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Adi Mulsandi
Sekolah Tinggi Meteorologi Klimatologi Dan Geofisika
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Energy Environmental Science Immunology & microbiology Mathematics Neuroscience Physics

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MODEL ESTIMASI DATA INTENSITAS RADIASI MATAHARI UNTUK WILAYAH BANTEN Munawar Munawar; Adi Mulsandi; Anistia Malinda Hidayat
Jurnal Sains & Teknologi Modifikasi Cuaca Vol. 21 No. 2 (2020): December 2020
Publisher : BPPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29122/jstmc.v21i2.4171

Abstract

Data intensitas radiasi matahari (Rs, MJ/m2/day) memiliki peran yang sangat penting dalam pemodelan cuaca dan iklim guna mengkuantifikasi panas yang dipertukarkan antara permukaan dan atmosfer. Namun, keterbatasan jumlah titik pengamatan intensitas radiasi matahari menjadikan pemodelan sebagai alternatif solusi yang relatif mudah dan murah untuk pengambilan data intensitas radiasi. Penelitian ini bertujuan untuk mengevaluasi performa model dalam mengestimasi nilai intensitas radiasi matahari di wilayah penelitian menggunakan dua pendekatan model yang berbeda, yaitu model empiris oleh Keiser, Arkansas (AR) dan model deterministik. Tiga variabel utama cuaca yang digunakan sebagai input data model adalah curah hujan (mm), suhu maksimum (°C), dan suhu minimum (°C). Kedua model tersebut dipilih karena dapat diterapkan dengan hanya melibatkan variabel utama atmosfer yang tersedia dalam waktu yang panjang di lokasi penelitian. Hasil prediksi yang dilakukan dengan model kemudian dibandingkan dengan data reanalisis National Centers for Environmental Prediction (NCEP) pada titik koordinat wilayah Stasiun Klimatologi Pondok Betung. Hasilnya menunjukkan performa model empirik lebih baik dalam menggambarkan variasi temporal dan prediksi variabel intensitas matahari dibandingkan model deterministik. Hal tersebut ditunjukkan dengan nilai korelasi yang cukup baik, yakni mencapai 0,72 (korelasi kuat) dan nilai Root Mean Square Error (RMSE) 2,0. Atas dasar hasil pemodelan yang cukup representatif di lokasi penelitian, analisis secara spasial kemudian diterapkan untuk skala wilayah yang lebih luas, yaitu Provinsi Banten. Berdasarkan tinjauan secara spasial di wilayah kajian, model empirik memiliki performa yang bervariasi di wilayah Provinsi Banten. Hasil prediksi intensitas radiasi matahari di wilayah bagian barat memiliki performa yang lebih baik dibandingkan wilayah bagian timur.  
VARIABILITAS INTERANNUAL HUJAN MONSUN INDONESIA: REVIEW ARTIKEL TENTANG PENGARUH GAYA EKSTERNALNYA Mulsandi, Adi; Koesmaryono, Yonny; Hidayat, Rahmat; Faqih, Akhmad; Sopaheluwakan, Ardhasena
Jurnal Meteorologi dan Geofisika Vol. 24 No. 2 (2023)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v24i2.1049

Abstract

The IMR variability is notorious for its hydrometeorological disasters. This paper examines recent studies on IMR and the main factors controlling its variability. The focus of this study is to investigate the impact of the atmosphere-ocean interaction that acts as the external forcing of IMR in the tropical Indian and Pacific Oceans. Specifically, the study will examine the influence of two climate phenomena, namely the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) and their interdecadal changes associated Pacific Decadal Oscillation (PDO), on the IMR. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. Furthermore, data sets (such as rainfall, wind field, and SST) spanning 1990-2020 were used to verify the key findings. In general, this study concludes that the majority of the authors coincided with the following conclusion: ENSO and IOD events impact IMR by changing its amplitude, duration, intensity, and frequency of mean and extreme rainfall. Additionally, it has been shown that their impacts on IMR are most substantial during the dry seasons, specifically in June, July, and August (JJA), and not as strong as during the wet seasons, specifically in December, January, and February (DJF). Spatially, the effects of ENSO and IOD on IMR variability are clearly found more eastward and westward of the region, respectively. The expansions towards the east and west directions were facilitated by the displacement of the ascending and descending of Walker circulation patterns in the Indonesian region, respectively. Given the interannual fluctuations in IMR, caused mainly by ocean-atmosphere interactions, the knowledge gap of atmospheric factors like the Quasi-Biennial Oscillation (QBO) must be investigated in the future, as suggested by previous research and our preliminary study.
A station-scale modeling framework for heavy rainfall classification in tropical weather using representative machine learning approaches MULSANDI, ADI; MIFTAHUDDIN, MIFTAHUDDIN
Jurnal Natural Volume 25 Number 3, October 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i3.48605

Abstract

Extreme daily rainfall in rapidly urbanizing tropical cities frequently overwhelms drainage and disrupts critical services, yet station-scale forecasting remains limited by convective variability and sparse observations. This motivates lightweight, interpretable machine-learning tools that operate on routine station data. We propose and evaluate a station-scale framework to classify heavy-rainfall days (50 mm) in a humid tropical setting. Using 1,796 daily observations from the Soekarno-Hatta Meteorological Station (20182022), we engineered lag-informed predictors (e.g., previous-day rainfall, 3-day sums/means) and compared three representative classifiers, Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF). Class imbalance was addressed with class-weighted training, and models were assessed on a held-out test set using precision, recall, F1, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). LR achieved the highest recall (0.429), indicating moderate sensitivity to rare heavy-rainfall events, whereas RF yielded the best probabilistic discrimination (AUC = 0.619) but failed to flag positives at the default threshold; SVM displayed near-random behavior. Feature analyses highlighted humidity, temperature, and recent rainfall accumulation as the most influential predictors, consistent with tropical convective processes. Despite severe class imbalance, simple, station-based classifiers can extract actionable signals for rare-event screening in data-limited tropical regions. Operational value is likely to improve through probability calibration and threshold tuning, ensemble integration, and spatial generalization to multi-station settings.
Co-Authors A.A. Ketut Agung Cahyawan W Achmad Zakir Ahmad Muhlis Akhmad Faqih Anistia Malinda Hidayat Aries Kristianto Hidayat, Anistia Malinda Ichsan Setiawan Lutfi Fitriano Lutfi Fitriano, Lutfi Mamenun Mamenun Mamenun, Mamenun Miftahuddin Miftahuddin Munawar Munawar Perdana, Ilham Fajar Putra Prasetya, Ferdian Adhy Rahmat Hidayat Rahmat Hidayat Rismana, Yosza Indra Saputro, Hari Sopaheluwakan, Ardhasena Yonny Koesmaryono Zakir, Achmad Zakir, Achmad