Autor(s): Faozan Indresputra, Rizmoon Nurul Zulkarnaen, Muhammad Rifqi Hariri, Fitri Fatma Wardani, Prima Wahyu Kusuma Hutabarat, Dwi Setyanti, Widya Ayu Pratiwi, Lutfi Rahmaningtiyas, Dina Safarinanugraha
DOI: 10.59465/ijfr.2023.10.1.1-19


Since the establishment of the Bogor Botanical Garden (BBG) in 1817, the protection of the tree collections, even the loss of aging trees (> 100 years old), has been one of its most important tasks. Abiotic factors such as intense extreme events, i.e., heavy rainfall and strong winds, as well as biotic factors from human activities, pests and diseases, and the deterioration of the health of the plant collection with age, has threatened the survival of the old tree collections. As the BBG has many functions for conservation and human ecological activities, tree fall accidents have become a primary concern in preventing the loss of biodiversity and human life. Therefore, disaster map zonation is required to prevent and minimize such accident together with a prediction of which individual specimen is likely to fall. We examined the health of 154 to determine the falling probability of 1106 aged trees based on several factors that might cause the fall in the past and to make model predictions generated by nine supervised machine learning algorithms to get a binary value of falling probability and then classified into four categories (neglectable, low, moderate, and high probability of falling). Inverse Distance Weighted interpolation method was used to depict a zone map of trees prone to fall in BBG. We found 885 susceptible trees, of which 358 individual trees were highly susceptible to fall (red zone color), dominated by families from Fabaceae, Lauraceae, Moraceae, Meliaceae, Dipterocarpaceae, Sapindaceae, Rubiaceae, Myrtaceae, Araucariaceae, Malvaceae, and Anacardiaceae. This result was based on Random Forest model due to its highest accuracy among algorithms and its lowest false negative (FN) value. The FN value was important to minimize error calculation on aged trees that were not prone to fall but turned out to be prone to fall. The dominant factor contributing to high falling intensity was hollow and brittle on the tree trunks where many were found to have pests inside damaged parts such as termites, wood-borers, and bark-eaters. Several trees were found to have combined damages with more than a single causative factor that exacerbated tree’s health and increased falling probability.


aged trees; 100 years old; probability to fall; model predictions

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Achlioptas, P., & Stanford. (2017). Stochastic Gradient Descent in Theory and Practice. Stanford, pp. 1–16.

Affandi, S. D., Halimatussadiah, A., & Asrofani, F. W. (2020). Visitors’ preferences on the characteristics of bogor botanical gardens. Sustainability (Switzerland), 12(22), 1–18.

Agarwal, S., Jha, B., Kumar, T., Kumar, M., & Ranjan, P. (2019). Hybrid of Naive Bayes and Gaussian Naive Bayes for Classification: A Map Reduce Approach. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6), 266–268.

Appanah, S., & Turnbull, J. M. (1996). A review of Dipterocarps. In Center for International Forestry Research (Vol. 7).

Ariati, S. R., Astuti, R. S., Supriyatna, I., Yuswandi, A. Y., Setiawan, A., Saftaningsih, D., & Pribadi, D. O. (2019). An Alphabetical List of Plant Species Cultivated in The Bogor Botanic Gardens.

Ashari, H., Arifianto, D., & Faruq, H. A. Al. (2020). Perbandingan Kinerja Algoritma Multinomial Naïve Bayes ( Mnb ), Multivariate Bernoulli Dan Rocchio Algorithm Dalam Klasifikasi Konten Berita Hoax Berbahasa Indonesia Pada Media Sosial (Universitas Muhammadiyah Jember). Retrieved from

Australian National Botanic Gardens. (2016). Tree management strategy 2016-2026. Retieved from

Balakumar, B., Raviraj, P., & Sivaranjani, K. (2018). Prediction of Survivors in Titanic Dataset: A Comparative Study using Machine Learning Algorithms. International Journal of Emerging Research in Management and Technology, 6(6), 1.

Charbuty, B., & Abdulazeez, A. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 2(01), 20–28.

Chen, F. W., & Liu, C. W. (2012). Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy and Water Environment, 10(3), 209–222.

Chen, G., & Sun, W. (2018). The role of botanical gardens in scientific research, conservation, and citizen science. Plant Diversity, 40(4), 181–188.

Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794.

Coley, P. D. (1983). Herbivory and Defensive Characteristics of Tree Species in a Lowland Tropical Forest. Ecological Monographs, 53(2), 209–234.

Cutler, A., Cutler, D. R., & Stevens, J. R. (2011). Chapter: Random Forests. In Machine Learning (pp. 1–20).

