Saran - KESIMPULAN, KETERBATASAN, DAN SARAN

BAB 5. KESIMPULAN, KETERBATASAN, DAN SARAN

5.2 Saran

Berdasarkan penelitian yang telah dilakukan dan kesimpulan dari hasil penelitian ini, saran yang dapat diberikan untuk penelitian selanjutnya yaitu menggunakan dataset dengan data history lebih banyak dan membuat model FFNN yang lebih optimal, sehingga grafik dan nilai yang didapat dari proses peramalan lebih baik, dan juga dapat digunakan untuk peramalan jangka panjang (long-term prediction).

DAFTAR PUSTAKA

1. Isdarmanto. (2017). Dasar-Dasar Kepariwisataan dan Pengelolaan Destinasi Pariwisata.

2. Susilo, A., Rumende, C. M., Pitoyo, C. W., Santoso, W. D., Yulianti, M., Herikurniawan, H., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E. J., Chen, L.

K., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O. M., & Yunihastuti, E. (2020). Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia, 7(1), 45.

https://doi.org/10.7454/jpdi.v7i1.415

3. Syah, F., & Syafganti, I. (2021). Kesiapan Pariwisata Depok di Era New Normal. 4(1), 73–80. http://ojs.stiami.ac.id

4. Febrian, D., Idrus, S. I. Al, & Nainggolan, D. A. J. (2020). The Comparison of Double Moving Average and Double Exponential Smoothing Methods in Forecasting the Number of Foreign Tourists Coming to North Sumatera. Journal of Physics: Conference Series, 1462(1). https://doi.org/10.1088/1742- k6596/1462/1/012046

5. Ighravwe, D. E., & Anyaeche, C. O. (2019). A comparison of ARIMA and ANN techniques in predicting port productivity and berth effectiveness. International Journal of Data and Network Science, 3(1), 13–22.

https://doi.org/10.5267/j.ijdns.2018.11.003

6. Safitri, B. A., Iriany, A., & Wardhani, N. W. S. (2021). Perbandingan Akurasi Peramalan Curah Hujan dengan menggunakan ARIMA, Hybrid ARIMA-NN, dan FFNN di Kabupaten Malang. Seminar Nasional Official Statistics, 2021(1), 245–253. https://doi.org/10.34123/semnasoffstat.v2021i1.853

7. Sari, V., & Maulidany, D. A. (2020). Prediksi Kecepatan Angin dalam Mendeteksi Gelombang Air Laut terhadap Skala Beaufort dengan Metode Hybrid Arima-Ann. Jurnal Statistika, 8(1), 8–17.

8. Qamara, L. N., Wahyuningsih, S., & Tisna, D. (2019). Peramalan Harga Minyak Mentah Menggunakan Model Autoregressive Integrated Moving Average Neural Network ( ARIMA-NN ). Jurnal EKSPONENSIAL, 10, 127–134.

9. Rianto, M., & Yunis, R. (2021). Analisis Runtun Waktu Untuk Memprediksi Jumlah Mahasiswa Baru Dengan Model Random Forest. Paradigma - Jurnal Komputer Dan Informatika, 23(1). https://doi.org/10.31294/p.v23i1.9781 10. Rola, J. B., Sta Romana, C. L. C., Feliscuzo, L. S., Lopez, I. F. M., & Rola, C.

N. (2020). A comparative analysis of ARIMA and feed-forward neural network prognostic model for bull services. Advances in Science, Technology and Engineering Systems, 5(2), 411–418. https://doi.org/10.25046/aj050253

11. Baskoro, F., Alamsyah, F., & Suprianto, B. (2021). Peramalan Beban Listrik Harian Menggunakan Artificial Neural Network. Jurnal Teknik Elektro, 10(1), 203–209.

12. Rizk-Allah, R. M., & Hassanien, A. E. (2020). COVID-19 forecasting based on an improved interior search algorithm and multi-layer feed forward neural network. 1–24. https://doi.org/10.1007/978-3-030-91103-4_8

13. Module II Perception networks – Learning rule – Training and testing algorithm , AdaptiveLinear Neuron , Back propagation Network – Architecture , Training algorithm . (n.d.).

