Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung
Abstract
Dalam persaingan ketat saat ini, promosi yang baik dapat memberikan kredibilitas untuk produk baru. Promosi perlu mendapat perhatian lebih dan serius, karena dalam kehidupan sehari-hari timbul produk unggulan, jika tidak mengetahuinya, kemungkinan produk yang ditawarkan kepada konsumen kurang ditanggapi oleh pasar, oleh karena itu perusahaan harus mengupayakan produknya, meyakinkan dan mempengaruhi konsumen untuk menciptakan permintaan akan produk ini. Langkah yang bisa dilakukan oleh perusahaan untuk melakukannya adalah dengan melakukan pemasaran langsung. Peningkatan akurasi prediksi pemasaran langsung dapat dilakukan dengan cara melakukan seleksi terhadap atribut, karena seleksi atribut mengurangi dimensi dari data sehingga operasi algoritma data mining dapat berjalan lebih efektif dan lebih cepat. Dalam penelitian ini akan digunakan metode support vector machine dan akan dilakukan seleksi atribut dengan menggunakan particle swarm optimization untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47% dan nilai AUC sebesar 0,896. Kemudian dilakukan seleksi atribut dengan menggunakan particle swarm optimization dimana atribut yang semula berjumlah 16 variabel prediktor terpilih 12 atribut yang digunakan. Hasil menunjukkan nilai akurasi yang lebih tinggi yaitu sebesar 89,38%, nilai precision 89,89% dan nilai AUC sebesar 0,909 dengan nilai akurasi klasifikasi sangat baik (excellent clasiffication). Sehingga dicapai peningkatan akurasi sebesar 0,67 %, dan peningkatan AUC sebesar 0,013.
Kata Kunci: Particle Swarm Optimization, Pemasaran Langsung, Seleksi Atribut
Abstract
In the current intense competition a good promotion can provide credibility for a new product. Promotion needs to get more attention and serious, because in everyday life arise a prime product, if not find out, the possibility of products offered to consumers less responded by the market, therefore the company should strive for its products. , convincing, and influencing consumers to create demand for these products. Steps that can be done by the company to do so is to do direct marketing. Increased accuracy of direct marketing predictions can be done by selecting attributes, because of the selection. Data mining can run more effectively and quickly. In this study the method to be used is. With particle swarm optimization for direct marketing prediction optimization. After testing, the results obtained are support vector engine yield value of 88.71%, precision value 89.47% and AUC value of 0.896. Then the attribute selection is done using particle swarm optimization where the original attribute uses 16 predictor variables selected 12 attributes used. The results showed a higher value of 89.38%, 89.89% accuracy and AUC value of 0.909 with very good fair value (excellent classification). The price increase is 0.67%, and the increase of AUC is 0,013.
Keywords: Particle Swarm Optimization, Direct Marketing, Selection Attributes.
Keywords
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Aydin, I., Karakose, M., & Akin, E. (2011). A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Journal Applied Soft Computing(11), 120-129.
Bellotti, T., & Crook, J. (2007). Support vector machines for credit scoring and discovery of significant features. Expert System with Application: An International Journal(36), 3302-3308.
Dawson, C. W. (2009). Project in Computing and Information System A Student’s Guide. England: Addison-Wesley.
Elsalamony, H. (2014). Bank Direct Marketing Analysis of Data Mining Techniques. International Journal of Computer Applications, 0975-8887 Volume 85 - No. 7.
Elsalamony, H. A., & Elsayad, A. M. (2013, Agustus). Bank Direct Marketing Based on Neural Network and C5.0 Models. IJEAT, II(6), 392-400.
Han, J., & Kamber, M. (2007). Data Mining Concepts and technique. San Francisco, USA: Diane Cerra.
Kotler, P., Armstrong, G., Ang, S. H., Leong, S. M., Tan, C. T., & Tse, D. K. (2005). Principles Of Marketing An Asian Perspective. Kuala Lumpur: Prentice Hall; 11TH EDITION edition.
Maimon, O., & & Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook (2nd ed). New York: Springer Dordrecht Heidelberg London.
Moro, S., & Laureano, R. M. (2012). Using Data Mining for Bank Direct Marketing: An application of the CRISP-DM methodology. European Simulation and Modelling Conference, Figure I, 117-121.
UCI. (2012, Februari 14). Retrieved from UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/bank+marketing
Vercellis, C. (2009). Business Intelegent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: john Willey & Sons, Ltd.
Yin, H., X., J., Chai, Y., & Fang, B. (2015). Scene classification based on single-layer SAE and SVM. Expert Systems with Applications, 7(42), 3368-3380.
Yusup, N., Zain, A. M., Zaiton, S., & & Hashim, M. (2012). Prosedia Engeneering Overview of PSO for Optimizing Process Parameters of Machining. Elsevier.
Prasetio, R. T., & Riana, D. (2015, November). A comparison of classification methods in vertebral column disorder with the application of genetic algorithm and bagging. In 2015 4th international conference on instrumentation, communications, information technology, and biomedical engineering (ICICI-BME) (pp. 163-168). IEEE.
DOI: https://doi.org/10.31294/ji.v5i1.2736
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