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All Journal Jurnal Ilmu Pertanian Indonesia J-Sil (Jurnal Teknik Sipil dan Lingkungan) Agromet TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian Jurnal Meteorologi dan Geofisika
Santikayasa, I Putu
Bogor Agricultural University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Energy Engineering Physics Transportation

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Sea Surface Temperature Anomaly Characteristics Affecting Rainfall in Western Java, Indonesia Qurrata A'yun Kartika; Akhmad Faqih; I Putu Santikayasa; Amsari Mudzakir Setiawan
Agromet Vol. 37 No. 1 (2023): JUNE 2023
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.37.1.54-65

Abstract

Western Java is densely populated with high socio-economic activity. Climate-related disasters can be mitigated with the support of an understanding of systems that produce reliable climate predictions. One of the climate variables included in hydrometeorological disasters is rainfall. The characteristics of rainfall in Western Java cannot be separated from the sea surface temperature (SST) around the area. This study compares the relationship between SST and rainfall with singular value decomposition (SVD) and compares it with Pearson's correlation. SVD Model performance was evaluated using square covariance fraction (SCF) and Pearson correlation. The results showed that rainfall has a higher correlation with SST Anomaly (SSTA) by using SVD, with a correlation of about 0.63 in 6 to 9 months without lag time. Rainfall in western Java was closely related to the positive SSTA anomaly in southern Indonesia, especially the waters south of Java Island, and negative anomalies in other areas. Furthermore, atmospheric dynamic analysis showed that the positive coefficient expansion is followed by warmer SST, lower surface air pressure, higher water vapor, and higher rainfall, all were respective to their normal conditions around western Java. This study concludes that warmer SSTA around Western Java causes increased rainfall in western Java than normal and potentially impacts the hydrological disaster in West Java.
Mapping research trends on tropical cyclone–induced flood susceptibility: a bibliometric and systematic review method Soenardi Soenardi; Bambang Dwi Dasanto; Yonny Koesmaryono; I Putu Santikayasa; Giarno Giarno
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27614

Abstract

Climate change has intensified tropical cyclones (TC), increasing extreme rainfall and flood hazards in many regions. Flood susceptibility (FS) mapping is therefore essential for understanding flood risk. This study analyzes global research trends on TC-induced FS by integrating bibliometric analysis and a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based systematic literature review (SLR) using Google Scholar (GS) publications from 2014 to 2024. A total of 993 journal articles were analyzed, yielding an h-index of 101 and a g-index of 168, indicating strong and growing research interest. The results reveal an increasing application of machine learning (ML), deep learning (DL), remote sensing (RS), and geographic information systems (GIS) for FS mapping. Several gaps remain, including limited use of high-resolution data, underrepresentation of data-scarce and equatorial regions, restricted integration of hybrid models, and a lack of long-term assessments considering climate change and socio-economic factors. The model’s performance is also highly dependent on data quality and regional characteristics, limiting its generalizability across different conditions. The main contribution of this study is the knowledge mapping and synthesis of TC-induced FS research, providing a structured foundation for future studies and supporting evidence-based flood risk management and climate adaptation.
Machine learning-based reconstruction of missing rainfall extremes: a comparative analysis with classical models Yanuar Henry Pribadi; Tania June; I Putu Santikayasa; Supari Supari; Ana Turyanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27404

Abstract

The limited availability of daily rainfall data remains a key challenge in rainfall data analysis. This study assesses the effectiveness of spatial interpolation and bias correction techniques using satellite-derived rainfall data to fill missing observations in the Banten and Jakarta regions. Three interpolation methods inverse distance weighting (IDW), kriging, and spline were compared. Nine statistical and machine learning-based bias correction methods were applied to climate hazards group infrared precipitation with station data (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and global precipitation measurement-integrated multi-satellite retrievals for GPM (GPM IMERG). Performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), bias, Pearson correlation (R), and Kling-Gupta efficiency (KGE) in the expert team on climate change detection and indices (ETCCDI) extreme index. The research findings indicate that CHIRPS with quantile mapping (QM) bias correction delivers the best performance, followed by random forest regression (RFR) as the most accurate machine learning method. In spatial interpolation, IDW stands out as the leading method. Testing the extreme index ETCCDI confirms that CHIRPS-QM consistently outperforms machine learning and interpolation methods. In general, CHIRPS-QM and IDW represent the most effective combination of techniques for reconstructing daily rainfall, particularly extreme events. This study uniquely integrates spatial interpolation and bias correction in a unified evaluation.
Enhanced Clutter Mitigation in Weather Radar Observations Through Comparison Between a Dual-Polarisation, Dual-Scan, and Dual-Polarisation Dual-Scan Ali Wardhana; Rizaldi Boer; Bambang Dwi Dasanto; Danang Eko Nuryanto; I Putu Santikayasa
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1219

Abstract

Ground clutter remains a significant source of contamination in weather radar observations, adversely affecting the interpretation of echoes and subsequent meteorological applications. This study assesses the Dual-Polarisation Dual-Scan (DPDS) framework. This Bayesian-based classifier combines polarimetric descriptors (ρₕᵥ, ZDR) with temporal coherence (ρ₁₂) derived from consecutive azimuthal scans. The analysis uses I/Q data from an operational dual-polarisation C-band weather radar located in Sidoarjo, Surabaya, Indonesia. Results indicate that the DPDS framework significantly outperforms traditional Dual-Polarisation (DP) and Dual-Scan (DS) methods. For moving weather (W), the DPDS achieved a Probability of Detection (POD) of 0.939, a 313-fold improvement over the DP-only method, which suffered from severe polarimetric overlap between clutter and rain. While the clutter class exhibited a False Alarm Ratio (FAR) of 0.749, this is attributed to the 83-second scan interval of the Sidoarjo radar; over this duration, stable tropical rain remains highly correlated, mimicking the temporal signature of stationary ground clutter (C). However, the framework successfully preserved the integrity of the meteorological field, reducing the misclassification of zero-velocity weather (W0) compared to DS-only methods and achieving an overall accuracy of 0.982. These findings highlight the effectiveness of integrating polarimetric and temporal decorrelation information to establish a more robust, physically consistent echo classification framework, particularly under challenging conditions of clutter and low-velocity weather.
Co-Authors . Agis Akhmad Faqih Alfi Rizky Sanusi Ali Wardhana Amsari Mudzakir Setiawan Ana Turyanti Bambang Dwi Dasanto Danang Eko Nuryanto Flegor Hermes Sabuna Giarno Giarno Hariyadi Iqbal Hanun Azizi Kartika, Qurrata A'yun Mauludiyyatus Syarifah Muh Taufik Muh. Taufik Prasetya, Dimas Ardi Priambodo, Oktanindita Rini Hidayati Rizaldi Boer RR Mashita Fauzia Hannum Siti Maesaroh Soenardi Soenardi Supari Supari Suwarto Tania June Widyastuti, Marliana Tri Yanuar Henry Pribadi Yonny Koesmaryono