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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
I Kadek Nova Hary Saputra
Politeknik Negeri Bali
Computer Science & IT Economics, Econometrics & Finance

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Penerapan Data Mining pada Sistem Rekomendasi Destinasi Wisata Berbasis Term Frequency-Inverse Document Frequency dan Vector Space Model I Kadek Nova Hary Saputra; Agus Adi Putrawan; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 2 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No2.pp524-534

Abstract

Digital tourism transitions cause information overload, complicating tourist decisions with unstructured data. Collaborative filtering systems also encounter cold-start issues for new destinations that lack sufficient popularity metrics. To address this, this research develops a recommendation system for Bali tourist destinations using content-based filtering. The method integrates Term Frequency-Inverse Document Frequency (TF-IDF) to extract text weights and the Vector Space Model (VSM) via cosine similarity to measure user query and destination relevance. Built on a Model-View-Controller (MVC) architecture, the system dynamically links PHP Laravel and Python Flask via a REST API. Text preprocessing involves case folding, tokenizing, filtering, and stemming using the Sastrawi library. Evaluation results without Top-N cutoff restrictions show an average recall rate of 90.20%, an average accuracy of 88.18%, and a mean average precision (MAP) of 72.82%. Although the absence of cutoff limits suppressed average precision to 34.43%, setting a 0.05 relevance threshold efficiently filtered out irrelevant noise. The system comprehensively recommends potential semantic destinations without relying on popularity, while maintaining high-ranking quality.
Co-Authors Agus Adi Putrawan Gde Brahupadhya Subiksa