Segmentation of Hikers Preferences for Hiking Boot Recommendations Using K-Means and SAW

Dian Fitri Islamiati Munisah; Devita Maulina  Putri

doi:10.32664/smatika.v16i03.2468

Penulis

Dian Fitri Islamiati Munisah Universitas Merdeka Malang
Devita Maulina Putri Universitas Merdeka Malang

DOI:

https://doi.org/10.32664/smatika.v16i03.2468

Kata Kunci:

Hiking Shoes, Hiker Preference, K-Means, Multi-Criteria Decision Making, Simple Additive Weighting

Abstrak

The increasing number of mountain climbers in Indonesia, particularly beginners, has led to issues in selecting the right hiking shoes. Without data-driven guidance, shoe selection tends to be influenced solely by brand popularity rather than suitability with physical needs and trail characteristics, which potentially increases the risk of injuries such as blisters or sprains. This problem is exacerbated by the lack of a system capable of accommodating differences in preferences among climber segments, considering that the needs of technical trail climbers differ significantly from those doing light hikes (day hikes).This research aims to identify mountain climbers' preference patterns and build a decision support system for hiking shoe recommendations based on user segmentation. The approach used is the integration of the K-Means Clustering method to group climbers based on shoe attribute preferences, and the Simple Additive Weighting (SAW) method to generate objective brand recommendation rankings based on weights extracted from the clustering results. Data was collected from 154 climber respondents on Mount Buthak and Mount Penanggungan, East Java, using a ranking-based preference questionnaire.The clustering results with the best performing though still suboptimal, configuration of K=2 (Silhouette Score 0.4506) produced two segments: Cluster 0, which prioritizes comfort and durability (80 respondents), and Cluster 1, which is oriented towards Brand and shoe Weight (74 respondents). The application of SAW yielded different recommendations for each segment, with Consina being the top recommendation for Cluster 0 (score 0.8663) and Nineten for Cluster 1 (score 0.8810). These results prove that the integration of K-Means and SAW can produce more objective and personalized recommendations compared to approaches based solely on brand popularity, while also serving as a foundation for market segmentation strategies for outdoor equipment manufacturers.

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