Perbandingan Model Hybrid ARIMAX-FFNN-EGARCH dan Model Hybrid SETAR-EGARH untuk Peramalan (Studi Kasus: Data Cash Outflow dan Inflow Bank Indonesia Kota Kediri)

Suharsono, Agus; Monica, Marieta; Purnomo, Jerry Dwi Trijoyo

doi:10.12962/j27213862.v5i1.12470

Cited by 2 publications

(1 citation statement)

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“…FFNN in statistical modeling could be viewed as a flexible class of nonlinear functions. A special form of FFNN with one hidden layer consisting of q neuron units and an output layer consisting of only one neuron unit with response or output values y ̂ was calculated by the Equation 5 [15]: : Neuron bias of the output layer; 𝑓 ?…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Forecasting the Number of Passenger at Jenderal Ahmad Yani Semarang International Airport Using Hybrid Singular Spectrum Analysis-Neural Network (Ssa-Nn) Method

Yunitasari,

Haris,

Arum

2023

Jurnal Statistika Universitas Muhammadiyah Semarang

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Transportation was an important sector of supporting the economic growth of a country. The impact of the Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted in the movement of the number of passengers decreasing quite drastically, but in mid-2020 the movement of the number of passengers had slowly increased. Forecasting was done to determine the flow of movement of the number of passengers in the future using the Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. The SSA method was expected to be able to decompose various patterns in the data into trend, seasonality and noise. Furthermore, the NN method was used to analyze nonlinear patterns in the data. The results showed that the best method was a combination of the SSA method with a window length of 40 and the NN method with a 6-8-1 network architecture (6 input neurons, 8 hidden neurons and 1 output neuron) for the trend component, 11-15-1 (11 neurons input, 15 hidden neurons and 1 output neuron) for the seasonal component, and 10-15-1 (10 input neurons, 15 hidden neurons and 1 output neuron) for the noise component. The model produces a prediction error based on a MAPE value of 0.54% or an accuracy rate of 99.46%.

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Section: Neural Network (Nn)mentioning

confidence: 99%