Analisis Kebakaran pada Hutan dan Lokasi Lahan di Provinsi Riau Menggunakan Metode C4.5
DOI:
https://doi.org/10.32493/informatika.v7i1.17176Keywords:
Data mining, C4.5, Decision Tree, Forest FireAbstract
Data mining is a step of the process that is useful and necessary to analyze the data that has been obtained from a particular situation with mathematical calculations. A decision tree (decision tree) is an algorithm that is widely used in data mining classification. One of the algorithms that can help produce decision tree outputs is the C4.5 algorithm. Data mining consists of preprocessing, evaluating patterns, and presenting knowledge from an application. The dataset used in forest fires is taken from the BMKG website and classified according to its attributes so that the data can be processed to produce output for decision-making. The classification process used to process datasets with specific methods can predict events such as weather forecasts and traffic jams to predict natural disasters. Forest fires are very detrimental disasters because they can pollute the environment, interfere with health such as breathing and vision, and weaken the economy of a region. This study aims to apply the C4.5 algorithm, which can analyze and predict forest fires in the Riau province. The implementation of the C.45 algorithm method with an average value attribute has an accuracy value of 92.54% and a decision tree, which is an accuracy value of 99.2% with an equation test and test results with a confusion matrix.References
Endrawati, Purwanto, J., Nugroho, S., & S., R. A. (2018). Identifikasi Areal Bekas Kebakaran Hutan Dan Lahan Menggunakan Analisis Semi Otomatis Citra Satelit Landsat.
Fathimah, A. I., Arianti, N. D., Mupaat, & Junfithrana, A. P. (2021). Penerapan Data Mining Dengan Metode Apriori Pada Penjualan Sembako, 8(1), 20-26.
LPPM IPB. (2020, 12 18). P4W - LPPM. Retrieved 12 30, 2021, from https://p4w.ipb.ac.id/en/kajian-akademis-dan-penyusunan-penaksiran-kerugian-pasca-kejadian-kebakaran-hutan-dan-lahan-karhutla-tahun-2020-di-kabupaten-kotawaringin-timur/
Muhamad, Windarto, A. P., & Suhada. (2019). Penerapan Algoritma C4.5 Pada Klasifikasi Potensi Siswa Drop Out, 3(1), 753-760.
Nugroho, T. B., & Sugiharti, E. (2021). The Improvement of C4.5 Algorithm Accuracy in Predicting Forest Fires Using Discretization and AdaBoost, 43-52.
Sineva, I., & Molovtsev, M. D. (2020). An Integrated Approach to the Regression Problem in Forest Fires Detection.
Sugiharti, E., Firmansyah, S., & Devi, F. R. (2017). Predictive Evaluation of Performance of Computer Science Students of UNNES using Data Mining Based on Naïve Bayes Classifier (NBC) Algorithm, 95(4).
Verbeke, W., Dejaeger, K., Martens, D., Hur, J., & Baesens, B. (2012). New Insights into Churn Prediction in the Telecommunication Sector: A Profit Driven Data Mining Approach. European Journal of Operational Research, 218(1), 211-229. doi:10.1016/j.ejor.2011.09.031
Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Burlington: Morgan Kaufmann.
Yap, B. W., Rani, K. A., Rahman, H. A., Fong, S., Khairudin, Z., & Abdullah, N. N. (2014). An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). 285, pp. 13-22. Singapore: Springer. doi:10.1007/978-981-4585-18-7_2
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Informatika Universitas Pamulang have CC-BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Informatika Universitas Pamulang recognize that free access is better than priced access, libre access is better than free access, and libre under CC-BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
Jurnal Informatika Universitas Pamulang is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
YOU ARE FREE TO:
- Share : copy and redistribute the material in any medium or format
- Adapt : remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms