Arisanti, R., Notodiputro, K. A., Sadik, K., & Lim, A. (2017, March). Bias Reduction in Estimating Variance Components of Phytoplankton Existence at Na Thap River Based on Logistics Linear Mixed Models. In IOP Conference Series: Earth and Environmental Science (Vol. 58, No. 1, p. 012014). IOP Publishing.

Arisanti, R., Sumertajaya, I.M., Notodiputro, K.A., and Indahwati. (2020). Firth Bias Correction for Estimating Variance Components of Logistics Linear Mixed Model using Penalized Quasi Likelihood Method, Communication in Mathe-matical Biology and Neuroscience, 1–15.
Batta, M. (2018). Machine Learning Algorithms - A Review. International Journal of Science and Research, 18(8), 381–386.
Bolker, B.M. (2015). Ecological Statistics: Contemporary theory and application. Oxford University Press.
Budi, I.S., Ardillah, Y., Sari, I.P., & Septiawati, D. (2018). Analisis Faktor Risiko Kejadian penyakit Tuberculosis Bagi Masyarakat Daerah Kumuh Kota Palembang, Jurnal Kesehatan Lingkungan Indonesia, 17(2).
Caraka, R. E., Chen, R. C., Toharudin, T., Pardamean, B., Yasin, H., & Wu, S. H. (2019). Prediction of status particulate matter 2.5 using state Markov chain stochastic process and HYBRID VAR-NN-PSO. IEEE Access, 7, 161654-161665.
Carter, D.J., Glaziou, P., Lonnroth, K., Siroka, A., Floyd, K., Weil, D., Raviglione, M., Houben, R.M.G.J., Boccia, D. (2018). The impact of social protection and poverty elimination on global tuberculosis incidence: a statistical model-ling analysis of Sustainable Development Goal 1, Lancet Global Health, 6(5), 514–522.
Charbuty, B., & Abdulazeez, A.M. (2021). Classification Based on Decision Tree Algorithm for Machine Learning, Jour-nal of Applied Science and Technology Trends, 2(01), 20–28.
Endharta, A. J. (2009). Short term electricity load demand forecasting in Indonesia by using double seasonal recurrent neural networks. International Journal of Mathematical Models and Methods in Applied Sciences, 3(3), 171-178.
Fausset, L. (1994). Fundamental of Neural Networks: Architectures, Algorithms, and Applications, New Jersey: Prentice-Hall.
Liu, S., & Sun, W. (2023). Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast fur-nace gas generation, ScienceDirect: Energy, 262(A).
McCulloch, W. S., & Pits, W. H. (1943). Bulletin of Mathematical Biophysics, 5, 115-133Widjanarko, B., Gompelman, M., Dijkers, M., and van der Werf, M.J. (2009). Factors that influence treatment adherence of tuberculosis patients liv-ing in Java, Indonesia, Patient Prefer. Adherence, 3, 231–238.
McCulloch, C.E. (1997). Maximum Likelihood Algorithms for Generalized Linear Mixed Models, Journal of American Statistical Association, 92(437), 162–170.
McCulloch, C.E. (2000). An Introduction to Generalized Linear Mixed Models, Journal of the american Statistical Asso-ciation, 95(452).
Pangaribuan, L., Kristina, K., Perwitasari, D., Tejayanti, T., & Lolong, D. B. (2020). Faktor-Faktor yang Mempengaruhi Kejadian Tuberkulosis pada Umur 15 Tahun ke Atas di Indonesia, Buletin Penelitian Sistem Kesehatan, 23(1), 10–17.
Pangestika, D. (2020). West Java extends PSBB in Jakarta's satellite cities until July 2, The Jakarta Post.
Patan, K. (2019). Neural Networks. In Studies in Systems, Decision and Control, Switzerland.
Pontoh, R. S., Toharudin, T., Zahroh, S., & Supartini E. (2020). Effectiveness of the public health measures to prevent the spread of covid-19. Commun. Math. Biol. Neurosci., Article-ID.
Prihanti, G.S., Sulistiyawati, & Rahmawati, I. (2015). Analisis faktor kejadian tuberkulosis paru, Jurnal Ilmu Kesehatan dan Kedokteran Keluarga, 11(2).
Ronald, R.D., Marais, B.J., & Clifton, E.B. (2010). Age and the epidemiology and pathogenesis of tuberculosis, The Lan-cet, 375(9729), 1852-4.
Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Washington D.C: Spartan.
Sastri, R., & Setiadi, Y. (2018). Generalized Linear Mixed Model Untuk Data Kematian Bayi di Indonesia. Jakarta: STIS.
Suhermi, N., Permata, R.P., & Rahayu, S.P. (2019). Forecasting the Search Trend of Muslim Clothing in Indonesia on Google Trends Data Using ARIMAX and Neural Network. In Proceedings of the Communications in Computer and In-formation Science, Iizuka, Japan.
Sulistyawati, S., & Ramadhan, A. W. (2021). Risk Factors for Tuberculosis in an Urban Setting in Indonesia: A Casecon-trol Study in Umbulharjo I, Yogyakarta, Journal of University of Occupational and Environmental Health, 43(2), 165–171.
Stroup, W.W. (2012). Generalized Linear Mixed Models: Modern Concepts, CRC Press: A Chapman & Hall Book.
Utami, T.W. (2013). Analisis regresi binomial negatif untuk mengatasi overdispersion regresi poisson pada kasus demam berdarah dengue, Jurnal Statistika., 1(2), 59–65.
Velavan, T. P., & Meyer, C. G. (2020). The COVID-19 epidemic, Trop. Med. Int. Health, 25, 278–280.
Warsito, B., Yasin, H., & Prahutama, A. (2019). Particle Swarm Optimization to Obtain Weights in Neural Network, 35.
Widjanarko, B., Gompelman, M., Dijkers, M., & van der Werf, M.J. (2009). Factors that influence treatment adherence of tuberculosis patients living in Java, Indonesia, Patient Prefer. Adherence, 3, 231–238.
Yasin, H., Warsito, B., Santoso, R., & Suparti, S. (2018). Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based. In Proceedings of the E3S Web of Conferences, Semarang, Indonesia, 73, 13008.
Yirga A.A, Melesse, S.F., Mwambi, H.G., & Ayele, D.G. (2020). Negative binomial mixed models for analyzing longitu-dinal CD4 count data, Nature Journal., 10(1), 1–15.
Zhang, X., Mallick, H., Tang, Z., Zhang, L., Cui, X., Benson, A.K., & Yi, N. (2017). Negative binomial mixed models for analyzing microbiome count data, BMC Bioinformatics, 18(1), 1–10.
Zulaikha, E. (2018). Pemetaan dan Analisis Faktor-Faktor Yang Mempengaruhi Tuberkulosis Menggunakan Geograph-ically Weighted.