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Title: | Pengelompokan Lokasi Pariwisata di Indonesia dengan Menggunakan Variasi Distance pada Algoritma K-Means |
Other Titles: | Grouping of Tourism Locations in Indonesia Using Distance Variations in the K-Means Algorithm |
Authors: | Farida, Juni Irsan |
metadata.dc.contributor.advisor: | Lubis, Andre Hasudungan |
Keywords: | Clustering;Tourism;K-Means Algorithm;Distance Metrics;Pariwisata;Algoritma K-Means;Metrik Jarak |
Issue Date: | 21-Apr-2025 |
Publisher: | Universitas Medan Area |
Series/Report no.: | NPM;198160036 |
Abstract: | Indonesia memiliki kekayaan destinasi wisata yang beragam, namun pemetaan dan pengelompokannya masih menjadi tantangan dalam pengelolaan sektor pariwisata. Penelitian ini bertujuan untuk mengelompokkan lokasi wisata di Indonesia dengan menerapkan algoritma K-Means menggunakan tiga variasi metrik jarak, yaitu Euclidean Distance, Manhattan Distance, dan Canberra Distance. Data penelitian diperoleh dari sumber terbuka, kemudian melalui tahapan pra-pemrosesan, termasuk normalisasi data. Penentuan jumlah klaster optimal dilakukan menggunakan metode Elbow, sedangkan evaluasi hasil clustering dianalisis dengan Silhouette Score dan Davies-Bouldin Index. Hasil penelitian menunjukkan bahwa penggunaan Manhattan Distance menghasilkan nilai Silhouette Score tertinggi (0,321463), yang menunjukkan kualitas pengelompokan yang lebih baik dibandingkan dua metode lainnya. Hasil pengelompokan ini diharapkan dapat memberikan informasi yang lebih komprehensif bagi pemangku kepentingan dalam mendukung strategi promosi dan pengembangan infrastruktur pariwisata secara lebih efektif. Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts. |
Description: | 11 Halaman |
URI: | https://repositori.uma.ac.id/handle/123456789/27664 |
Appears in Collections: | SP - Informatic Engineering |
Files in This Item:
File | Description | Size | Format | |
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198160036 - Juni Irsan Farida - Fulltext.pdf | Fultext | 1.01 MB | Adobe PDF | View/Open |
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