PERAMALAN WISATAWAN MANCANEGARA BERKUNJUNG KE BALI MENGGUNAKAN METODE HYBRID HOLT WINTERS-ARTIFICIAL NEURAL NETWORK

LAILY, HIMMATURROHMAH (2023) PERAMALAN WISATAWAN MANCANEGARA BERKUNJUNG KE BALI MENGGUNAKAN METODE HYBRID HOLT WINTERS-ARTIFICIAL NEURAL NETWORK. Sarjana / Sarjana Terapan (S1/D4) thesis, Universitas Muhammadiyah Semarang.

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Abstract

ABSTRAK Laily Himmaturrohmah, 2022, Peramalan Wisatawan Mancanegara Berkunjung ke Bali Menggunakan Metode Hybrid Holt Winters-Artificial Neural Network, Skripsi, Program Studi Statistika, Universitas Muhammadiyah Semarang. Pembimbing: I. M Al Haris, M.Si., II. Indah Manfaati Nur, M.Si. Bali merupakan salah satu tempat yang sering dikunjungi wisatawan karena memiliki keindahan alam khususnya pada sektor pariwisata. Jumlah wisatawan mancanegara yang datang ke Bali sampai tahun 2019 terus meningkat dari tahun ke tahun, akan tetapi terjadi penurunan yang sangat signifikan pada tahun 2020. Peramalan jumlah wisatawan mancanegara berkunjung ke Bali pada masa yang akan datang diperlukan untuk memberikan masukan atau rekomendasi kepada pemerintah maupun pelaku bisnis dalam mengantisipasi keputusan yang diambil dalam proses pengembangan sektor pariwisata di Bali. Salah satu metode peramalan yang dapat digunakan yaitu metode Holt Winters. Metode Holt Winters merupakan salah satu variasi dari Exponential Smoothing yang didasarkan atas tiga persamaan penghalusan, yaitu unsur stasioner, trend, dan musiman. Akan tetapi metode Holt Winters hanya dapat menangkap pola linier sehingga dibutuhkan metode yang dapat menangkap pola non linier, salah satunya yaitu Artificial Neural Network. Penelitian ini difokuskan pada jumlah wisatawan mancanegara yang berkunjung ke Bali dengan metode Hybrid Holt Winters-Artificial Neural Network. Hasil penelitian menunjukkan bahwa secara umum data jumlah wisatawan mancanegara setiap bulannya mengalami fluktuasi. Metode terbaik untuk meramalkan jumlah wisatawan mancanegara adalah Hybrid Holt Winters (α=0.987,β=0.000001,dan γ=1)-Artificial Neural Network (12-15-1) karena memiliki akurasi terbaik yang ditunjukkan dengan nilai MAD 0.036684, MSE 0.01098698, dan MAPE 6.30417%. Kata Kunci: Artificial Neural Network, Holt Winters, Wisatawan Mancanegara.   ABSTRACT Laily Himmaturrohmah, 2022, Forecasting Foreign Tourists Visiting Bali Using the Hybrid Holt Winters-Artificial Neural Network Method. Thesis, Statistics Study Program, University of Muhammadiyah Semarang. Supervisor: I. M Al Haris, M.Si., II. Indah Manfaati Nur, M.Si. Bali is a place that is often visited by tourists because it has natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 continues to increase from year to year, but there is a very significant decrease in 2020. Forecasting the number of foreign tourists visiting Bali in the future is needed to provide input or recommendations to the government and actors business in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used is the Holt Winterss method. The Holt-Winters method is a variation of Exponential Smoothing which is based on three smoothing equations, namely stationary, trend and seasonal elements. However, the Holt Winters method can only capture linear patterns, so a method is needed that can capture non-linear patterns, one of which is an Artificial Neural Network. This research is focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that in general the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists is the Hybrid Holt Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by MAD values of 0.036684, MSE 0.01098698, and MAPE 6.30417%. Keywords: Artificial Neural Network, Holt Winters, International Tourists.

Item Type: Thesis (Sarjana / Sarjana Terapan (S1/D4) )
Call Number: 004/Statistika/VII/2023
Subjects: L Education > Statistics
Divisions: Faculty of Agricultural Science and Technology > S1 Statistics
Depositing User: perpus unimus
Date Deposited: 17 Jul 2023 03:18
Last Modified: 17 Jul 2023 03:18
URI: http://repository.unimus.ac.id/id/eprint/7124

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