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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Indonesian Journal of Business Intelligence (IJUBI) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Industri Terintegrasi (JUTIN) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) CITIZEN: Jurnal Ilmiah Mulitidisiplin Indonesia Jurnal Informatika Polinema (JIP)
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Journal : SKANIKA: Sistem Komputer dan Teknik Informatika

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Prediksi Kunjungan Wisatawan Di Kabupaten Bantul Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) Anisa Tri Banowati; Dhina Puspasari Wijaya; Dita Danianti; Deden Hardan Gutama
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 8 No 2 (2025): Jurnal SKANIKA Juli 2025
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v8i2.3583

Abstract

The Bantul Regency Tourism Office faces challenges in collecting tourist attraction data because it is still done manually. This study aims to develop a web-based prediction system using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. This system is designed to facilitate data collection on tourist attractions while generating predictions of visitor numbers. Based on the results of accuracy testing using MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error), the Parangtritis and Depok Beach tourist areas have MAE values of 35,157.41 and MAPE 22.75%, indicating a fairly large absolute prediction error but still reasonable considering the high volume of visits. Meanwhile, Samas Beach recorded the highest MAPE value of 165.22%, due to data fluctuations that make predictions inaccurate. Conversely, predictions for Goa Cemara Beach, Kwaru Beach, Goa Selarong Area, and Goa Cerme Area have MAPE values below 15%, indicating the model is able to provide fairly good prediction results with a small average error. However, at Pandansimo Beach, the MAPE value reached 46.47%, indicating the model was not yet adequate for this location due to unstable data trends. The results showed that the SARIMA model can be applied to a system to predict tourist visits, but with varying levels of accuracy at each tourist destination, depending on the stability of each tourist destination's data. Keywords: Tourist visit prediction, SARIMA, time series forecasting, web-based system, Bantul Regency Tourism Office
Co-Authors Amalia, Ade Ami Fauzijah Andari, Rachma Yuni Anisa Tri Banowati Darmawan, Yogi Dwiki Desta Prastowo, Wahit Dhina Puspasari Wijaya Dhina Puspasari Wijaya, Dhina Puspasari Gutama, Deden Hardan Gutama, Deden Hardan Hardan Gutama, Deden Imam Suryadi, Imam Iskandar, Reza Fauzan Nur Iskarini, Ariska Jawahir, M Haikal KHOIRUL ANAM Muhammad Rosyad Dzikrillah Mutiara Rahmadhani, Yola Padmarasti, Erda Panglima Perkasa, Putra Pramuntadi, Andri Prastowo, Wahit Desta Puspasari Wijaya, Dhina Qomaruzaman, Salis Nizar Romauli Nur Imani L Siregar, Ananda Rusmiyati, Ratih Saidah, Nadiyah Siregar, Sokina Wati Subhan Yazid, Ahmad Sukmaraharjo, Guntur Tri Wulandari Wahit Desta Prastowo Wijaya, Dhina Puspasawi