Prediksi Suhu Udara Menggunakan Long Short-Term Memory (LSTM) pada Data AWOS
DOI:
https://doi.org/10.26418/justin.v14i2.99858Keywords:
Prediksi Suhu, LSTM, AWOS, PCA, meteorologiAbstract
Penelitian ini mengembangkan model prediksi suhu udara jangka pendek berbasis Long Short-Term Memory (LSTM) menggunakan data deret waktu dari Automatic Weather Observing System (AWOS) BMKG Juanda. Pemanfaatan data operasional AWOS yang bersifat kontinu dan real-time memberikan peluang untuk meningkatkan akurasi peramalan suhu pada lingkungan bandara yang memiliki dinamika cuaca tinggi. Penelitian ini membandingkan dua pendekatan pemodelan, yaitu LSTM murni dan LSTM dengan seleksi fitur berbasis Principal Component Analysis (PCA), untuk mengevaluasi pengaruh reduksi dimensi terhadap performa prediksi suhu udara. Dataset yang digunakan merupakan data observasi meteorologi tahun 2022 dengan interval 30 menit. Hasil evaluasi pada data pengujian menunjukkan bahwa kedua model menghasilkan kinerja yang sangat baik dengan nilai MAE sekitar 0,31 °C. Model LSTM + PCA menghasilkan MAE sebesar 0,3171 °C, sedangkan LSTM murni sebesar 0,3178 °C, menunjukkan bahwa perbedaan akurasi kedua pendekatan relatif kecil. Meskipun tidak meningkatkan akurasi secara signifikan, penerapan PCA mampu mengurangi kompleksitas fitur dan meningkatkan efisiensi komputasi pada proses pelatihan model. Kontribusi penelitian ini terletak pada pemanfaatan data AWOS operasional nyata, penerapan fitur meteorologi multivariat dalam pemodelan LSTM, serta evaluasi pengaruh seleksi fitur berbasis PCA melalui perbandingan performa antara model LSTM murni dan LSTM+PCA dalam prediksi suhu udara jangka pendek di lingkungan bandara. Hasil penelitian menunjukkan bahwa arsitektur LSTM efektif untuk pemodelan deret waktu meteorologi berbasis AWOS dan berpotensi mendukung sistem peringatan dini dalam operasional penerbangan.References
A. Kristianto et al., “Pemanfaatan Data Pengamatan Cuaca Berbasis Data Penginderaan Jauh dan Model Cuaca Numerik Bencana Hidrometeorologi,” J. Geol. Edukasi dan Lingkung., vol. 2, no. 2, pp. 87–96, 2018.
C. Adaptation and S. Factsheets, “Climate Adaptation Synthesis Factsheets Aviation and Temperature Change Climate Adaptation Synthesis Factsheets Aviation and Temperature Change,” pp. 1–2.
M. Kamran et al., “Spectrum of Engineering Sciences INTEGRATING ADVANCED DEEP LEARNING ALGORITHMS FOR CLIMATE SYSTEMS : ENHANCING WEATHER FORECAST ACCURACY , REAL-TIME CLIMATE MONITORING , AND LONG-TERM CLIMATE PREDICTIONS Spectrum of Engineering Sciences,” vol. 3138, pp. 365–403, 2025.
O. A. Wani et al., “Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas,” Sci. Rep., vol. 14, no. 1, 2024, doi: 10.1038/s41598-024-77687-x.
B. Ghojogh and A. Ghodsi, “Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey,” 2023, [Online]. Available: http://arxiv.org/abs/2304.11461
E. Supriyadi, “Prediksi Parameter Cuaca Menggunakan Deep Learning Long-Short Term Memory (LSTM) Weather Parameters Prediction Using Deep Learning Long-Short Term Memory (LSTM),” J. Meteorol. Dan Geofis., vol. 21, no. 21, pp. 56–59, 2019, [Online]. Available: http://bmkgsoft.database.bmkg.go.id.
J. Anjani, H. P. Agustintjahyaningtijas, I. G. P. Asto Buditjahjanto, and L. Anifah, “Prediction of Air Temperature on Runway 10 Juanda Airport Using Hybrid LSTM,” Indones. J. Electr. Electron. Eng., vol. 7, no. 2, pp. 50–58, 2024, [Online]. Available: https://doi.org/10.26740/inajeee.v7n250
D. Jansen, T. Handhayani, and J. Hendryli, “Penerapan Metode Long Short-Term Memory Dalam Memprediksi Data Meteorologi Di Kalimantan Timur,” Simtek J. Sist. Inf. dan Tek. Komput., vol. 8, no. 2, pp. 348–352, 2023, doi: 10.51876/simtek.v8i2.202.
