Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail
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https://doi.org/10.24843/MITE.2017.v16i02p10
Abstrak
Abstract— This study investigates the performance of artificial neural network method on clustering method. Using UD. Fenny’s customer profile in year 2009 data set with the Recency, Frequency and Monetary model data. Clustering methods were compared in this study is between the Self Organizing Map and Adaptive Resonance Theory 2. The performance evaluation method validation is measured by the index cluster validation. Validation index clusters are used, among others, Davies-Bouldin index, index and index Dunn Silhouette. The test results show the method Self Organizing Map is better to process the data clustering.
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Referensi
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[7] Ariana, A.A. Gede Bagus., Dita Andriawan, I Wayan (2014) Segmentasi Pelanggan PDAM dengan Metode Adaptive Resonance Theory 2. Prosiding Seminar Nasional Ilmu Komputer 2014. ISBN: 978-602-19406-2-4. Universitas Gadjah Mada Yogyakarta.
[8] Kohonen, T. (1990) The Self-Organizing Map, Invited Paper. Procedings of the IEEE, Vol. 78, No. 9, September 1990.
[9] Carpenter, G.A., & S. Grossberg (1987) ART 2: Self-organization of Stable Category Recognition Codes for Analog Input Patterns. Applied Optics. 26:4919-4930. A reprint from Applied Optics volume 26, number 23, December 1987.
[10] Yao, X. (1999) Evolving Artificial Neural Networks, Proceedings of the IEEE, 7(9):1423-1447, September 1999.
[11] Sivanandam, S. N., Sumathi, S. (2006) Introduction to Data Mining and its Applications. Spinger, Verlag Berlin Heidelberg.
Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2006) Introduction to neural networks using MATLAB 6.0. New Delhi: The McGraw-Hill Companies.
[2] Cheng, C. H. & Chen Y. S. (2009) Classifying the segmentation of customer value via RFM model and RS Theory. Expert System with Application, 36, 4176-4184.
[3] Hughes, A. M. (1994) Strategic Database Marketing. Chicago: Probus Publishing Company.
[4] Nugraheni, Yohana (2013) Data Mining Using Fuzzy Method for Customer Relationship Management in Retail Industry. Lontar Komputer Vol. 4 No. 1, April 2013. ISSN: 2088-1541. Universitas Udayana.
[5] Smita, N., (2010) Potential use of Artificial Neural Network in Data Mining. Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference. Vol.2, 339-343.
[6] Ariana, A.A. Gede Bagus (2011) Customer Segmentation dengan Metode Self Organizing Map (Studi Kasus: UD. Fenny) Lontar Komputer Vol. 2 No. 1 Juni 2011. ISSN: 2088-1541. Universitas Udayana
[7] Ariana, A.A. Gede Bagus., Dita Andriawan, I Wayan (2014) Segmentasi Pelanggan PDAM dengan Metode Adaptive Resonance Theory 2. Prosiding Seminar Nasional Ilmu Komputer 2014. ISBN: 978-602-19406-2-4. Universitas Gadjah Mada Yogyakarta.
[8] Kohonen, T. (1990) The Self-Organizing Map, Invited Paper. Procedings of the IEEE, Vol. 78, No. 9, September 1990.
[9] Carpenter, G.A., & S. Grossberg (1987) ART 2: Self-organization of Stable Category Recognition Codes for Analog Input Patterns. Applied Optics. 26:4919-4930. A reprint from Applied Optics volume 26, number 23, December 1987.
[10] Yao, X. (1999) Evolving Artificial Neural Networks, Proceedings of the IEEE, 7(9):1423-1447, September 1999.
[11] Sivanandam, S. N., Sumathi, S. (2006) Introduction to Data Mining and its Applications. Spinger, Verlag Berlin Heidelberg.
Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2006) Introduction to neural networks using MATLAB 6.0. New Delhi: The McGraw-Hill Companies.
Diterbitkan
2017-08-31
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ARIANA, Anak Agung Gede Bagus; DARMA PUTRA, I Ketut Gede; LINAWATI, Linawati.
Perbandingan Metode SOM/Kohonen dengan ART 2 pada Data Mining Perusahaan Retail.
Jurnal Teknologi Elektro, [S.l.], v. 16, n. 2, p. 55-59, aug. 2017.
ISSN 2503-2372.
Tersedia pada: <https://ojs.unud.ac.id/index.php/mite/article/view/ID24040>. Tanggal Akses: 15 oct. 2025
doi: https://doi.org/10.24843/MITE.2017.v16i02p10.
Bagian
Articles
Kata Kunci
Data Mining; Jaringan Saraf Tiruan; Self Organizing Map; Adaptive Resonance Theory 2
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This work is licensed under a Creative Commons Attribution 4.0 International License
