Abstract :

Rainfall in the Deli Serdang region is influenced by global climate phenomena. This study aims to determine the characteristics of rainfall based on machine learning due to the simultaneous occurrence of IOD and MJO in the Deli Serdang region. This study uses a descriptive method and Pearson correlation analysis using rainfall, IOD, and MJO data. The results of the study with machine learning showed that the accuracy value of the SVM model was 56.16% and when the MJO was strong and the IOD was positive in January – December 2024 in the Tuntungan region, the highest was 258 mm and the lowest was Bandar Khalipa 167 mm. Strong MJO and Negative IOD were found in December 2022, the highest area was Sibiru-biru 264 mm and the lowest was 146.16 mm. Weak MJO and Positive IOD in the low-lying Bandar Khalipa region were 140 mm. Dry months can be predicted using several indicators, including the MJO (Madden-Julian Oscillation) and IOD (Indian Ocean Dipole). However, dry months are more often predicted using the IOD indicator. IOD has a significant influence on rainfall in Indonesia, especially in eastern Indonesia. When IOD is in a positive phase, rainfall in Indonesia tends to decrease, increasing the possibility of a dry month. MJO has a greater influence on rainfall on a shorter time scale, such as weekly or monthly. MJO can affect rainfall in Indonesia, but its influence is not as great as IOD in predicting dry months.

---

Keywords :

Deli Serdang, IOD, Machine learning, MJO, rainfall

---

References :

1. Anggraini, A, Noveni, T, Lubis . (2022). Analisis Karakteristik Intensitas Curah Hujan di Kabupaten Deli Serdang 1413-1428 H. Jurnal Ilmu Fisika dan Teknologi, pp 34-3.
2. Heryadi, Y. and Wahyono, T. (2020). Machine Learning Konsep dan Implementasi. Cetakan 1. Yogyakarta: Gava Media.
3. 2021. “Analisis Curah Hujan Rencana Dengan Menggunakan Distribusi Gumbel untuk Wilayah Kabupaten Lampung Selatan”. Journal of Science, Technology, and Visual Culture 1 (1), 51-54.
4. Kurniawan, D. (2021). Pengenalan Machine Learning dengan Python. Jakarta: ElexMediaKomputindo.
5. Maloney, E. and D. Hartmann. 2000. “Modulation of Eastern North Pacific hurricanes by the Madden Julian Oscillation”. J Climate, 13:1451-1460.
6. Pakpahan Sahata. (2023). Identifikasi Karakteristik Kejadian Curah Hujan Ekstrem di Provinsi Sumatera Utara. Tesis. Jurusan Fisika FMIPA. Universitas Sumatera Utara: Sumatera Utara.
7. Puspitotanti, E. (2021). Kajian Kerentanan Sosial Terhadap Bencana Banjir. Kajian Ruang,
8. Putra, R. M., Solih, A., dan Bagas, H. A. E. 2021. “Identifikasi Pengaruh EL Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden Julian Oscillation (MJO) Terhadap Intensitas Curah Hujan Bulanan di Indonesia Berbasis Machine Learning”. Buletin Meteo Ngurah Rai, 6(2),pp. 1.
9. Safitri, A.N. (2021). Manajemen Risiko Bencana Hidroklimatologi Untuk Ketahanan Kota Di Semarang. Jurnal Biosains Pascasarjana, 23(1), 6-15.
10. Solihin, R.R., Anri, N.A.R., Novi, A. 2021. Studi Analisis Perubahan Iklim Terhadap Distribusi Curah Hujan Pada Daerah Tangkapan Air Pataruman. JITSi (Jurnal Ilmiah Teknik Sipil), 1(2), 10-19.