, Teaching and Teacher Education (32), 66-74.
[24] Wang, G.A., Atabakhsh, H., dan Chen, H., 2011. A hierarchical Naïve Bayes model for approximate identity matching, Decision support system (51), 413-423.
[25] Widayati, N., Hariadi, M dan Mardi, S., 2011. Pemetaan prioritas perencanaan pembangunan berbasis penggalian data multidimensi menggunakan websom, Magister tesis, Institut tehnologi sepuluh nopember, Surabaya.
[26] Zhang, C., 2009, An empirical study on the relationship between educational equity and the quality of economic growth in China : 1978-2004, Economic Journal (54), 125-134.
[27] Zhang, H., Liu, J., Ma, D., dan Wang, Z., 2011, Data core based fuzzy min - max neural networks for pattern classification, Neural Networks (22), 2339 -2352.
[28] Zhang, Y., Yin, Y., Guo, D., Yu, X., dan Xiao, L., 2014, Cross validation based weights and structure determination of Chebyshev-polynomial neural networks for pattern classification, Pattern recognition (47), 3414-3428.