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All Journal International Journal of Advances in Intelligent Informatics Inferensi
Suhartono Suhartono
Department of Statistics, Institut Teknologi Sepuluh Nopember
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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Estimating the function of oscillatory components in SSA-based forecasting model Winita Sulandari; Subanar Subanar; Suhartono Suhartono; Herni Utami; Muhammad Hisyam Lee
International Journal of Advances in Intelligent Informatics Vol 5, No 1 (2019): March 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i1.312

Abstract

The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with those obtained by nonlinear least square based on Levenberg Marquardt (NLM) method. A simulation study based on the time series data which has a linear amplitude modulated sinusoid component is conducted to investigate the error of estimated parameters of the model obtained by the proposed method. A real data series was also considered for the application example. The results show that in terms of forecasting accuracy, the SSA-based model where the oscillatory components are obtained by iterative OLS is nearly the same with that is obtained by the NLM method.
The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity Hendri Prabowo; Suhartono Suhartono; Dedy Dwi Prastyo
Inferensi Vol 3, No 1 (2020): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v3i1.6876

Abstract

The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models.
SSA and ARIMA for Forecasting Number of Foreign Visitor Arrivals to Indonesia Agustinus Angelaus Ete; Suhartono Suhartono; Raden Mohammad Atok
Inferensi Vol 3, No 1 (2020): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v3i1.6882

Abstract

Singular Spectrum Analysis (SSA) is the technique of non-parametric analysis of time series used for forecasting. SSA aims to decompose the original time series into a summation of a small number of components that can be interpreted as the trend, oscillatory components, and noise. The purpose of this research is to understand how the SSA model in forecasting the number of foreign tourist arrivals to Indonesia through four entrances. The result of forecasting obtained by using SSA will be compared with ARIMA method to assess its superiority. The data used in this study are the data of the number of foreign tourist arrivals to Indonesia through four entrances in the period January 1996 to August 2016. Four entrances used in this study are Ngurah Rai Airport, Kualanamu Airport, Soekarno-Hatta Airport, and Juanda Airport. The level of forecasting accuracy generated by each forecasting method is measured using the Mean Absolute Percentage Error (MAPE) criterion. The results showed that SSA method is the best forecasting method for forecasting the number of foreign tourist arrivals through Ngurah Rai Airport with an average MAPE value of 9.6%. Forecasting the number of foreign tourist arrivals through Kualanamu Airport, ARIMA method is the best forecasting method with an average MAPE value of 22.4%. In forecasting the number of foreign tourist arrivals through Soekarno-Hatta Airport, ARIMA method is the best forecasting method with an average MAPE value of 10.5%. In forecasting the number of foreign tourist arrivals through Juanda Airport, ARIMA method is the best forecasting method with an average MAPE value of 9.9%.
Peramalan Jumlah Penumpang dan Barang di Bandar Udara Internasional Juanda dan Pelabuhan Tanjung Perak Menggunakan Model Hybrid ARIMAX dan Deep Learning Neural Networks Bella Puspa Dewani; Suhartono Suhartono; Muhammad Sjahid Akbar
Inferensi Vol 2, No 1 (2019): Inferensi
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (888.03 KB) | DOI: 10.12962/j27213862.v2i1.6805

Abstract

Arus penumpang dan barang di Bandar Udara Internasional Juanda dan Pelabuhan Tanjung Perak cenderung fluktuatif dan tidak menentu. Oleh karena itu diperlukan pengetahuan akan keadaan arus penumpang dan barang di masa depan, agar pengembangan yang dilakukan tepat dan berguna. Penelitian ini dilakukan bertujuan untuk memodelkan serta mendapatkan peramalan mengenai jumlah penumpang dan barang di Bandar Udara Internasional Juanda dan Pelabuhan Tanjung Perak dengan membandingkan 5 model. Model tersebut antara lain model ARIMAX, model FFNN, model DLNN dengan 2 hidden layer, model hybrid ARIMAX-FFNN dan model hybrid ARIMAX-DLNN untuk mendapatkan hasil peramalan terbaik. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diperoleh dari Badan Pusat Statistika (BPS). Data yang digunakan adalah data bulanan mulai Januari 2001 hingga Desember 2017 untuk Bandar Udara Internasional Juanda, sedangkan Pelabuhan Tanjung Perak mulai Januari 2006. Hasil penelitian menunjukkan model hybrid ARIMAX-DLNN memiliki kemampuan yang baik untuk menangkap pola data yang beragam dan menghasilkan ramalan yang baik pada data training. Hal tersebut dilihat dari nilai RMSEP yang lebih kecil dibandingkan dengan model lainnya. Namun model DLNN memiliki kemampuan yang baik dalam meramalkan data testing. Model terbaik untuk 8 variabel yang digunakan, terdapat 7 variabel dengan model terbaik yaitu model DLNN, sedangkan sisanya model hybrid ARIMAX-DLNN.
Co-Authors Agustinus Angelaus Ete Bella Puspa Dewani Dedy Dwi Prastyo Hendri Prabowo Herni Utami Muhammad Hisyam Lee Muhammad Sjahid Akbar Raden Mohammad Atok Subanar . Winita Sulandari