S. Anwar, “Modifikasi Pembobotan Systemic Important Score dengan Principal Component Analysis,” J. Ekon. dan Pembang. Indones., vol. 18, no. 2, pp. 128–151, 2018, doi: 10.21002/jepi.2018.08.
L. Jiang, Y. Wang, W. Zheng, C. Jin, Z. Li, and S. G. Teo, “LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data,” 2022, [Online]. Available: http://arxiv.org/abs/2203.04305
Jason Brownlee, “Deep Learning for Time Series Forecasting: Predict the Future with MLPs ... - Jason Brownlee - Google Books,” 2018, [Online]. Available: https://books.google.com.ng/books/about/Deep_Learning_for_Time_Series_Forecastin.html?id=o5qnDwAAQBAJ&redir_esc=y
M. Rizki, S. Basuki, and Y. Azhar, “Implementasi Deep Learning Menggunakan Arsitektur Long Short Term Memory(LSTM) Untuk Prediksi Curah Hujan Kota Malang,” J. Repos., vol. 2, no. 3, pp. 331–338, 2020, doi: 10.22219/repositor.v2i3.470.
D. Thesis, “Application of Hybrid Methods Combining Recurrent Neural Networks and Modified Metaheuristics for Time-Series Forecasting,” 2024.
E. Verianto, “Penerapan LSTM Dengan Regularisasi Untuk Mencegah Overfitting Pada Model Prediksi Tingkat Inflasi di Indonesia,” Simkom, vol. 9, no. 2, pp. 195–204, 2024, doi: 10.51717/simkom.v9i2.460.
J. Ul Rahman, R. Zulfiqar, and A. Khan, “SwishReLU: A Unified Approach to Activation Functions for Enhanced Deep Neural Networks Performance,” 2024.
Nurfalinda, M. Al Fiani, and M. R. Rathomi, “Prediksi Temperatur Maksimum di Kota Tanjungpinang Menggunakan Model CNN-LSTM,” Komputa J. Ilm. Komput. dan Inform., vol. 14, no. 1, pp. 129–138, 2025, doi: 10.34010/komputa.v14i1.15377.
M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Comput. Appl., vol. 35, no. 23, pp. 17095–17112, 2023, doi: 10.1007/s00521-023-08568-z.
I. Ardhanur, M. Martanto, A. R. Dikananda, and M. Mulyawan, “Analisis Prediksi Penjualan Tisu Menggunakan Regresi Linear,” J. Inform. dan Tek. Elektro Terap., vol. 13, no. 2, pp. 739–746, 2025, doi: 10.23960/jitet.v13i2.6310.
I. M. Andika Surya, T. A. Cahyanto, and L. A. Muharom, “Deep Learning dengan Teknik Early Stopping untuk Mendeteksi Malware pada Perangkat IoT,” J. Teknol. Inf. dan Ilmu Komput., vol. 12, no. 1, pp. 21–30, 2025, doi: 10.25126/jtiik.2025128267.
F. Pranandasya, A. Pratama, D. A. Sulistyo, and F. S. Mukti, “Peningkatan Akurasi Deteksi Intrusi Jaringan dengan Model Hybrid Convolutional Neural Network dan Long Short-Term Memory,” vol. 10, pp. 528–539, 2025.
T. R. Mahesh et al., “Transformative Breast Cancer Diagnosis using CNNs with Optimized ReduceLROnPlateau and Early Stopping Enhancements,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, 2024, doi: 10.1007/s44196-023-00397-1.
T. Antonius, D. Kurniawan, A. Setiawan, F. Tita, and J. Tengah, “Perbandingan Kinerja Metode Support Vector Regression dan Metode Regresi Linier Berganda dalam Memprediksi BMI pada Dataset ASTHMA,” vol. 8, no. 2, pp. 133–142, 2025.
L. Q. Rosydah and T. Widiyaningtyas, “Perbandingan Cosine Similarity dan Mean Squared Difference dalam Rekomendasi Buku Fiksi berbasis Item,” Edumatic J. Pendidik. Inform., vol. 8, no. 2, pp. 565–574, 2024, doi: 10.29408/edumatic.v8i2.27783.
R. Tanjung, A. Listiani, and F. Lestari, “Prediksi Multivariate Time Series Parameter Cuaca Menggunakan Long Short - Term Memory (LSTM),” Pros. Semin. Nas. Sains Data, vol. 4, no. 1, pp. 445–456, 2024, doi: 10.33005/senada.v4i1.253.
BMKG. Automatic Weather Observing System (AWOS). [Online] Tersedia: https://www.bmkg.go.id
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