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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Intizar Indonesian Journal of Artificial Intelligence and Data Mining Zero : Jurnal Sains, Matematika, dan Terapan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Prosiding University Research Colloquium El-Mal: Jurnal Kajian Ekonomi & Bisnis Islam Teknologi : Jurnal Ilmiah Sistem Informasi
Laelatul Khikmah
Institut Teknologi Statistika Dan Bisnis Muhammadiyah Semarang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Physics Social Sciences

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Predicting Malaria Incidence Using LSTM and Environmental Variables Wellie Sulistijanti; Laelatul Khikmah; Erisa Adyati Rahmasari; Cikal Arbitan Putra Sangnandha; Idan Maulana Yusuf; Dzahari Alikharimah Azizah
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 2 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i2.26043

Abstract

Climate change is exacerbating malaria risk in Indonesia, especially in Papua. This study proposes a Bidirectional Long Short-Term Memory (LSTM) model to forecast malaria incidence using climate variables. The dataset comprises monthly malaria and climate records (rainfall, temperature, humidity) from four high-endemic provinces between 2014 and 2024. Key methodologies included data augmentation to address data imbalances and a grouped time-series cross-validation for robust model evaluation. An ARIMA model was implemented as a validation baseline to benchmark the proposed approach. The Bi-LSTM model delivered superior performance, achieving an average test R² of 0.7210 and SMAPE of 11.02%. the model demonstrated excellent generalization with no evidence of overfitting, significantly outperforming the ARIMA baseline. The findings validate the use of deep learning models as effective tools for public health surveillance, providing reliable early warnings to support timely interventions. Future work will apply SHAP interpretability techniques and expanding the model's geographic scope.
Co-Authors Cikal Arbitan Putra Sangnandha Dina Novitasari Dini Gartina Dzahari Alikharimah Azizah Erisa Adyati Rahmasari Faikul Fahmi Idan Maulana Yusuf Istiawan, Deden Kholishah Isnaini Muhammad Firza Ibnu Hartono Nur Hafid Ibrahim Reska Reska Santoso, Arif Gunawan Wellie Sulistijanti Wulan Septya Ningrum