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Iman, Hadad Karsa Nur
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Computer Science & IT Control & Systems Engineering

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Pemanfaatan Metode Decision Tree dengan Algoritma C4.5 Untuk Prediksi Potensi Kunjungan Wisatawan Iman, Hadad Karsa Nur; Latifah, Noor; Supriyono, Supriyono; Nugraha, Fajar
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.6684

Abstract

This research aims to predict potential tourism visits in Pati Regency, Indonesia, utilizing data mining methods, specifically the Decision Tree with the C4.5 algorithm. The significance of the tourism sector in a region's economy and sustainability, along with the potential of data in formulating more effective and targeted strategies and decisions, motivated this objective. The initial experiment results demonstrated the superior performance of the Decision Tree C4.5 method in predicting potential tourism visits in Pati Regency, with an accuracy of 96.42%, precision of 96.42%, and recall of 96.66%. This performance exceeded the Naive Bayes method, which yielded an accuracy of 82.14%, precision of 84.49%, and recall of 83.07%. The research highlights the potential of data mining methods in the tourism sector, especially for predicting tourism visits. The results are expected to assist stakeholders in formulating more effective strategies and decisions, contributing positively to the wider development of the tourism sector.
Pemanfaatan Metode Decision Tree dengan Algoritma C4.5 Untuk Prediksi Potensi Kunjungan Wisatawan Iman, Hadad Karsa Nur; Latifah, Noor; Supriyono, Supriyono; Nugraha, Fajar
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.6684

Abstract

This research aims to predict potential tourism visits in Pati Regency, Indonesia, utilizing data mining methods, specifically the Decision Tree with the C4.5 algorithm. The significance of the tourism sector in a region's economy and sustainability, along with the potential of data in formulating more effective and targeted strategies and decisions, motivated this objective. The initial experiment results demonstrated the superior performance of the Decision Tree C4.5 method in predicting potential tourism visits in Pati Regency, with an accuracy of 96.42%, precision of 96.42%, and recall of 96.66%. This performance exceeded the Naive Bayes method, which yielded an accuracy of 82.14%, precision of 84.49%, and recall of 83.07%. The research highlights the potential of data mining methods in the tourism sector, especially for predicting tourism visits. The results are expected to assist stakeholders in formulating more effective strategies and decisions, contributing positively to the wider development of the tourism sector.
Co-Authors Fajar Nugraha Noor Latifah Supriyono Supriyono