Metode Klasterisasi Untuk Pengelompokan Potensi Wisata guna Menunjang Digitalisasi Wisata di Indonesia

Authors

  • Muqorobin Institut Teknologi Bisnis AAS Indonesia, Sukoharjo, Indonesia
  • Sri Supatminingsih Institut Teknologi Bisnis AAS Indonesia, Sukoharjo, Indonesia
  • Dewi Hermawati Wahyuningsih Sekolah Tinggi Pariwisata Sahid Surakarta, Surakarta, Indonesia

DOI:

https://doi.org/10.23887/jstundiksha.v13i2.84743

Keywords:

Clustering Method, Tourism Potential, Tourism Digitalization, Indonesia

Abstract

Digitalisasi wisata telah menjadi tren global yang memerlukan pendekatan sistem cerdas dalam pengelolaan dan pemasaran destinasi wisata. Pengembangan metode klasterisasi yang efektif dapat menjadi langkah kunci untuk mengidentifikasi, mengelompokkan, dan memahami potensi wisata yang beragam di Indonesia. Dengan adanya pengembangan metode klasterisasi yang canggih maka dapat memungkinkan terwujudnya strategi digital yang lebih efektif. Pengembangan Metode Klasterisasi yang efektif penting untuk mengidentifikasi dan memahami beragam potensi pariwisata di Indonesia. Hal ini menjadi urgensi masalah yang sangat penting mengingat banyak wisatawan yang kesulitan memilih tempat wisata terbaik. Tujuan Penelitian ini untuk mengembangkan metode klasterisasi untuk mendukung digitalisasi pariwisata di Indonesia, dengan mengintegrasikan Algoritma K-Means Clustering dan Weighted Product. Pendekatan yang digunakan dalam penelitian ini yakni dengan Metode untuk mengelompokkan potensi wisata dalam tiga klaster yaitu besar, sedang, dan kecil. Kemudian dilanjutkan rekomendasi wisata terbaik berdasarkan kriteria: keindahan, budaya, akses, fasilitas, aktivitas, iklim, keunikan, keamanan, keterlibatan, kebersihan, promosi. Penelitian ini meliputi studi literatur, pengumpulan data, pengembangan metode klasterisasi dan pengujian sistem. Model ini diharapkan dapat membantu pengelola pariwisata dalam mewujudkan digitalisasi yang lebih efektif di Indonesia. Hasilnya akhir dari model klasterisasi ini adalah tiga klaster pariwisata dengan tingkat potensi yang berbeda-beda yaitu pada cluster 1 adalah 12%, cluster 2 adalah 54% dan cluster 3 adalah 34%. Pengujian yang dilakukan pada uji fungsionalitas menunjukan hasil diterima pada seluruh skenario uji dan hasil uji Silhouette Coefisien menunjukkan struktur data yang kuat dengan skor rata-rata 0,87.

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Published

2024-07-25

How to Cite

Muqorobin, Sri Supatminingsih, & Dewi Hermawati Wahyuningsih. (2024). Metode Klasterisasi Untuk Pengelompokan Potensi Wisata guna Menunjang Digitalisasi Wisata di Indonesia. JST (Jurnal Sains Dan Teknologi), 13(2), 217–230. https://doi.org/10.23887/jstundiksha.v13i2.84743

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Vol. 13 No. 2 (2024): July

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