Penerapan Metode Monte Carlo dalam Memprediksi Suhu Daerah Perkotaan
-
Tulus Joseph Marpaung
*
Universitas Sumatera Utara, Medan,
Indonesia
- Rony Genevent Marpaung Universitas Sumatera Utara, Medan, Indonesia (*) Corresponding Author
Abstract
Changes in temperature patterns due to climate change are a global challenge that requires in-depth analysis, especially in tropical regions such as the city of Medan, Indonesia. This research aims to project future temperature patterns using the Monte Carlo simulation method, utilizing historical data on daily average temperatures from the Meteorology, Climatology and Geophysics Agency (BMKG). A probability-based Monte Carlo method is used to analyze the future temperature distribution, applying the normal distribution as the basic model. Parameters such as mean and standard deviation are calculated accurately, and thousands of iterations are performed to ensure stable and representative simulation results. The analysis process is carried out using Python and supporting libraries such as NumPy, SciPy, and Matplotlib, which provide flexibility and efficiency in environmental data processing. The results of this study show that the Monte Carlo method can produce future temperature distributions that reflect daily temperature variations as well as the probability of extreme events. These predictions provide important insights for various sectors, including health, energy and urban planning, in developing strategic plans to deal with the impacts of climate change. This research confirms that Monte Carlo simulation is an effective approach for analyzing climate data in tropical regions. Additionally, this research opens up opportunities for further development, such as integrating additional data and adapting the model to different environmental scenarios to improve prediction accuracy and relevance of results.
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References
S. M. Rohmah, Rachmaniyah, Rusmiati, Khambali, and I. Sulistio, “Kondisi Kualitas Udara dan Keluhan Kesehatan Masyarakat Akibat Paparan Gas Amonia Pada Lokasi Lumpur Lapindo,” J. Sanitasi Lingkung., vol. 3, no. 2, pp. 53–60, 2023, doi: https://doi.org/10.36086/jsl.v3i2.1804.
S. Liwan and P. C. Latue, “Analisis Spasial Perubahan Suhu Permukaan Daratan Kota Kupang Menggunakan Pendekatan Geospatial Artificial Intelligence (GeoAI),” Buana J. Geogr. Ekol. dan Kebencanaan, vol. 1, no. 1, pp. 14–20, 2023, doi: 10.56211/buana.v1i1.343.
Yovi, Ringgo Dwika, and Eka, “Penerapan Metode Monte Carlo pada Simulasi Prediksi Jumlah Calon Mahasiswa Baru Universitas Muhammadiyah Bengkulu,” J. Process., vol. 17, no. 2, pp. 74–81, 2022, doi: 10.33998/processor.2022.17.2.1224.
D. C. Dewi, S. Sumijan, and G. W. Nurcahyo, “Simulasi Monte Carlo dalam Mengidentifikasi Peningkatan Penjualan Tanaman Mawar (Studi Kasus di Toko Bunga 5 Bersaudara Kota Solok),” J. Inform. Ekon. Bisnis, vol. 3, pp. 60–65, 2020, doi: 10.37034/infeb.v3i2.67.
M. Seprima and D. Defrianto, “Prediksi Curah Hujan Dan Kelembaban Udara Kota Pekanbaru Menggunakan Metode Monte Carlo,” Komun. Fis. Indones., vol. 17, no. 3, p. 134, 2020, doi: 10.31258/jkfi.17.3.134-138.
R. Lubis, “Simulasi Jenis Penyakit Pasien yang Berobat Menggunakan Metode Monte Carlo,” J. Sistim Inf. dan Teknol., vol. 4, no. 2, pp. 42–46, 2022, doi: 10.37034/jsisfotek.v4i2.121.
A. E. Syaputra, “Akumulasi Metode Monte Carlo dalam Memperkirakan Tingkat Penjualan Keripik Sanjai,” J. Inform. Ekon. Bisnis, vol. 5, pp. 209–216, 2023, doi: 10.37034/infeb.v5i1.222.