14. Škrinjarić, T. (2019). Book review: Multivariate time series analysis and applications. In Oeconomica Jadertina (Vol. 9, Issue 2).

15. Li, L., Wang, Y., & Li, X. (2020). Tourists Forecast Based on the Baolan High-Speed Railway by the Arima Model. Applied Mathematics and Nonlinear Sciences, 5(1), 55–60. https://doi.org/10.2478/amns.2020.1.00006

16. Shabri, A., Samsudin, R., & Yusoff, Y. (2020). Combining Deep Neural Network and Fourier Series for Tourist Arrivals Forecasting. IOP Conference Series: Materials Science and Engineering, 864(1).

https://doi.org/10.1088/1757-899X/864/1/012094

17. Herawati, S., & Latif, M. (2021). Forecasting tourist visits using data

decomposition technique and learning optimization of artificial neural network.

Journal of Physics: Conference Series. https://doi.org/10.1088/1742- 6596/1811/1/012092

18. Ukhra, A. U. (2014). Pemodelan Dan Peramalan Data Deret Waktu Dengan Metode Seasonal Arima. Jurnal Matematika UNAND, 3(3), 59.

https://doi.org/10.25077/jmu.3.3.59-67.2014

19. Rizal, A. A., & Soraya, S. (2018). Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 18(1), 115–124.

https://doi.org/10.30812/matrik.v18i1.344

20. Rusdi. (2011). Uji Akar-Akar Unit dalam Model Runtun Waktu Autoregresif.

Statistika, 11(2), 67–78.

21. Ritha, N., Bettiza, M., & Dufan, A. (2016). Prediksi Curah Hujan dengan Menggunakan Algoritma Levenberg-Marquardt dan Backpropagation. Jurnal Sustainable, 5(2), 11–16.

22. Rahmahwati, F. S., & Arifin, F. (2017). DEVELOPING ARTIFICIAL NEURAL NETWORK. 23(4), 402–411.

23. Sammen, S. S., Ghorbani, M. A., Malik, A., Tikhamarine, Y., AmirRahmani, M., Al-Ansari, N., & Chau, K. W. (2020). Enhanced artificial neural network with Harris hawks optimization for predicting scour depth downstream of ski- jump spillway. Applied Sciences (Switzerland), 10(15).

https://doi.org/10.3390/app10155160

24. Prediction, S. W. S. (2019). Optimization of Feedforward Neural Networks Using an Improved Flower Pollination Algorithm for Short-Term Wind Speed Prediction. Energies, doi:10.3390/en12214126

25. Nanda, S. K., Tripathy, D. P., Nayak, S. K., & Mohapatra, S. (2013).

Prediction of Rainfall in India using Artificial Neural Network (ANN) Models.

International Journal of Intelligent Systems and Applications, 5(12), 1–22.

https://doi.org/10.5815/ijisa.2013.12.01

26. Toharudin, T., Caraka, R. E., Bachrudin, A., Bakar, S. A., & Ambarsari, D.

(2019). Copula-based feedforward neural network genetic algorithm cargo forecasting. International Journal of Advances in Soft Computing and Its Applications, 11(1), 22–33.

27. Ayu, R., Gernowo, R., Fisika, D., Sains, F., Diponegoro, U., & E-, S. (2019).

Metode Autoregressive Integrated Movingaverage (Arima) Dan Metode Adaptive Neuro Fuzzy Inference System (Anfis) Dalam Analisis Curah Hujan.

Berkala Fisika, 22(1), 41–48.

