Analisis Konseptual tentang Penerapan Teori Probabilitas Lanjut dalam Pengembangan Model Statistik Modern
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May 31, 2025
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This study aims to examine the application of advanced probability theory in the development of modern statistical models through a Systematic Literature Review approach to 25 scientific articles published in 2020–2025. This study is complemented by a bibliometric analysis using VOSviewer to explore the relationship of terms, temporal trends, and concept density in the literature. The visualization results show that the terms "model" and "probability" are the main nodes in concept development, with the strengthening of the themes of distribution, uncertainty, and cross-disciplinary applications. The application of probability theory is seen dominantly in the fields of engineering, environment, transportation systems, and human behavior. The study also shows a shift in focus from classical risk distribution to more complex and contextual predictive modeling. The emergence of new terms such as "stack effect", "reaction time", and "p pod method" marks the growing interest in the application of probability in physical simulations and advanced technical systems. These results strengthen the position of probability theory as an adaptive conceptual framework in responding to the challenges of modern data analysis and uncertainty.
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Hanggoro, D. B. D. (2025). Analisis Konseptual tentang Penerapan Teori Probabilitas Lanjut dalam Pengembangan Model Statistik Modern. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(2), 1458-1567. https://doi.org/10.63447/jimik.v6i2.1450
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References
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Bera, B., Saha, S., & Bhattacharjee, S. (2020). Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India. Trees, Forests and People, 2, 100034.
Chandra, A., & Verma, A. (2025). To delay or not to delay? A hybrid relationship between departure delay, en-route conflict probability, and number of conflicts. Journal of the Air Transport Research Society, 4, 100053.
Chen, H. H., Lu, H. H. S., Weng, W. H., & Lin, Y. H. (2023). Developing a machine learning algorithm to predict the probability of medical staff work mode using human-smartphone interaction patterns: algorithm development and validation study. Journal of Medical Internet Research, 25, e48834.
Chouhan, R., Dhamaniya, A., Rao, A. M., & Gupta, K. (2025). Quantifying the relationship between auditory distractions, reaction time, and crash probability. IATSS Research, 49(1), 60-71.
Elbatal, I., Sarwar, M., Jamal, F., Daniyal, M., Hussain, Z., & Ghorbal, A. B. (2025). Modelling on gross domestic product annual growth rate data by using time series, machine learning, and probability models. Journal of Radiation Research and Applied Sciences, 18(2), 101481.
Halstensen, T. D., Hardeland, C., Ghanima, W., Grøndahl, V. A., Hubin, A., & Tavoly, M. (2024). Development and internal validation of a simple clinical score for the estimation of the probability of deep vein thrombosis in outpatient emergency department patients. Research and Practice in Thrombosis and Haemostasis, 8(8), 102608. https://doi.org/10.1016/j.rpth.2024.102608.
Han, C., & Xu, Z. (2023). Prediction and output estimation of pattern moving in non-Newtonian mechanical systems based on probability density evolution. CMES - Computer Modeling in Engineering and Sciences, 139(1), 515–536. https://doi.org/10.32604/cmes.2023.043464.
Hosseinian, S., Hemmati, M., Dede, C., Salzillo, T. C., van Dijk, L. v., Mohamed, A. S. R., Lai, S. Y., Schaefer, A. J., & Fuller, C. D. (2024). Cluster-based toxicity estimation of osteoradionecrosis via unsupervised machine learning: Moving beyond single dose-parameter normal tissue complication probability by using whole dose-volume histograms for cohort risk stratification. International Journal of Radiation OncologyBiologyPhysics, 119(5), 1569–1578. https://doi.org/10.1016/j.ijrobp.2024.02.021.
Ingman, N., Voytolovskiy, N., Kurakova, O., & Mezina, N. (2022). Estimate the probability of default of a transport company with limited statistical information. Transportation Research Procedia, 63, 1561–1568. https://doi.org/10.1016/j.trpro.2022.06.168.
Jena, R., Shanableh, A., Al-Ruzouq, R., Pradhan, B., Gibril, M. B. A., Khalil, M. A., Ghorbanzadeh, O., & Ghamisi, P. (2023). Earthquake spatial probability and hazard estimation using various explainable AI (XAI) models at the Arabian peninsula. Remote Sensing Applications: Society and Environment, 31, 101004. https://doi.org/10.1016/j.rsase.2023.101004.
