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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Edriani, Tiara Shofi
Institut Teknologi Sumatera
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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Data-Efficient LSTM Modeling for Climate-based Dengue Early Warning in Lampung, Indonesia Fauzi, Rifky; Sinaga, Mia Syntia Br; Rizka, Nela; Noor, Dear Michiko Mutiara; Pribadi, Aswan Anggun; Edriani, Tiara Shofi
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.v9i2.26192

Abstract

We present a data-efficient recurrent framework for climate-informed dengue early warning in Lampung Province. Monthly incidence and climate records are transformed into supervised sequences with 2–3-month lags, consistent with the observed lead–lag structure. Three architectures i.e. single-layer LSTM, stacked LSTM, and Temporal-Attention LSTM (TA-LSTM) are tuned via a compact genetic search under a time-ordered split. Performance improves with longer history; the TA-LSTM (37 units) attains the best accuracy. Permutation feature importance reveals a clear hierarchy: relative humidity and maximum temperature dominate, autoregressive incidence contributes moderately, while rainfall, sunshine, and minimum temperature are secondary; average temperature is largely redundant. The findings indicate that adding meaningful historical context and selective temporal weighting yields robust early-warning capability from coarse, time-limited data, and that humidity–temperature dynamics, together with short-term incidence persistence, are the principal drivers in this provincial setting.
Co-Authors Noor, Dear Michiko Mutiara Pribadi, Aswan Anggun Rifky Fauzi Rizka, Nela Sinaga, Mia Syntia Br