28. Cheng, X., Feng, Z. K., & Niu, W. J. (2020). Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer. IEEE Access, 8, 157346–157355.

https://doi.org/10.1109/ACCESS.2020.3019574

29. Khan, S., Aslam, S., Mustafa, I., & Aslam, S. (2021). Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine. Forecasting, 3(3), 460–477.

https://doi.org/10.3390/forecast3030028

30. Anggarini, D. T. (2021). Upaya Pemulihan Industri Pariwisata Dalam Situasi Pandemi Covid -19. Jurnal Pariwisata, 8(1), 22–31.

https://doi.org/10.31294/par.v8i1.9809

31. Budiyanti, E. (2020). Dampak Virus Corona Terhadap Sektor Perdagangan Dan Pariwisata Indonesia. Kajian Bidang Ekonomi Dan Kebijakan Publik, XII(4), 19–24. http://berkas.dpr.go.id/puslit/files/info_singkat/Info Singkat-XII- 4-II-P3DI-Februari-2020-219.pdf

32. Rofifah, D. (2020). Pengantar Pariwisata. In Paper Knowledge . Toward a Media History of Documents.

33. Terence C. Mills. (2010). Applied time series analysis. In Lecture Notes in Electrical Engineering: Vol. 59 LNEE. https://doi.org/10.1007/978-90-481- 8768-3_2

34. Saâdaoui, F., Saadaoui, H., & Rabbouch, H. (2020). Hybrid feedforward ANN with NLS-based regression curve fitting for US air traffic forecasting. Neural Computing and Applications, 32(14), 10073–10085.

https://doi.org/10.1007/s00521-019-04539-5

LAMPIRAN-LAMPIRAN Lampiran 1 Tabel Jumlah Pengunjung Objek Wisata Kabupaten Situbondo

N O

OBYEK

WISATA ALAMAT

JUMLAH JUMLAH JUMLA

JUMLA H

Total ( orang )

Januari Pebruari Maret April Mei Juni Juli Agustus Septemb

er Oktober Nopemb er

Desembe r

Indonesia Asing Indonesia Asing In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

In do nes ia

A si n g

Indonesia Asing

1 TN Baluran Banyuputih 12.549 34 4.683 56 4.8

20 6 9

5.6 31

7 4

6.1 02

1 0 3

87 4

5 6

16.

75 9

2 1 9

5.4 05

1 8 5

5.5 20

1 7 4

4.9 11

1 3 8

5.4 64

3 9

12.

12 6

3 84.844 1.210

2 Pantai Pasir

Putih Bungatan 24.034 30 6.479 31 9.2

38 2 6

11.

25 4

5 0

17.

89 5

4 5

8.8

51 6 8.3 13 2

11.

47 2

2 2

15.

34 5

3 10.

63 9

2 9

11.

37 2

1 2

26.

28 9

161.181 256

Desa Kebangsaan

Wonorejo

Wonorejo 100 15 120 8 25

2 4 45

6 3 65

0 2 78

9 5 52

4 3 45

6 1 21

0 1 21

5 1 19

3 2 3.965 45

Rafting Samir Situbondo Adventure

Asembagus 201 - 245 - 29

8 - 25

2 - 27

4 - 25

7 - 23

0 - 21

0 - 19

5 - 31

0 1 19

5 1 2.667 2

Puncak Rengganis

Argopuro

Sumbermala

ng 12 5 10 3 9 2 7 4 8 2 5 1 6 2 7 2 5 1 4 - 9 1 82 23

6 Air Terjun

Talempong Banyuglugur 12 - 8 - 7 - 12 - 9 - 10 - 15 - 12 - 16 - 14 - 10 - 125 -

252.864 1.536

Lampiran 2 Tabel Jumlah Pengunjung Wisata Pantai Pasir Putih Situbondo tahun 2017-2021

Tanggal

Jumlah Pengunjung

Wisata

Tanggal

Jumlah Pengunjung

Wisata 31/01/2017 24064 31/07/2019 15673

28/02/2017 6510 31/08/2019 8535

31/03/2017 9264 30/09/2019 9536

30/04/2017 11304 31/10/2019 11157 31/05/2017 17940 30/11/2019 14065 30/06/2017 8857 31/12/2019 14810 31/07/2017 8315 31/01/2020 22614 31/08/2017 11494 29/02/2020 7035 30/09/2017 15348 31/03/2020 4373

31/10/2017 10668 30/04/2020

30/11/2017 11384 31/05/2020

31/12/2017 26892 30/06/2020

31/01/2018 24658 31/07/2020

28/02/2018 10368 31/08/2020

31/03/2018 10417 30/09/2020 6252 30/04/2018 17918 31/10/2020 15259 31/05/2018 8767 30/11/2020 14629 30/06/2018 30549 31/12/2020 3092 31/07/2018 16968 31/01/2021 9401

