Pengembangan Aplikasi Sistem Prediksi Kasus Demam Berdarah Dengue Berdasarkan Curah Hujan Berbasis Web
DOI:
https://doi.org/10.35889/progresif.v22i3.4065Keywords:
Demam Berdarah Dengue, Curah Hujan, LSTM, Prediksi, Aplikasi WebAbstract
Dengue Hemorrhagic Fever (DHF) is an infectious disease that poses a serious public health problem in tropical regions, with its spread strongly influenced by rainfall; however, current surveillance systems remain manual and reactive, often delaying intervention. This study aims to design and implement a rainfall-based DHF case prediction system using a Long Short-Term Memory (LSTM) algorithm tailored to each sub-district. The system was developed using the Prototyping method and integrated into a web application, with Laravel as the main interface and Flask as the prediction server. Testing was conducted through model accuracy evaluation using MAE, RMSE, and MAPE metrics compared against the ARIMA model, along with Black Box Testing of system functionality. Results show that LSTM outperformed ARIMA in 9 of 11 sub-districts (81.8%), and all Black Box test scenarios ran as designed, confirming that the system can proactively support local health decision-making.
Keywords: dengue hemorrhagic fever; rainfall; LSTM; prediction; web application
Abstrak
Demam Berdarah Dengue (DBD) merupakan penyakit menular yang menjadi permasalahan kesehatan masyarakat serius di wilayah tropis, dengan penyebaran yang sangat dipengaruhi oleh curah hujan; namun sistem surveilans yang berjalan saat ini masih bersifat manual dan reaktif sehingga intervensi sering terlambat. Penelitian ini bertujuan merancang dan mengimplementasikan sistem prediksi kasus DBD berbasis curah hujan menggunakan algoritma Long Short-Term Memory (LSTM) yang spesifik untuk masing-masing kecamatan. Sistem dikembangkan menggunakan metode Prototyping dan diintegrasikan ke dalam aplikasi web dengan arsitektur Laravel sebagai antarmuka utama dan Flask sebagai peladen prediksi. Pengujian dilakukan melalui evaluasi akurasi model menggunakan metrik MAE, RMSE, dan MAPE yang dibandingkan dengan model ARIMA, serta pengujian Black Box terhadap fungsionalitas sistem. Hasil pengujian menunjukkan LSTM unggul pada 9 dari 11 kecamatan (81,8%) dibanding ARIMA, dan seluruh skenario Black Box berjalan sesuai rancangan, mengonfirmasi bahwa sistem mampu mendukung pengambilan keputusan kesehatan secara proaktif di tingkat kecamatan.
References
[1] R. Abdallah, S. Abdelgaber, and H. A. Sayed, "Leveraging AHP and transfer learning in machine learning for improved prediction of infectious disease outbreaks," Scientific Reports, vol. 14, no. 1, art. no. 81367, 2024.
[2] A. Arwansyah et al., "Model Prediksi Deret Waktu Menggunakan Deep Convolutional LSTM," SISITI Seminar Ilmiah Sistem Informasi Dan Teknologi Informasi, vol. 13, no. 2, pp. 21–25, 2024.
[3] K. Azhar, R. Marina, and A. Anwar, "A prediction model of Dengue incidence using climate variability in Denpasar city," Health Science Journal of Indonesia, vol. 8, no. 2, pp. 68–73, 2017.
[4] BMKG, "Informasi Peringatan Dini DBD Berbasis Iklim - Berita Utama," BMKG, 2020. [Online]. Tersedia: https://www.bmkg.go.id/berita/utama/informasi-peringatan-dini-dbd-berbasis-iklim.
[5] J. Brownlee, Deep learning for Time Series Forecasting. Machine learning Mastery, 2020.
[6] X. Chen and P. Moraga, "Forecasting dengue across Brazil with LSTM neural networks and SHAP-driven lagged climate and spatial effects," BMC Public Health, vol. 25, no. 1, p. 973, 2025.
[7] S. T. da Silva et al., "When climate variables improve the dengue forecasting: a machine learning approach," European Physical Journal: Special Topics, vol. 234, no. 3, pp. 555–569, 2025.
[8] Docker, "Manuals | Docker Docs," 2024. [Online]. Tersedia: https://docs.docker.com/manuals/.
[9] European Centre for Disease Prevention and Control, "Aedes aegypti - Factsheet for experts," 2017. [Online]. Tersedia: https://www.ecdc.europa.eu/en/disease-vectors/facts/mosquito-factsheets/aedes-aegypti.
