Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN)

A. A.A.W. Kusuma; S. Mardiyati; D. Lestari; A. H. Zili; S. Devila

doi:10.1063/5.0010446

Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN)

A. A.A.W. Kusuma, S. Mardiyati, D. Lestari, A. H. Zili, S. Devila

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this modern era, people become aware and use insurance as part of their long-term financial planning in terms of protecting against unpredictable risk. Health insurance is one type of insurance that can be used to help people in alleviating the costs of treatment of the disease they suffered. Some insurance companies offer insurance products that finance the treatment of Tuberculosis (TB), and one of the references used to calculate premiums is TB morbidity. A model for predicting TB morbidity is needed so that premium calculations can be carried out properly. This study aims to predict the TB morbidity rate in Indonesia using the Temporal Convolutional Neural Network (TCNN) method. The results of the model validation were measured by using the Mean Absolute Percentage Error (MAPE) value, where the model produced in this study has a score below 10 %. The model that obtained in this study is then used to forecast TB morbidity rate in Indonesia from 2019 to 2021.

Original language	English
Title of host publication	Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
Editors	Terry Mart, Djoko Triyono, Tribidasari Anggraningrum Ivandini
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735420014
DOIs	https://doi.org/10.1063/5.0010446
Publication status	Published - 1 Jun 2020
Event	5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 - Depok, Indonesia Duration: 9 Jul 2019 → 10 Jul 2019

Publication series

Name	AIP Conference Proceedings
Volume	2242
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
Country/Territory	Indonesia
City	Depok
Period	9/07/19 → 10/07/19

Keywords

CNN
deep learning
forecasting
Morbidity rate
tuberculosis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1063/5.0010446

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Kusuma, A. A. A. W., Mardiyati, S., Lestari, D., Zili, A. H., & Devila, S. (2020). Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN). In T. Mart, D. Triyono, & T. A. Ivandini (Eds.), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 Article 030018 (AIP Conference Proceedings; Vol. 2242). American Institute of Physics Inc.. https://doi.org/10.1063/5.0010446

Kusuma, A. A.A.W. ; Mardiyati, S. ; Lestari, D. et al. / Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN). Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019. editor / Terry Mart ; Djoko Triyono ; Tribidasari Anggraningrum Ivandini. American Institute of Physics Inc., 2020. (AIP Conference Proceedings).

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title = "Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN)",

abstract = "In this modern era, people become aware and use insurance as part of their long-term financial planning in terms of protecting against unpredictable risk. Health insurance is one type of insurance that can be used to help people in alleviating the costs of treatment of the disease they suffered. Some insurance companies offer insurance products that finance the treatment of Tuberculosis (TB), and one of the references used to calculate premiums is TB morbidity. A model for predicting TB morbidity is needed so that premium calculations can be carried out properly. This study aims to predict the TB morbidity rate in Indonesia using the Temporal Convolutional Neural Network (TCNN) method. The results of the model validation were measured by using the Mean Absolute Percentage Error (MAPE) value, where the model produced in this study has a score below 10 %. The model that obtained in this study is then used to forecast TB morbidity rate in Indonesia from 2019 to 2021.",

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author = "Kusuma, {A. A.A.W.} and S. Mardiyati and D. Lestari and Zili, {A. H.} and S. Devila",

note = "Funding Information: The authors would like to thank Universitas Indonesia for fully funded this research via HIBAH PITTA 2019. Publisher Copyright: {\textcopyright} 2020 Author(s).; 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019 ; Conference date: 09-07-2019 Through 10-07-2019",

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Kusuma, AAAW, Mardiyati, S, Lestari, D , Zili, AH & Devila, S 2020, Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN). in T Mart, D Triyono & TA Ivandini (eds), Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019., 030018, AIP Conference Proceedings, vol. 2242, American Institute of Physics Inc., 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019, Depok, Indonesia, 9/07/19. https://doi.org/10.1063/5.0010446

Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN). / Kusuma, A. A.A.W.; Mardiyati, S.; Lestari, D. et al.
Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019. ed. / Terry Mart; Djoko Triyono; Tribidasari Anggraningrum Ivandini. American Institute of Physics Inc., 2020. 030018 (AIP Conference Proceedings; Vol. 2242).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kusuma, A. A.A.W.

AU - Mardiyati, S.

AU - Lestari, D.

AU - Zili, A. H.

AU - Devila, S.

PY - 2020/6/1

Y1 - 2020/6/1

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AB - In this modern era, people become aware and use insurance as part of their long-term financial planning in terms of protecting against unpredictable risk. Health insurance is one type of insurance that can be used to help people in alleviating the costs of treatment of the disease they suffered. Some insurance companies offer insurance products that finance the treatment of Tuberculosis (TB), and one of the references used to calculate premiums is TB morbidity. A model for predicting TB morbidity is needed so that premium calculations can be carried out properly. This study aims to predict the TB morbidity rate in Indonesia using the Temporal Convolutional Neural Network (TCNN) method. The results of the model validation were measured by using the Mean Absolute Percentage Error (MAPE) value, where the model produced in this study has a score below 10 %. The model that obtained in this study is then used to forecast TB morbidity rate in Indonesia from 2019 to 2021.

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Kusuma AAAW, Mardiyati S, Lestari D , Zili AH , Devila S. Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN). In Mart T, Triyono D, Ivandini TA, editors, Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019. American Institute of Physics Inc. 2020. 030018. (AIP Conference Proceedings). doi: 10.1063/5.0010446

Forecasting the tuberculosis morbidity rate in Indonesia using temporal convolutional neural network (TCNN)

Abstract

Publication series

Conference

Keywords

UN SDGs

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