Classification of tourism business certification institution auditors based on the decision tree algorithm C4.5: case study of LSUP XYZ
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Published:
May 30, 2023
Keywords:
Auditors , Classification Algorithm C4.5 , Decision Tree , Tourism Business
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Computer Science
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Abstract
Auditors who have appropriate competence are needed to carry out certification. The amount of experience a person has before becoming an Auditor determines the competence possessed by an Auditor. To be assigned to carry out certification, an auditor needs special competencies such as areas of expertise, work experience, and the Schemes and Standards that become the reference for certification. Auditors owned by LSUP need to be classified so that they can be assigned according to their competence. The C4.5 Decision tree algorithm is one of the methods that can be used to create a model used to classify and predict auditor competence. 65 data from 221 data are used as training data to form a model in the form of a tree structure. This model consists of 3 attributes that form a Node in a tree structure, namely the Workplace, Skill and Standard attributes. Each attribute has a value that forms a branch. The model was evaluated using the Confusion Matrix with a matrix size of 7x7 which resulted in a TP value of 99, FP value of 121, FN value of 121, and TN value of 1199. The accuracy level of this model is quite good, namely 84%. But the Precise and Recall values are not very good, only 45%.
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Mariani, N. W. R. . and Sudjana, I. M. . (2023) “Classification of tourism business certification institution auditors based on the decision tree algorithm C4.5: case study of LSUP XYZ”, Jurnal Mantik, 7(1), pp. 112-123. doi: 10.35335/mantik.v7i1.3647.
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Chern, C.-C., Chen, Y.-J., & Hsiao, B. (2019). Decision tree–based classifier in providing telehealth service. BMC Medical Informatics and Decision Making, 19, 1–15.
Duan, L., He, J., Li, M., Dai, J., Zhou, Y., Lai, F., & Zhu, G. (2021). Based on a decision tree model for exploring the risk factors of smartphone addiction among children and adolescents in China during the COVID-19 pandemic. Frontiers in Psychiatry, 12, 652356.
Haik, M. N., Alburquerque-Sendín, F., Fernandes, R. A. S., Kamonseki, D. H., Almeida, L. A., Liebano, R. E., & Camargo, P. R. (2020). Biopsychosocial aspects in individuals with acute and chronic rotator cuff related shoulder pain: Classification based on a decision tree analysis. Diagnostics, 10(11), 928.
Hasanah, I., Purwanti, E., & Widiyanti, P. (2020). Design and Implementation of an Early Screening Application for Dengue Fever Patients Using Android-Based Decision Tree C4. 5 Method. International Journal on Advanced Science, Engineering and Information Technology, 10(6), 2237–2243.
Https://bsn.go.id/main/berita/detail/9497/workshop-pengembangan-sni-usaha-hotel-di-yogyakarta. (2018). Workshop Pengembangan SNI Usaha Hotel di Yogyakarta. Https://Bsn.Go.Id/Main/Berita/Detail/9497/Workshop-Pengembangan-Sni-Usaha-Hotel-Di-Yogyakarta.
Irena, B., & Setiawan, E. B. (2020). Fake news (hoax) identification on social media twitter using decision tree c4. 5 method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(4), 711–716.
Jia, S., & Pang, Y. (2018). Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm. International Journal of Emerging Technologies in Learning, 13(10).
Jijo, B. T., & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Evaluation, 6, 7.
Kemenparekraf. (2021). Menteri Pariwisata Dan Ekonomi Kreatif/ Kepala Badan Pariwisata Dan Ekonomi Kreatif Republik Indonesia. Peraturan Menteri Pariwisata Dan Ekonomi Kreatif RI Nomor 4 Tahun 2021 Tentang Standar Kegiatan Usaha Pada Penyellagaraan Perizinan Berusaha Berbasis Risiko Sektor Pariwisata. www.jdih.kemenparekraf.go.id
Križani?, S. (2020). Educational data mining using cluster analysis and decision tree technique: A case study. International Journal of Engineering Business Management, 12, 1847979020908675.
Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 1–29.
Munandar, T. A. (2018). The classification of cropping patterns based on regional climate classification using decision tree approach. ArXiv Preprint ArXiv:1803.11259.
No, P. P. (52 C.E.). Tahun 2012 tentang Sertifikasi Kompetensi dan Sertifikasi Usaha di Bidang Parwisata.
Nurkholis, A., Imas, S. S., Annisa, A., & Sobir, S. (2021). Spatial decision tree model for garlic land suitability evaluation. IAES International Journal of Artificial Intelligence, 10(3), 666.
Sadiq, M. H., & Ahmed, N. S. (2019). Classifying and predicting students’ performance using improved decision tree C4.5 in higher education institutes. Lubricants, 7(12), 1291–1306. https://doi.org/10.3844/jcssp.2019.1291.1306
Saeed, M. S., Mustafa, M. W., Sheikh, U. U., Khidrani, A., & Mohd, M. N. H. (2020). Theft Detection In Power Utilities Using Ensemble Of Chaid Decision Tree Algorithm. Science Proceedings Series, 2(2), 161–165.
Song, Y.-Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130.
Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11(2), 612–619.
Yang, S., Guo, J.-Z., & Jin, J.-W. (2018). An improved Id3 algorithm for medical data classification. Computers & Electrical Engineering, 65, 474–487.
Zhu, F., Tang, M., Xie, L., & Zhu, H. (2018). A classification algorithm of CART decision tree based on MapReduce attribute weights. International Journal of Performability Engineering, 14(1), 17.
Zhu, L., & Yang, Y. (2017). Improvement of decision tree ID3 algorithm. Collaborate Computing: Networking, Applications and Worksharing: 12th International Conference, CollaborateCom 2016, Beijing, China, November 10–11, 2016, Proceedings, 595–600.
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