Statistical Learning for Predicting Dengue Fever Rate in Surabaya


  • Siana Halim Industrial Engineering Department Petra Christian University
  • Felecia Felecia Industrial Engineering Department Petra Christian University
  • Tanti Octavia Industrial Engineering Department Petra Christian University



Global Moran I statistics, Local Moran I statistics, Regression, Spatial Regression, Geographically Weighted Regression


Dengue fever happening most in tropical countries and considered as the fastest spreading mosquito-borne disease which is endemic and estimated to have 96 million cases annually. It is transmitted by Aedes mosquito which infected with a dengue virus. Therefore, predicting the dengue fever rate as become the subject of researches in many tropical countries. Some of them use statistical and machine learning approach to predict the rate of the disease so that the government can prevent that incident. In this study, we explore many models in the statistical learning approaches for predicting the dengue fever rate. We applied several methods in the predictive statistics such as regression, spatial regression, geographically weighted regression and robust geographically weighted regression to predict the dengue fever rate in Surabaya. We then analyse the results, compare them based on the mean square error. Those four models are chosen, to show the global estimator’s approaches, e.g. regression, and the local ones, e.g. geographically weighted regression. The model with the minimum mean square error is regarded as the most suitable model in the statistical learning area for solving the problem. Here, we look at the estimates of the dengue fever rate in the year 2012, to 2017, area, poverty percen­tage, precipitation, number of rainy days for predicting the dengue fever outbreak in the year 2018. In this study, the pattern of the predicted model can follow the pattern of the true dataset.


Bhatt, S., Gething, P.W., Brady, O.J., Messina, J.P., Farlow, A.W., Moyes, C.L., Drake, J.M., Brownstein, J.S., Hoen, A.G., Sankoh, O., Myers, M.F., George D.B., Jaenisch, T., Wint, G.R., Simmons, C.P., Scott, T.W., Farrar, J.J., and Hay, S.I., The Global Distribution and Burden of Dengue, Nature, 496(7446), 2013, pp. 504–507.

Haryanto, B., Indonesia Dengue Fever: Status, Vulnerability, and Challenges. IntechOpen: Current Topics in Tropical Emerging Diseases and Travel Medicine, 2018.

Karyanti, M.R., Uiterwaal, C.S., Kusriastuti, R., Hadinegoro, S.R., Rovers, M.M., Heesterbeek, H., Hoes, A.W., Buijning-Verhagen, P., The Changing Incidence of Dengue Haemorrhagic Fever in Indonesia: A 45-year Registry-Based Analysis, BMC Infectious Diseases, 14, 2014, p.412

Herath, P.H.M.N., Perera, A.A.I., and Wijekoon, H.P., Prediction of Dengue Outbreaks in Srilanka using Artificial Neural Network, International Journal of Computer Applications, 101,2014, pp. 1-5.

Jongmuenwai, B., Lowanichchai, S., and Jabjone, S., Prediction Model of Dengue Hemorrhagic Fever Outbreak using Artificial Neural Networks in Northeast of Thailand, International Journal of Pure and Applied Mathematics, 118(8), 2018, pp3407-3417.

Laureano-Rosario, A.E., Duncan, A.P., Mendez-Lazaro, P.A., Garcia-Rejon, J.E., Gomez-Carro, S., Farfan-Ale, J., Savic, D.A., and Muller-Karger, F.E., Application of Artificial Neural Networks for Dengue Fever Outbreak Predictions in the Northwest Coast of Yucatan, Mexico and San Juan, Puerto Rico, Tropical Medicine Infectious Disease, 3(5), 2018, pp. 1-16.

Mahdiana, D., Winarko, E., Ashari, A., and Kusnanto, H., A Model for Forecasting the Number of Cases and Distribution Pattern of Dengue Hemorrhagic Fever in Indonesia, International Journal of Advanced Computer Science and Applications, 8(11), 2017, pp. 143-150.

Tang, S.C.N., Rusli, M., and Lestasi, P., Climate Variability and Dengue Hemorrhagic Fever in Surabaya, East Java, Indonesia, Preprint, 2018, Retrieved from: ppr/ppr71486

Ramadona, A.L, Lazuardi, L. Hii, Y.L., Holmner, Å, Kusnanto, H., and Rocklöv, J., Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data, Plos One, 11(3), 2016, pp. 1-18.

Widyaningrum, R., Partiwi, S., Rahman, A., and Sudiarno, A., Prediction of Dengue Fever Epidemic Spreading using Dynamics Transmission Vector Model, Indonesia Journal of Tropical and Infectious Disease, 5(2), 2014, pp. 41-48.

Nuryunarsih, D., Sociodemographic Factors to Dengue Hemmorrhagic Fever Case in Indonesia, Kesmas: National Public Health Journal, 10(1), 2015, pp. 10-16.

Halim, S., Octavia, T., Felecia, and Handojo, A. Dengue Fever Outbreak Prediction in Surabaya using a Geographically Weighted Regression, Times-Icon Proceeding, Thailand Dec, 2019.

Kassambara, A., Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning, STHDA,2017,

Kaufman L., and Rousseeuw, P.J., Finding Group in Data: An Introduction to Cluster Ana¬ly-sis, Wiley, New York, 1990.

Anselin, L., Local Indicators of Spatial Asso¬cia-tion-LISA, Geographical Analysis, 27, 1995, pp.930115.

ver Hoef, J.M., Peterson, E.E., Hooten, M.B., Hanks, E.M., and Fortin, M.J., Spatial Auto¬re-gressive Models for Statistical Inference from Ecological Data, Ecological Society of America, 88(1), 2018, pp. 36-59.

Fotheringham, A.S., Brunsdon, C.C., and Charlton, M.E., Geographically Weighted Regres-sion: The Analysis of Spatially Varying Relation-ships, Wiley, Chichester, 2002.

Gollini, I., Lu, B., Charlton, M., Brunsdon, C., and Harris, P., GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models, Journal of Statistical Soft¬ware, 63(17), 2015, pp. 1-50

Permenkes, Peraturan Menteri Kesehatan Republik Indonesia No. 75 Tahun 2014 tentang Pusat Kesehatan Masyarakat.

Kompas, Gerakan Satu Rumah Satu Jumantik, Lankah Risma agar Surabaya Bebas DBD, Kompas, 11 November 2011. Retrieved from: gerakan-1-rumah-1-jumantik-langkah-risma-agar-surabaya-bebas-dbd.

BPS, Surabaya dalam Angka, Biro Pusat Statistika Surabaya, 2018, retrieved from

Rousseeuw, P. J., Silhouttes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathe-matics, 20, 1987, pp. 53-65.




How to Cite

Halim, S., Felecia, F., & Octavia, T. (2020). Statistical Learning for Predicting Dengue Fever Rate in Surabaya. Jurnal Teknik Industri, 22(1), 37-46.




Most read articles by the same author(s)

1 2 3 > >>