[10] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016.
[11] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[12] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. OTexts, 2021. [Online]. Tersedia: https://otexts.com/fpp3/.
[13] Kementerian Kesehatan Republik Indonesia, "Peraturan Menteri Kesehatan Nomor 50 Tahun 2017 Tentang Standar Baku Mutu Kesehatan Lingkungan dan Persyaratan Kesehatan untuk Vektor dan Binatang Pembawa Penyakit Serta Pengendaliannya," 2017.
[14] Kementerian Kesehatan Republik Indonesia, "Pedoman Pencegahan dan Pengendalian Demam Berdarah Dengue di Indonesia," 2024.
[15] Laravel, "Laravel 11.x - The clean stack for Artisans and agents," 2024. [Online]. Tersedia: https://laravel.com/docs/11.x/releases.
[16] X. Lu et al., "Application of multiple linear regression model and Long Short-Term Memory with compartmental model to forecast dengue cases in Selangor, Malaysia based on climate variables," Infectious Disease Modelling, vol. 10, no. 1, pp. 240–256, 2025.
[17] M. A. Majeed et al., "Prediction of dengue cases using the attention-based Long Short-Term Memory (LSTM) approach," Geospatial Health, vol. 18, no. 1, art. no. 1176, 2023.
[18] Mamenun et al., "Spatiotemporal Characterization of Dengue Incidence and Its Correlation to Climate Parameters in Indonesia," Insects, vol. 15, no. 5, p. 366, 2024.
[19] V. Monita, S. Raniprima, and N. Cahyadi, "Comparative Analysis of Daily and Weekly Heavy Rain Prediction Using LSTM and Cloud Data," Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), vol. 10, no. 4, pp. 833–842, 2024.
[20] E. Mussumeci and F. Codeço Coelho, "Large-scale multivariate forecasting models for Dengue - LSTM versus Random Forest regression," Spatial and Spatio-Temporal Epidemiology, vol. 35, art. no. 100372, 2020.
[21] M. Nasir et al., "Machine learning Approach to Predict the Dengue Cases Based on Climate Factors," Window of Health : Jurnal Kesehatan, vol. 7, no. 2, pp. 203–214, 2024.
[22] J. Noble, "What are ARIMA Models? | IBM," IBM. [Online]. Tersedia: https://www.ibm.com/think/topics/arima-model.
[23] Oracle, "MySQL :: MySQL Documentation," 2024. [Online]. Tersedia: https://dev.mysql.com/doc/.
[24] Pallets, "Welcome to Flask — Flask Documentation (3.1.x)," 2024. [Online]. Tersedia: https://flask.palletsprojects.com/en/stable/.
[25] R. S. Pressman and B. R. Maxim, Software Engineering: A Practitioner’s Approach, 9th ed. McGraw-Hill, 2020.
[26] R. S. Sari and P. Sandika, "Prediksi Penyebaran Kasus DBD dengan Pendekatan LSTM di Kota Bangko, Kabupaten Merangin," Impression : Jurnal Teknologi Dan Informasi, vol. 3, no. 3, pp. 164–173, 2024.
[27] A. Sebastianelli et al., "A reproducible ensemble machine learning approach to forecast dengue outbreaks," Scientific Reports, vol. 14, no. 1, art. no. 7241, 2024.
[28] TensorFlow, "tf.keras.Model | TensorFlow v2.16.1," 2024. [Online]. Tersedia: https://www.tensorflow.org/api_docs/python/tf/keras/Model.
[29] N. Tian et al., "Precision Prediction for Dengue Fever in Singapore: A Machine learning Approach Incorporating Meteorological Data," Tropical Medicine and Infectious Disease, vol. 9, no. 4, art. no. 84, 2024.
[30] F. Ulva and N. Hikmi, "Analisis Spasial Kejadian Demam Berdarah di Kota Padang Tahun 2020-2022," JIK JURNAL ILMU KESEHATAN, vol. 8, no. 1, pp. 152–159, 2024.
[31] World Health Organization, Dengue Guidelines for Diagnosis, Treatment, Prevention and Control, 2009.
[32] Y. Yulian et al., "Hubungan Curah Hujan Dan Suhu Udara Dengan Kejadian Demam Berdarah Dengue Di Kabupaten Tulang Bawang Barat," Jurnal Medika Malahayati, vol. 9, no. 2, pp. 225–235, 2025.