Darussalam, A. D., Sugiyanto, D. R., & Lubis, D. P. (2021). Analisis krisis public relations pada peristiwa tumbangnya pohon di Kebun Raya Bogor. Profesi Humas Jurnal Ilmiah Ilmu Hubungan Masyarakat, 5(2), 251.

De Sa, C., Olukotun, K., & Ré, C. (2015). Global convergence of stochastic gradient descent for some non-convex matrix problems. 32nd International Conference on Machine Learning, ICML 2015, 3, 2322–2331.

Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., … Gorné, L. D. (2016). The global spectrum of plant form and function. Nature, 529(7585), 167–171.

FAO. (2007). Overview of Forest Pests, Thailand. Forestry Department. Retrieved from

Galbraith, D. A., Iwanycki, N. E., McGoey, B. V., McGregor, J., Pringle, J. S., Rothfels, C. J., & Smith, T. W. (2011). The Evolving Role of Botanical Gardens and Natural Areas: A Floristic Case Study from Royal Botanical Gardens, Canada. Plant Diversity and Resources, 33(1), 123–131. 10. 3724/ SP. J. 1143. 2011. 10235

García-Gonzalo, E., Fernández-Muñiz, Z., Nieto, P. J. G., Sánchez, A. B., & Fernández, M. M. (2016). Hard-rock stability analysis for span design in entry-type excavations with learning classifiers. Materials, 9(7), 1–19.

Geekiyanage, N., Goodale, U. M., Cao, K., & Kitajima, K. (2018). Leaf trait variations associated with habitat affinity of tropical karst tree species. Ecology and Evolution, 8(1), 286–295.

Hamzah, R., & Prayogo T. (2014). Interpolation Methods for Sea Surface Height Mapping. International Journal of Remote Sensing and Earth Sciences Vol.11 No.1 June 2014 : 33 – 40, 11(1), 33–40.

Hanley, M. E., Lamont, B. B., Fairbanks, M. M., & Rafferty, C. M. (2007). Plant structural traits and their role in anti-herbivore defence. Perspectives in Plant Ecology, Evolution and Systematics, 8(4), 157–178.

Hurley, B. P., Slippers, B., Sathyapala, S., & Wingfield, M. J. (2017). Challenges to planted forest health in developing economies. Biological Invasions, 19(11), 3273–3285.

Khan, A. U., Choudhury, M. A. R., Maleque, M. A., Dash, C. K., Talucder, M. S. A., Maukeeb, A. R. M., … Adnan, M. (2021). Management of insect pests and diseases of jackfruit (Artocarpus heterophyllus l.) in agroforestry system: a review. Acta Entomology and Zoology, 2(1), 37–46.

Kitajima, K., & Myers, J. A. (2008). Seedling ecophysiology : strategies toward achievement of positive net carbon balance. In: M. A. Leck, V. T. Parker, and R. L. Simpson. Seedling Ecology and Evolution, Chapter 8(Cambridge University Press), 172–188.

Koeser, A. K., Hasing, G., Mclean, D., & Northrop, R. (2016). Tree Risk Assessment Methods: A Comparison of Three Common Evaluation Forms. Universiry of Florida. Retrieved from

Kong, D. L., Wang, J. J., Kardol, P., Wu, H. F., Zeng, H., Deng, X. B., & Deng, Y. (2016). Economic strategies of plant absorptive roots vary with root diameter. Biogeosciences, 13(2), 415–424.

Kong, D., Wang, J., Kardol, P., Wu, H., Zeng, H., Deng, X., & Deng, Y. (2015). The root economics spectrum: divergence of absorptive root strategies with root diameter. Biogeosciences Discussions, 12(15), 13041–13067.

Krishnan, S., & Novy, A. (2016). The role of botanic gardens in the twenty-first centuryy. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 11(December 2016).

Kumar, R., & Indrayan, A. (2011). Receiver Operating Characteristic (ROC) Curve for Medical Researchers. Indian Pediatrics, 48, 277–287.

Louppe, G. (2015). PhD dissertation: Understanding Random Forests: From Theory to Practice. Retrieved from

Nair, K. S. S. (2000). Insect pests and diseases in Indonesian forest: an assessment of the major threats, research efforts and literature. In Center for International Forestry Research.

Nakamura, R., Cornelis, J. T., de Tombeur, F., Yoshinaga, A., Nakagawa, M., & Kitajima, K. (2020). Diversity of silicon release rates among tropical tree species during leaf-litter decomposition. Geoderma, 368(February), 114288.