Kassem, Y., Gökçekuş, H., Çamur, H., & Esenel, E. (2021). Statistical analysis and determination of best-fit probability distribution for monthly rainfall in Northern Cyprus. Desalination and Water Treatment, 215, 347–379. https://doi.org/10.5004/dwt.2021.26556.
Kim, S., & Lee, S. (2022). Optimal Bayesian MCMC based fire brigade non-suppression probability model considering uncertainty of parameters. Nuclear Engineering and Technology, 54(8), 2941–2959. https://doi.org/10.1016/j.net.2022.03.015.
Lai, T.-Y., Hu, Y.-W., Wang, T.-H., Chen, J.-P., Shiau, C.-Y., Huang, P.-I., Lai, I.-C., Liu, Y.-M., Huang, C.-C., Tseng, L.-M., Huang, N., & Liu, C.-J. (2024). Estimating the risk of major adverse cardiac events following radiotherapy for left breast cancer using a modified generalized Lyman normal-tissue complication probability model. The Breast, 77, 103788. https://doi.org/10.1016/j.breast.2024.103788.
Liang, K., Liu, J., Al-Rashidi, N., Odhah, O. H., & Alshahrani, M. A. (2025). A modified sine–cosine probability distribution: Its mathematical features with statistical modeling in sports and reliability prospects. Alexandria Engineering Journal, 121, 414–425. https://doi.org/10.1016/j.aej.2025.02.051.
Menéndez Orellana, A. E., Mendler, A., Schmid, S., & Grosse, C. U. (2025). Predictive probability of detection curves for ultrasonic testing. NDT & E International, 153, 103346. https://doi.org/10.1016/j.ndteint.2025.103346.
Morgunov, A., Karminsky, A., & Tarnovskaya, P. (2024). The development of an ESG-rating model to assess the probability of default of corporate borrowers. Procedia Computer Science, 242, 992-999. https://doi.org/10.1016/j.procs.2024.08.247.
Park, Y. I., Cho, M. S., Chang, J. S., Kim, J. S., Kim, Y. B., Lee, I. J., ... & Choi, S. H. (2024). Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer. Clinical and Translational Radiation Oncology, 45, 100734.
Radford, D. A., Maier, H. R., van Delden, H., Zecchin, A. C., & Jeanneau, A. (2024). Predicting burn probability: Dimensionality reduction strategies enable accurate and computationally efficient metamodeling. Journal of Environmental Management, 371, 123086.
Radonic, S., Besserer, J., Meier, V., Bley, C. R., & Schneider, U. (2021). A novel analytical population tumor control probability model includes cell density and volume variations: Application to canine brain tumor. International Journal of Radiation Oncology* Biology* Physics, 110(5), 1530-1537.
Shen, Y., Xiang, H., Li, J., & Chen, Z. (2025). On the development and implications of a new probabilistic model in financial and supply chain management. Alexandria Engineering Journal, 121, 402-413.
Tymińska-Czabańska, L., Janiec, P., Hawryło, P., Ślopek, J., Zielonka, A., Netzel, P., ... & Socha, J. (2024). Modeling the effect of stand and site characteristics on the probability of mistletoe infestation in Scots pine stands using remote sensing data. Forest Ecosystems, 11, 100191.
Xie, C., Yang, Y., Li, Q., Yu, X., Wang, Z., Bai, B., ... & Guo, Y. (2025). Accessing the stack effect in high-rise buildings by the probability method based on climate demarcation: A case study in China. Energy and Built Environment.
Yamashita, N., Ohnuki, Y., Iwahashi, J., & Imaya, A. (2024). National-scale mapping of soil-thickness probability in hilly and mountainous areas of Japan using legacy and modern soil survey. Geoderma, 446, 116896.
Yuan, H. (2025). A new statistical model with simulation study and reliability analysis of an audio and podcast tool. Alexandria Engineering Journal, 116, 548–560. https://doi.org/10.1016/j.aej.2024.12.044.