DOI: https://doi.org/10.14710/j.gauss.14.2.314-324

PERBANDINGAN PERFORMA MODEL ARIMA-GARCH DAN LSTM DALAM MERAMALKAN JUMLAH KUNJUNGAN WISATAWAN DANAU KASTOBA

*Laily Nissa Atul Mualifah orcid scopus  -  Program Studi Statistika dan Sains Data, Sekolah Sains Data Matematika dan Informatika, IPB University, Jl. Raya Dramaga, Kabupaten Bogor 16680, Jawa Barat, Indonesia, Indonesia
Dalilah Husna  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Jasmita Yasmin  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Avrel Chesia Berbina  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Fadhilah Yumna  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Muhammad Ali Uraidly  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Adelia Putri Pangestika  -  Sekolah Sains Data Matematika dan Informatika, IPB University, Indonesia
Open Access Copyright 2025 Jurnal Gaussian under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract

Kastoba Lake, located on Bawean Island, East Java, is a unique natural tourist destination with significant potential for further development. To enhance strategic tourism management, predicting tourist visit numbers is necessary. This study aims to assess the performance of the ARIMA-GARCH and Long Short-Term Memory (LSTM) models in predicting daily tourist arrivals to Kastoba Lake, based on data collected between March 2023 and July 2024. These two methods were specifically selected because the dataset exhibits nonlinear patterns and heterogeneous variance. The ARIMA-GARCH model was employed to handle heteroscedasticity within the data, while LSTM was chosen for its ability to effectively learn and represent long-term patterns. The findings indicate that both models deliver comparable performance and are highly capable of identifying the underlying data trends. Moreover, each model is effective in forecasting short-term tourist visits, particularly over a 7-day horizon (one week). Consequently, these models are reliable tools for predicting and analyzing tourism trends at Kastoba Lake.

Note: This article has supplementary file(s).

Fulltext View|Download |  Set Data
Data Kunjungan Danau Kastoba
Subject
Type Set Data
  Download (27KB)    Indexing metadata
 Analisis Data
Syntax ARIMA-GARCH Kunjungan Danau Kastoba
Subject
Type Analisis Data
  View (8KB)    Indexing metadata
Keywords: ARIMA-GARCH; Kastoba Lake; LSTM