Nakamura, R., Ishizawa, H., Wagai, R., Suzuki, S., Kitayama, K., & Kitajima, K. (2019). Silicon cycled by tropical forest trees: effects of species, elevation and parent material on Mount Kinabalu, Malaysia. Plant and Soil, 443(1–2), 155–166.

Ocak, R. Ö., & Kurtaslan, B. Ö. (2017). Education Function of Botanical Gardens. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9(June 2015), 6.

Onoda, Y., Westoby, M., Adler, P. B., Choong, A. M. F., Clissold, F. J., Cornelissen, J. H. C., … Yamashita, N. (2011). Global patterns of leaf mechanical properties. Ecology Letters, 14(3), 301–312.

Onoda, Y., Wright, I. J., Evans, J. R., Hikosaka, K., Kitajima, K., Niinemets, Ü., … Westoby, M. (2017). Physiological and structural tradeoffs underlying the leaf economics spectrum. New Phytologist, 214(4), 1447–1463.

Palmiotto, P. A., Davies, S. J., Vogt, K. A., Ashton, M. S., Vogt, D. J., & Ashton, P. S. (2004). Soil-related habitat specialization in dipterocarp rain forest tree species in Borneo. Journal of Ecology, 92(4), 609–623.

Powledge, F. (2011). The evolving role of botanical gardens. BioScience, 61(10), 743–749.

Rachmadiyanto, A. N., Wanda, I. F., Rinandio, D. S., & Magandhi, M. (2020). Evaluasi Kesuburan Tanah Pada Berbagai Tutupan Lahan Di Kebun Raya Bogor. Buletin Kebun Raya, 23(2), 114–125.

Rahman, I. F. (2020). Implementasi Metode Support Vector Machine, Multilayer Perceptron Dan Xgboost Pada Data Ekspresi Gen. Retrieved from

Razali, M., & Wandi, R. (2019). Inverse Distance Weight Spatial Interpolation for Topographic Surface 3D Modelling. TECHSI - Jurnal Teknik Informatika, 11(3), 385.

Read, J., Gras, E., Sanson, G. D., Clissold, F., & Brunt, C. (2003). Does chemical defence decline more in developing leaves that become strong and tough at maturity? Australian Journal of Botany, 51(5), 489–496.

Ruder, S. (2016). An overview of gradient descent optimization algorithms. Retrieved from

Schulman, L., & Lehvävirta, S. (2011). Botanic gardens in the age of climate change. Biodiversity and Conservation, 20(2), 217–220.

Singh, G., Kumar, B., Gaur, L., & Tyagi, A. (2019). Comparison between Multinomial and Bernoulli Naïve Bayes for Text Classification. 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019, (May 2020), 593–596.

Song, S., Chaudhuri, K., & Sarwate, A. D. (2013). Stochastic gradient descent with differentially private updates. 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings, (December 2013), 245–248.

Verbeeck, H., Bauters, M., Jackson, T., Shenkin, A., Disney, M., & Calders, K. (2019). Time for a Plant Structural Economics Spectrum. Frontiers in Forests and Global Change, 2(August), 1–5.

Wagner, M. R., Cobbinah, J. R., & Bosu, P. P. (2008). Forest entomology in West Tropical Africa: Forests insects of Ghana. In Forest Entomology in West Tropical Africa: Forests Insects of Ghana.

Wang, Y., Zhang, Y., Lu, Y., & Yu, X. (2020). A Comparative Assessment of Credit Risk Model Based on Machine Learning : a case study of bank loan data. Procedia Computer Science, 174, 141–149.

War, A. R., Paulraj, M. G., Ahmad, T., Buhroo, A. A., Hussain, B., Ignacimuthu, S., & Sharma, H. C. (2012). Mechanisms of Plant Defense against Insect Herbivores. Plant Signaling and Behavior, 7(10), 1306–1320.

Whittaker, R. J., Bush, M. B., & Richards, K. (1989). Plant recolonization and vegetation succession on the Krakatau Islands, Indonesia. Ecological Monographs, 59(2), 59–123.

Wibawa, A. P., Kurniawan, A. C., Murti, D. M. P., Adiperkasa, R. P., Putra, S. M., Kurniawan, S. A., & Nugraha, Y. R. (2019). Naïve Bayes Classifier for Journal Quartile Classification. International Journal of Recent Contributions from Engineering, Science & IT (IJES), 7(2), 91.

Zhang, Z. (2016). Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11).

Zhao, Y. T., Ali, A., & Yan, E. R. (2017). The plant economics spectrum is structured by leaf habits and growth forms across subtropical species. Tree Physiology, 37(2), 173–185.


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