Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method

Ete, Agustinus Angelaus; Fitrianawati, Meita; Arifin, Muhammad

doi:10.2991/iconprocs-19.2019.24

Cited by 4 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…, 𝐼 " , … , 𝐼 9 was called eigentriple grouping. If 𝑚 = 𝑑 and 𝐼 = = {𝑗}, 𝑗 = 1, 2, … , 𝑑, then the appropriate grouping was called elementary [13].…”

Section: Reconstructionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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%.

show abstract

“…, 𝐼 " , … , 𝐼 9 was called eigentriple grouping. If 𝑚 = 𝑑 and 𝐼 = = {𝑗}, 𝑗 = 1, 2, … , 𝑑, then the appropriate grouping was called elementary [13].…”

Section: Reconstructionmentioning

confidence: 99%

“…with 𝑋 3 = Z𝜆 3 𝑈 3 𝑉 3 2 and matrix 𝑋 3 had rank 1. Therefore the 𝑋 3 matrix was an elementary matrix and a collection 7Z𝜆 3 , 𝑈 3 , 𝑉 3 9 was called eigentriple ke-i dari SVD [13].…”

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

View full text Add to dashboard Cite

show abstract

“…Nilai r ditentukan berdasarkan jumlah eigentriples yang tidak menunjukkan noise pada plot singular value. Rentetan singular values yang menurun secara perlahan pada plot singular value menunjukkan komponen noise [21]. Gambar 2 menunjukkan bahwa pergerakan antar singular values mulai melambat setelah eigentriple ke-7, maka eigentriple ke-1 sampai ke-7 merupakan komponen tren dan musiman.…”

Section: Identifikasi Dataunclassified

Peramalan jumlah kedatangan wisatawan mancanegara ke bali menggunakan metode hibrida SSA-WFTS

Clarissa

Sulandari

Respatiwulan³

2021

Jurnal Ilmiah Matematika

View full text Add to dashboard Cite

“…The existence of tourism will attract tourists. Tourists who come will increase regional income [11] . The number of visitors, the number of tourist objects, and the occupancy rate of hotels are factors that influence the tourism sector [2] .…”

Section: Introductionmentioning

confidence: 99%

Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

Mukhtar,

Remli,

Wan Salihin Wong

et al. 2023

TEM Journal

View full text Add to dashboard Cite

The DL (Deep Learning) method is the standard for forecasting tourist arrivals. This method provides very good forecasting results but needs improvement if the data is small. Statistical data from the BPS (Central Bureau of Statistics) needs to be corrected, resulting in forecasts that tend to be invalid. This study uses statistical data and GT (Google Trends) as a solution so that the data is sufficient. GT data has a lot of noise because there is a shift between web searches and departures. This difference will produce noise that needs to be cleaned. We use monthly data from January 2008 to December 2021 from BPS sources combined with GT. Hilbert-Huang Transform (HHT) is proposed to clean data from various disturbances. The DL used in this study is long short-time memory (LSTM) and was evaluated using the root mean squared error RMSE and mean absolute percentage error (MAPE). The evaluation results show that the HHT-LSTM results are better than without data cleaning.

show abstract

Forecasting the Number of Tourist Arrivals to Batam by applying the Singular Spectrum Analysis and the Arima Method

Cited by 4 publications

References 11 publications

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

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

Peramalan jumlah kedatangan wisatawan mancanegara ke bali menggunakan metode hibrida SSA-WFTS

Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

Contact Info

Product

Resources

About