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
Penerapan Metode Fuzzy Time Series Markov Chain Untuk Meramalkan Nilai Transaksi Belanja Menggunakan Uang Elektronik di Indonesia PENERAPAN MODEL REGRESI SEMIPARAMETRIK DERET FOURIER UNTUK MENGIDENTIFIKASI FAKTOR PENENTU ANGKA HARAPAN HIDUP DALAM KONTEKS SDGS 3 Pengklasteran dan Penerapan Rantai Markov untuk Prediksi Produksi padi dan Lahan Panen di Kalimantan Barat More recent articles
  1. Ananda, N., Wicaksana, H.S., Wijaya, Y.G., Hijazi, R. (2023) ‘Hyperparameter Tuning Lstm Sebagai Estimator Sensor Relative Humidity Pada Automatic Weather Station Berbasis Simulated Annealing Hyperparameter Tuning Lstm As Relative Humidity Sensor Estimator on Automatic Weather Station Based on Simulated Annealing’, Januari, 4(1), pp. 35–43
  2. Chandra, N.E. dan Sarinem (2015) ‘Kasus Virus Ebola Di Guinea Dengan Metode Arima’, Jurnal UJMC, 2(November), pp. 28–35. Available at: https://doi.org/https://doi.org/10.52166/ujmc.v2i1.447
  3. Desvina, A.P. dan Meijer, I.O. (2018) ‘Penerapan Model ARCH/GARCH untuk Peramalan Nilai Tukar Petani’, Jurnal Sains Matematika dan Statistika, 4(1), pp. 43–54
  4. Dzaky, A.T.R. (2021) ‘Deteksi Penyakit Tanaman Cabai Menggunakan Metode Convolutional Neural Network’, e-Proceeding of Engineering, 8(2), pp. 3039–3055. Available at: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/14701/14478
  5. Fahira, F. dan Prianto, C. (2023) ‘Prediksi Pola Kedatangan Turis Mancanegara dan Menganalisis Ulasan Tripadvisor dengan LSTM dan LDA’, Jurnal Tekno Insentif, 17(2), pp. 69–83. Available at: https://doi.org/10.36787/jti.v17i2.1096
  6. Garini, F.C. dan Anbiya, W. (2022) ‘Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region’, Jurnal Matematika, Statistika dan Komputasi, 18(2), pp. 198–223. Available at: https://doi.org/10.20956/j.v18i2.18382
  7. Hanif, A.F., Sasongko T.B. dan Laksito, A.D. (2023) ‘Perbandingan Kinerja LSTM, Bi-LSTM, dan GRU pada Klasifikasi Judul Berita Clickbait’, The Indonesian Journal of Computer Science, 12(4), pp. 2136–2150. Available at: https://doi.org/10.33022/ijcs.v12i4.3281
  8. Kurniawan, K., Ceasaro, B. dan Sucipto, S. (2024) ‘Perbandingan Fungsi Aktivasi Untuk Meningkatkan Kinerja Model LSTM Dalam Prediksi Ketinggian Air Sungai’, Jurnal Edukasi dan Penelitian Informatika (JEPIN), 10(1), pp. 134–143. Available at: https://doi.org/10.26418/jp.v10i1.72866
  9. Mualifah, L.N.A., Soleh, A.M. dan Notodiputro, K.A. (2024) ‘Comparison of GARCH, LSTM, and Hybrid GARCH-LSTM Models for Analyzing Data Volatility’, International Journal of Advances in Soft Computing and its Applications, 16(2), pp. 150–165. Available at: https://doi.org/10.15849/IJASCA.240730.10
  10. Notodiputro, K.A., Angraini, Y., Mualifah, L.N.A. (2025) ‘Analisis Data Deret Waktu dengan Python Pendekatan Box-Jenkins dan Machine Learning’, Bogor, IPB Press
  11. Pontoh, R.S., Toharudin, T., Ruchjana, B.N., Gumelar, F., Putri, F.A., Agisya, M.N., Caraka, R.E. (2022) ‘Jakarta Pandemic to Endemic Transition: Forecasting COVID-19 Using NNAR and LSTM’, Applied Sciences (Switzerland), 12(12). Available at: https://doi.org/10.3390/app12125771
  12. Putri, S.M. dan Arliani, E. (2022) ‘Peramalan produksi padi di Kabupaten Sleman menggunakan model arima forecasting rice production in Sleman Regency using the arima model’, Jurnal Kajian dan Terapan Matematika, 8(3), pp. 188–198. Available at: http://journal.student.uny.ac.id/ojs/index.php/jktm
  13. Raneo, A.P. dan Muthia, F. (2018) ‘Penerapan Model GARCH Dalam Peramalan Volatilitas di Bursa Efek Indonesia’, Jurnal Manajemen dan Bisnis Sriwijaya, 16(2018), pp. 194–202. Available at: http://ejournal.unsri.ac.id/index.php/jmbs
  14. Rizkilloh, M.F. dan Widiyanesti, S. (2022) ‘Prediksi Harga Cryptocurrency Menggunakan Algoritma Long Short Term Memory (LSTM)’, Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 6(1), pp. 25–31. Available at: https://doi.org/10.29207/resti.v6i1.3630
  15. Rusminto, M.Z., Wibowo, S.A. dan Wahyuni, F.S. (2024) ‘Peramalan Harga Saham Menggunakan Metode Arima (Autoregressive Integrated Moving Average) Time Series’, JATI (Jurnal Mahasiswa Teknik Informatika), 8(2), pp. 1263–1270. Available at: https://doi.org/10.36040/jati.v8i2.9089
  16. Saladan, F., Michael, A., Gallaran, F.B., Marchelin. (2023) ‘Prediksi Jumlah Kunjungan Wisatawan Di Toraja Utara Menggunakan Metode Long Short Term Memory’, 11 Infinity, 3(1), pp. 2777–0451. Available at: https://doi.org/10.47178/infinity.v3i1.2189
  17. Salsabila, F., Fatharani, R.A., Taqiyyudin, T.A., Rizki, M.I. (2022) ‘Aplikasi Model ARCH/GARCH dalam Peramalan Laju Inflasi Bulanan Indonesia’, Jurnal Sains Matematika dan Statistika, 8(1), pp. 34–45. Available at: https://doi.org/10.24014/jsms.v8i1.13252
  18. Setyowibowo, S., As’ad, M., Sujito, Farida, E. (2022) ‘Forecasting of Daily Gold Price using ARIMA-GARCH Hybrid Model’, Jurnal Ekonomi Pembangunan, 19(2), pp. 257–270. Available at: https://doi.org/10.29259/jep.v19i2.13903
  19. Suban, S.A., Madhan, K. dan Shagirbasha, S. (2023) ‘A bibliometric analysis of Halal and Islamic tourism’, International Hospitality Review, 37(2), pp. 219–242. Available at: https://doi.org/10.1108/ihr-05-2021-0038
  20. Sugiartawan, P., Permana, A.A.J., dan Prakoso, P.I. (2018) ‘Forecasting Kunjungan Wisatawan Dengan Long Short Term Memory (LSTM)’, Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI), 1(1), pp. 43–52. Available at: https://doi.org/10.33173/jsikti.5
  21. Sunarti, Mariani, S. dan Sugiman (2016) ‘Perbandingan Akurasi Model ARCH dan GARCH pada Peramalan Harga Saham Berbantuan MATLAB’, UNNES Journal of Mathematics, 3(2), pp. 4–7. Available at: http://journal.unnes.ac.id/sju/index.php/ujme
  22. Verianto, E. (2024) ‘Penerapan LSTM dengan regularisasi untuk mencegah overfitting pada model prediksi tingkat inflasi di Indonesia’, Jurnal Sistem Informasi dan Sistem Komputer, 9(2), pp. 195–204. Available at: https://doi.org/10.51717/simkom.v9i2.460
  23. Witanto, K.S., Sanjaya, ER., Karyawati, A.E., Kadyanan, I.A.G.A., Suhartana, I.K.G., Astuti, L.G. (2022) ‘Implementasi LSTM Pada Analisis Sentimen Review Film Menggunakan Adam Dan RMSprop Optimizer’, JELIKU (Jurnal Elektronik Ilmu Komputer Udayana), 10(4), p. 351. Available at: https://doi.org/10.24843/jlk.2022.v10.i04.p05

Last update:

No citation recorded.

Last update:

No citation recorded.