31/08/2018 8908 28/02/2021 7894

30/09/2018 9738 31/03/2021 9841

31/10/2018 6735 30/04/2021 8039

30/11/2018 9638 31/05/2021 13007 31/12/2018 2440 30/06/2021 18875

31/01/2019 9348 31/07/2021 288

28/02/2019 3822 31/08/2021 3358

31/03/2019 5382 30/09/2021 6435

30/04/2019 7659 31/10/2021 23362 31/05/2019 2586 30/11/2021 15671 30/06/2019 28854 31/12/2021 18539

Lampiran 3 library dan import dataset

Lampiran 4 visualisasi dataset

Lampiran 5 Peramalan Data Testing

Lampiran 6 Pembagian Dataset untuk Pembuatan Model FFNN

Lampiran 7 Pembutan Dimensi FFNN

Lampiran 8 Pembuatan Model FFNN

Lampiran 9 Visualisasi Peramalan Data Testing FFNN

Lampiran 10 Dataset Pengujian Hybrid model FFNN

Lampiran 11 Normalisasi Data

Lampiran 12 Pembuatan Dimensi

Lampiran 13 Pembuatan Dimensi

Lampiran 14 Pembuatan Model FFNN

Lampiran 15 Pengujian Model pada Dimensi

Lampiran 16 Peramalan Tahun 2021

Lampiran 17 Inverse Normalisasi Nilai Peramalan 2021

Lampiran 18 Pengambilan Index dari Dataset

Lampiran 19 Pemberian Index pada Nilai Peramalan

Lampiran 20 Visualisasi Nilai Peramalan

Lampiran 21 Membulatkan Nilai Peramalan

Lampiran 22 Menggabungkan Nilai Peramalan pada Dataset

Lampiran 23 Visualisasi Peramalan Hybrid

Lampiran 24 Peramalan ARIMA musiman 6 bulan

Lampiran 25 Nilai peramalan ARIMA musiman 6 bulan

Lampiran 26 Peramalan Hybrid model FFNN

Lampiran 27 Inverse Normalisasi

Lampiran 28 Pemberian Index Tanggal

Lampiran 29 Visualisasi Nilai Peramalan

Lampiran 30 Pembulatan nilai Peramalan

Lampiran 31 Penggabungan Nilai Peramalan pada Dataset

Lampiran 32 Pengisian Nilai Kosong Menggunakan Nilai Rata-Rata

Lampiran 33 Grafik Jumlah Pengunjung Wisata Menggunakan Nilai Rata-Rata

Lampiran 34 Tes ADF

Lampiran 35 Dekomposisi Dataset Rata-Rata

Lampiran 36 Model ARIMA (8,0,0)

Lampiran 37 Model ARIMA (17,0,0)

Lampiran 38 Plot ACF dan PACF musiman

Lampiran 39 Model ARIMA musiman

Lampiran 40 Plot Residual dan Plot Density

Lampiran 41 Grafik Prediksi Data Testing

Lampiran 42 Uji Nilai Error

Lampiran 43 Grafik Peramalan Data Testing ARIMA Musiman

Lampiran 44 Nilai Peramalan Data Testing ARIMA Musiman

Lampiran 45 Pembaruan Dataset Peramalan Data Testing

Lampiran 46 Grafik Dataset Baru

Lampiran 47 Pembuatan Model FFNN

Lampiran 48 Pembuatan Dimensi FFNN

Lampiran 49 Nilai Peramalan ARIMA musiman tahun 2022 Menggunakan Dataset Rata-Rata

Lampiran 50 Pembaruan Dataset Peramalan Hybrid

Lampiran 51 Pemberian Index Tanggal Pada Nilai Peramalan Hybrid Model FFNN

Dalam dokumen Peramalan Jumlah Wisatawan Menggunakan Metode Time Series Arima dan Feed-Forward Neural Network (Studi Kasus: Tempat Wisata Pasir Putih, Situbondo) (Halaman 51-79)