Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana

  • Ika Oktavianti Master Student, Sriwijaya University
  • Ermatita Ermatita Universitas Sriwijaya
  • Dian Palupi Rini Universitas Sriwijaya
Keywords: pattern prediction, time series data, multilayer perceptron, support vector regression, simple linear regression


Licensing services is one of the forms of public services that important in supporting increased investment in Indonesia and is currently carried out by the Investment and Licensing Services Department. The problems that occur in general are the length of time to process licenses and one of the contributing factors is the limited number of licensing officers. Licensing data is a time series data which have monthly observation. The Artificial Neural Network (ANN) and Support Vector Machine (SVR) is used as machine learning techniques to predict licensing pattern based on time series data. Of the data used dataset 1 and dataset 2, the sharing of training data and testing data is equal to 70% and 30% with consideration that training data must be more than testing data. The result of the study showed for Dataset 1, the ANN-Multilayer Perceptron have a better performance than Support Vector Regression (SVR) with MSE, MAE and RMSE values is 251.09, 11.45, and 15.84. Then for dataset 2, SVR-Linear has better performance than MLP with values of MSE, MAE and RMSE of 1839.93, 32.80, and 42.89. The dataset used to predict the number of permissions is dataset 2. The study also used the Simple Linear Regression (SLR) method to see the causal relationship between the number of licenses issued and licensing service officers. The result is that the relationship between the number of licenses issued and the number of service officers is less significant because there are other factors that affect the number of licenses.



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1. Menggunakan pendekatan hybrid, yang merupakan kombinasi Artificial Neural Network (ANN) dan Support Vector Regression (SVR).
2. Pemilihan parameter yang paling tepat dari fungsi kernel dan model Artificial Neural Network (ANN) serta diusulkan untuk menggunakan algoritma metaheuristic, Deep Learning, dan teknik algoritma dalam akurasi peramalan.
3. Mencari faktor lain yang dimungkinkan mempengaruhi variabel jumlah izin yang terbit selain dari petugas pelayanan (SDM).
Daftar Rujukan
[1] P. Meesad and R. I. Rasel., 2013. Predicting Stock Market Price Using Support Regression. International Conference on Informatics, Electronics and Vision (ICIEV).
[2] Ullu, H. H., 2013. Prognosis of Damage to Rolling Bearings Using the Support Vector Regression (SVR) Method. Universitas Diponegoro.
[3] Hasan, N., Rasel, R. I., and Nath, N. C., 2015. A Support Vector Regression Model for Forecasting Rainfall. International Conference on Electrical Information and Communication Technologies (EICT).
[4] A. Xu and M. Raginsky., 2017. Information-Theoretic Analysis of Generalization Capability of Learning Algorithm. 31st Conference on Neural Information Processing System (NIPS). CA, USA.
[5] P. Kumar and P. Sharma., 2014. Artificial Neural Network-A Study. International Journal of Emerging Engineering Research and Technology, 2 (2), pp. 143-148.
[6] Nazzal, J. M., El-Emary, I. M., and Najim, S. A., 2008. Multilayer Perceptron Neural Network (MPNN) for Analyzing The Properties of Jordan Oil Shale. World Applied Sciences Journal, 5, 546-552.
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