R. Maulana, “Aplikasi metode monte carlo dalam analisis prediksi impor beras dari pakistan,” JATI(Jurnal Mhs. Tek. Inform., vol. 8, no. 4, 2024, doi: https://doi.org/10.36040/jati.v8i4.10139.
R. D. Z. Putri, “Optimalisasi Prediksi Impor Buah-Buahan Dari Negara Australia Menggunakan Simulasi Monte Carlo,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, 2024, doi: https://doi.org/10.36040/jati.v8i4.10136.
S. Hidayatullah and A. Cherid, “Prediksi temperatur cuaca di negara norwegia menggunakan metode LSTM,” J. Sist. Inf. dan Sist. Komput., vol. 8, no. 2, pp. 187–198, 2023, doi: 10.51717/simkom.v8i2.192.
A. Asari et al., Dasar penelitian kuantitatif. Jawa Tengah: Lakeisha, 2023.
Musnaini, I. K. Mudhita, Asrini, and I. A. W. Laksmi, Statitiska Ekonomi : Metode Probabilitas. Jambi, 2024.
R. Kurniawan, A. Halim, and H. Melisa, “Prediksi Hasil Panen Pertanian Salak di Daerah Tapanuli Selatan Menggunakan Algoritma SVM (Support Vector Machine),” KLIK Kaji. Ilm. Inform. dan Komput., vol. 4, no. 2, pp. 903–912, 2023, doi: 10.30865/klik.v4i2.1246.
S. A. Maulana, “Penerapan Metode Monte Carlo Dalam Memprediksi Produksi Daging Sapi Di Provinsi Sulawesi Utara,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 4, 2024.
V. R. A. Priyantono, I. M. Di Asih, and I. T. Utami, “Analisis Portofolio Optimal Menggunakan Model Indeks Tunggal dan Pengukuran Value at Risk dengan Simulasi Monte Carlo,” J. Gaussian, vol. 12, no. 2, pp. 158–165, 2023, doi: https://doi.org/10.14710/j.gauss.12.2.158-165.
T. Rahmawati, E. Y. Sari, A. Priyanto, I. Yasin, V. R. B. Kurniawan, and D. D. Jaya, “Analisis Prediksi Penjualan Wedang Uwuh Instan dengan Simulasi Algoritma Monte Carlo,” G-Tech J. Teknol. Terap., vol. 8, no. 1, pp. 615–623, 2024, doi: https://doi.org/10.33379/gtech.v8i1.3705.
Muhammad Romzi and B. Kurniawan, “Pembelajaran Pemrograman Python Dengan Pendekatan Logika Algoritma,” JTIM J. Tek. Inform. Mahakarya, vol. 03, no. 2, pp. 37–44, 2020.
R. Adriyansyah and M. S. Hadi, “Penentuan Volume Kerucut Elips Menggunakan Monte Carlo dengan Bantuan Python,” J. Pembelajaran Dan Mat. Sigma, vol. 9, no. 1, pp. 172–176, 2023, doi: 10.36987/jpms.v9i1.4069.
F. Fauziah and W. Wisanty, “Color Palette menggunakan Python cv2 dan NumPy,” Semin. Nas. Tek. Elektro dan …, pp. 163–166, 2023.
T. Destiana, Y. Umaidah, and U. Enri, “Penerapan Algoritma Gaussian Naive Bayes Dalam Penentuan Prioritas Rehabilitasi Daerah Aliran Sungai Berdasarkan Parameter Lahan Kritis,” INFOTECH J., vol. 9, no. 2, pp. 507–513, 2023, doi: 10.31949/infotech.v9i2.6501.
R. Kaestria et al., “Penerapan Matplotlib dalam Visualisasi Data untuk Analisis Hubungan Penggunaan Gadget dan Hasil Belajar,” J. Digit. Bus. Inf. Technol., pp. 29–39, 2024, doi: 10.23971/jobit.v1i1.204.
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