Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal El-Jughrafiyah : Jurnal Geografi dan Terapannya Jurnal Pendidikan Indonesia (Japendi)
Saputro, Ogi Rahmawan Adi
Unknown Affiliation
Earth & Planetary Sciences Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Physics Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Dinamika Atmosfer di Benua Maritim Indonesia saat Fase Aktif CENS dan MJO Selama 30 Tahun (1994 - 2023) Adzan, Muhizzadin Abdul; Amanu, Rendy Syahril; Kuswanaji, Panji; Amri, Sayful; Saputro, Ogi Rahmawan Adi
EL-JUGHRAFIYAH Vol 5, No 1 (2025): El-Jughrafiyah : February, 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jej.v5i1.35655

Abstract

Beberapa variabilitas iklim di Benua Maritim Indonesia (BMI) dapat terjadi secara bersamaan dan memberikan dampak yang berbeda pada saat terjadi secara independen. Namun beberapa penelitian masih mengkajinya secara terpisah. Oleh karena itu, penelitian ini bertujuan untuk menganalisis dinamika atmosfer di BMI saat terjadi Cross-Equatorial Northerly Surge (CENS) dan Madden-Julian Oscillation (MJO) berdasarkan data anomali outgoing longwave radiation (OLR) dan curah hujan selama 30 tahun (1994 - 2023) dengan metode uji siginifikansi statistik Monte-Carlo. Hasil penelitian menunjukkan bahwa MJO aktif memengaruhi dampak yang ditimbulkan oleh CENS di BMI. Pada saat terjadi CENS tanpa MJO, wilayah Laut Jawa dan Jabodetabek mengalami peningkatan tutupan awan konvektif dan memicu curah hujan ekstrem, sementar di wilayah Sumatra dan Kalimantan justru mengalami penurunan. Namun saat CENS dan MJO fase 3 aktif, sebagian besar wilayah tersebut mengalami peningkatan tutupan awan dan curah hujan, yakni sebesar 25 mm/hari. Pada saat CENS dan  MJO fase 4 dan 5 aktif, terjadi peningkatan tutupan awan konvektif dan curah hujan sebesar 5 hingga > 25 mm/hari di sebagian besar wilayah Indonesia, kecuali pesisir timur Sumatra bagian utara, sebagian Jawa, Kalimantan, dan Papua. Pada fase 6 MJO, pola anomali positif curah hujan di BMI semakin bergeser ke timur, yaitu sebesar 2 hingga 8 mm/hari. Peningkatan curah hujan di pesisir utara Jawa bagian barat menunjukkan bahwa CENS berperan dalam menyediakan kelembapan serta menyeimbangkan suppressed convective MJO. Hasil yang sama juga terlihat pada fase 7 dan 8 MJO, wilayah perairan utara Jawa dan Nusa Tenggara yang masih mengalami peningkatan curah hujan, yaitu > 25 mm/hari.
Analisis Prediksi Parameter Signifikansi Siklon Tropis di Wilayah Utara dan Selatan Indonesia Fadhillah, Muhammad; Gilbert, Kevin; Saputro, Ogi Rahmawan Adi
Jurnal Pendidikan Indonesia Vol. 6 No. 3 (2025): Jurnal Pendidikan Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/japendi.v6i3.7284

Abstract

Tropical cyclones are atmospheric phenomena that occur in warm ocean areas, including the northern and southern regions of Indonesia. Although rarely formed directly in Indonesia, tropical cyclones from the Pacific and Indian Oceans often have a significant impact on weather conditions in Indonesia. This study was conducted to identify the influence of Sea Surface Temperature (SST) on the formation and development of tropical cyclones using a machine learning approach with a Random Forest model. The data used was in the form of reanalysis of SST data sourced from ECMWF (European Center for Medium - Range Weather Forecast) and analyzed for six tropical cyclone events representing the northern and southern regions of Indonesia. In addition, this study also aims to evaluate the effectiveness of machine learning-based prediction models in predicting SST parameters by using evaluation metrics such as RMSE, MAE, and R² to ensure prediction accuracy. The results showed that the SST values that supported the formation of tropical cyclones ranged from 31–33°C, which corresponds to the minimum temperature criteria for the formation of tropical cyclone systems. The Random Forest model showed excellent performance with low RMSE and MAE scores, and an R² value close to 1 in all cases tested with Tropical Cyclone Dahlia being the best case with the highest prediction accuracy. This study shows that the Random Forest model is able to effectively capture complex patterns of SST and provide accurate predictions, potentially as an instrument to understand and mitigate risks associated with tropical cyclone events in Indonesia.
Co-Authors Adzan, Muhizzadin Abdul Amanu, Rendy Syahril Amri, Sayful Gilbert, Kevin Kuswanaji, Panji Muhammad Fadhil Z, Rusdi Noor Rosa (p:43-50)