PREDIKSI JUMLAH KUNJUNGAN WISATAWAN ASIA TENGGARA KE INDONESIA MENGGUNAKAN RANTAI MARKOV DAN IMPLIKASINYA TERHADAP KESEJAHTERAAN KELUARGA LOKAL DI DAERAH PARIWISATA

Cecilia Br Parangin-angin; Combest Prajogo Tambunan; Nia Devi Friskauly; Novita Atika Sitorus; Retno Ayu Zalianti; Sudianto Manullang; Alvi Sahri Nasution

doi:10.24114/jkss.v23i1.55145

Authors

Cecilia Br Parangin-angin Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Combest Prajogo Tambunan Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Nia Devi Friskauly Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Novita Atika Sitorus Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Retno Ayu Zalianti Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Sudianto Manullang Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221
Alvi Sahri Nasution Matematika, FMIPA, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, Sumatera Utara 20221

DOI:

https://doi.org/10.24114/jkss.v23i1.55145

Abstract

Tourism is a strategic sector that contributes significantly to the national economy. One important indicator in this sector is the number of tourist visits from countries in Southeast Asia. This study aims to predict the number of tourist visits from countries in Southeast Asia to Indonesia using the Markov chain approach, a stochastic model that relies on the probability of transition between states. The data used is the number of tourist visits in 2024 obtained from the Central Statistics Agency (BPS). The number of visits is classified into three states: low, medium, and high. The analysis process includes the formation of a transition matrix, Markov properties test using the Chi-Square method, and steady state calculation using the Chapman-Kolmogorov equation. The test results show that the data fulfill the Markov properties, and the steady state prediction shows a stable long-term probability distribution of tourist visits.