Classification of Tourist Attractions in Central Aceh District using the C4.5 Decision Tree Algorithm
Article Sidebar
Main Article Content
Abstract
Central Aceh Regency is a region with rapidly growing tourism potential, characterized by lakes, mountains, and cultural sites typical of the Gayo people. Although the available tourist attractions are quite diverse, the presentation of unstructured information often makes it difficult for tourists to determine destinations that suit their needs and preferences. To address this problem, this study implemented the C4.5 decision tree algorithm to classify tourist attractions in Central Aceh Regency. The study used five main attributes: type of tourism, accessibility, facilities, ticket prices, and the Number of annual visitors. Data were obtained through field observations, interviews, and online reviews, with a total of 54 tourist attractions being sampled. The analysis process began with data preprocessing, entropy calculations, and information gain and gain ratio to construct a decision tree. The modelling results showed that the accessibility attribute produced the highest gain ratio and became the root node in the tree. Furthermore, the Number of visitors attributed became the dominant factor in the next branch, consistently distinguishing the classes. The classification system resulted in three recommendation categories: Highly Recommended, Recommended, and Not Recommended. Model evaluation using a confusion matrix showed 92% accuracy, 90% precision, and 90% recall, indicating that the C4.5 algorithm is effective at grouping tourist attractions based on their characteristics. This research contributes to a data-driven model that can help tourists obtain more systematic information, while also supporting local governments and tourism stakeholders in developing more targeted destination development strategies.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] S. Sasmita and D. Putri, “Tourism development and regional economy,” Tourism Economics, vol. 28, no. 3, pp. 601–615, 2022, doi: 10.1177/13548166211034567.
[3] Zulfahmi et al., “Tourism recommendation systems using data mining,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, pp. 210–218, 2023, doi: 10.14569/IJACSA.2023.0130425.
[4] A. Pradana et al., “Classification techniques in data mining,” Journal of Big Data, vol. 9, no. 1, pp. 1–15, 2022, doi: 10.1186/s40537-022-00545-9.
[5] J. R. Quinlan, “C4.5: Programs for machine learning,” Machine Learning, vol. 16, no. 3, pp. 235–240, 2014, doi: 10.1023/A:102264320487.
[6] A. Wicaksono and D. Prasetya, “Explainable decision tree models,” IEEE Access, vol. 12, pp. 33421–33430, 2024, doi: 10.1109/ACCESS.2024.3342109.
[7] N. Leniawati and A. Wijayanto, “Classification of tourism villages using C4.5,” Journal of Tourism Research, vol. 18, no. 1, pp. 55–63, 2024, doi: 10.1080/13032917.2024.1132456.
[8] R. Ela and T. Hariyanto, “Tourist satisfaction analysis using decision tree,” Journal of Information Technology, vol. 7, no. 2, pp. 101–109, 2023, doi: 10.1109/JIT.2023.9876543.
[9] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2022, doi: 10.1016/C2019-0-02134-6.
[10] M. Musa et al., “Classification performance of C4.5,” Applied Soft Computing, vol. 115, p. 108190, 2022, doi: 10.1016/j.asoc.2022.108190.
[11] D. Andriani et al., “Decision support systems in tourism,” Sustainability, vol. 14, no. 6, p. 3201, 2022, doi: 10.3390/su14063201.
[12] F. Irawan et al., “Tourism data analytics,” Information, vol. 14, no. 1, p. 22, 2023, doi: 10.3390/info14010022.
[13] R. Budiarto et al., “Supervised learning for tourism data,” Computers, vol. 11, no. 5, p. 68, 2022, doi: 10.3390/computers11050068.
[14] S. Dewi et al., “Big data mining trends,” Future Internet, vol. 14, no. 9, p. 267, 2022, doi: 10.3390/fi14090267.
[15] M. Yunus et al., “Decision tree implementation,” Journal of Information Science, vol. 48, no. 4, pp. 550–560, 2022, doi: 10.1177/01655515221087654.
[16] N. Suryani et al., “AI-based tourism systems,” IEEE Transactions on Engineering Management, vol. 70, no. 2, pp. 412–421, 2023, doi: 10.1109/TEM.2022.3145678.
[17] A. Rahman et al., “Gain ratio optimization,” Expert Systems with Applications, vol. 210, p. 118382, 2022, doi: 10.1016/j.eswa.2022.118382.
[18] Nasrullah, “Decision tree accuracy analysis,” Journal of Computer Science, vol. 17, no. 3, pp. 231–240, 2021, doi: 10.3844/jcssp.2021.231.240.
[19] Qisthiano et al., “Student data classification,” Procedia Computer Science, vol. 216, pp. 82–90, 2023, doi: 10.1016/j.procs.2023.01.010.
[20] Fitryah et al., “Family welfare classification,” International Journal of Data Science, vol. 6, no. 1, pp. 14–22, 2022, doi: 10.1504/IJDS.2022.123456.