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All Journal Jurnal Ilmu Pertanian Indonesia J-Sil (Jurnal Teknik Sipil dan Lingkungan) Agromet POSITRON TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Geografi : Media Informasi Pengembangan dan Profesi Kegeografian Majalah Geografi Indonesia Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Sains & Teknologi Modifikasi Cuaca IJEMS (Indonesian Journal of Environmental Management and Sustainability) JURNAL SUMBER DAYA AIR Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Journal of the Civil Engineering Forum Jurnal Meteorologi dan Geofisika J-Sil (Jurnal Teknik Sipil dan Lingkungan) Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research)
Santikayasa, I Putu
Department Of Geophysics And Meteorology, Faculty Of Mathematics And Natural Sciences, IPB University, Dramaga Campus, Bogor, Indonesia 16680
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Physics Public Health Social Sciences Transportation Other

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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.
Fragmentasi Jaringan dan Koalisi Advokasi dalam Tata Kelola Daerah Aliran Sungai: Pelajaran dari DAS Paguyaman, Indonesia (Network Fragmentation and Advocacy Coalitions in Watershed Governance: Lessons from the Paguyaman Basin, Indonesia) Ivan Taslim; Sambas Basuni; Yanuar Jarwadi Purwanto; I Putu Santikayasa
Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Management Research) Vol 9 No 2 (2025): Jurnal Penelitian Pengelolaan Daerah Aliran Sungai (Journal of Watershed Manageme
Publisher : Asosiasi Peneliti dan Teknisi Kehutanan dan Lingkungan Hidup Indonesia (APTKLHI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59465/jppdas.2025.9.2.201-222

Abstract

Watershed management in developing countries often faces persistent implementation gaps, despite the existence of sound environmental policies. This study investigates how stakeholder structure and coalition dynamics contribute to governance challenges in the Paguyaman Watershed, Gorontalo, Indonesia. Using a descriptive-exploratory approach, the MACTOR method was applied to analyze the relational structure among 15 key actors and 22 strategic objectives, based on a structured questionnaire, focus group discussions, and influence mapping. The results reveal a polarized stakeholder network divided into two coherent coalitions: a Government-Academic coalition oriented towards technocratic goals and a Civil Society coalition focused on socio-ecological justice. Divergence scores between the coalitions ranged from 0.81 to 0.87, indicating a high significant ideological conflict. Industry actors are found structurally isolated, with low convergence scores (<0.3), undermining collaborative efforts. The influence-dependence analysis identified four Driving Actors, seven Linking Actors, one Dependent Actor, and three Autonomous Actors. These imbalanced relational explain the failure of institutional coordination and the limited policy legitimacy. These findings emphasize that technical solutions alone are insufficient; effective watershed governance requires strategic interventions to bridge polarized coalition, engage isolated industry sectors, and strengthen coordinating actors to promote inclusive, and collaborative governance.
A comparative assessment of machine learning, statistical, and hybrid approaches in amending ECMWF rainfall bias in the Batanghari river basin Arif Ma&#039;rufi; Tania June; I Putu Santikayasa; Apip Apip; Siswanto Siswanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27848

Abstract

In tropical basins, rainfall predictions using international numerical weather prediction (NWP) models, like the European Centre for Medium-Range Weather Forecast (ECMWF), exhibit significant persistent biases and insufficient ensemble dispersion. This study evaluates statistical, machine learning (ML)-based, and hybrid bias correction methods to improve daily rainfall estimates for Indonesia’s Batanghari River Basin. Findings indicate that the ECMWF model significantly overestimates wet-day frequency (WDF) (81.5% versus 43.4% observed) and underestimates extreme maximum magnitudes, exhibiting an annual maximum rainfall bias of -15.6 mm/day. Standalone ML-based methods, such as random forest (RF) and extreme gradient boosting (XGBoost), experience significant variance deflation, inadequately addressing these substantial underestimations. In contrast, quantile mapping (QM) significantly reduces the intrinsic drizzle bias and achieves the highest monthly temporal stability among the tested frameworks (Kling-Gupta efficiency (KGE) = 0.341) with low processing requirements. Moreover, hybrid methodologies, including RF-QM and XGBoost-QM, successfully restore the natural ensemble spread and robustly reconstruct the upper tails of extreme rainfall, albeit at the expense of long term volume conservation (resulting in negative monthly KGE). Ultimately, QM is favored as an effective baseline for continuous hydrological modeling, whereas hybrid methodologies are highly suited for evaluating localized flash flood hazards and short-term severe intensities.
Co-Authors . Agis Akhmad Faqih Alfi Rizky Sanusi Ali Wardhana Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan, Amsari Mudzakir Ana Turyanti Andika, Steven Andini, Fitri Yusti Andini, Nastiti Apip Apip Arif Ma&#039;rufi Azizi, Iqbal Hanun Bambang Dwi Dasanto Danang Eko Nuryanto Dede Dirgahayu Domiri, Dede Dirgahayu Donaldi Sukma, Permana Findy Renggono Fitri Yusti Andini Flegor Hermes Sabuna Giarno Giarno Hariyadi, Hariyadi I Nengan Muliarta Iqbal Hanun Azizi Istiqomah, Sifa Ivan Taslim Ivan Taslim Kartika, Qurrata A'yun Kii, Meriana Ina Marjuki Marjuki Marliana Tri Widyastuti Mauludiyyatus Syarifah Muh Taufik Muh. Taufik Muh. Taufik Okhy Wiranta, Dimas Patri, Syiskhaeka Prasetya, Dimas Ardi Pratikasiwi, Hilda Ayu Priambodo, Oktanindita Qonita, Ismi Rizqi Rachmat Fajar Lubis Rini Hidayati Rizaldi Boer RR Mashita Fauzia Hannum Samba Wirahma Sambas Basuni Setena, Made Setini, Made Siswanto Siswanto Siti Maesaroh Soenardi Soenardi Sopaheluwakan, Ardhasena Sulistiani Sulistiani, Sulistiani Supari Supari Supari Suria Darma Tarigan Suwarto Syarifah Mumtazah Tania June Wibawanty, Dinda Rosyia Widyastuti, Marliana Tri Yanuar Henry Pribadi Yanuar Jarwadi Purwanto Yonny Koesmaryono