http://repository.ipb.ac.id/handle/123456789/90 2026-08-29T18:38:56Z 2026-08-29T18:38:56Z Wardhana, Ali http://repository.ipb.ac.id/handle/123456789/179351 2026-08-15T04:20:53Z 2026-01-01T00:00:00Z

DEVELOPMENT OF EARLY WARNING FOR EXTREME RAINFALL IN SURABAYA AND ITS SURROUNDINGS USING DUAL-POLARIZATION WEATHER RADAR ENHANCED BY ARTIFICIAL INTELLIGENCE Wardhana, Ali Precise Quantitative Precipitation Estimation (QPE) is crucial for mitigating hydrometeorological disasters, particularly urban flooding in metropolitan areas such as Surabaya. Rainfall in this region exhibits significant spatial and temporal variability due to complex tropical convective systems. While C-band dual- polarization weather radar provides high-resolution observations, its operational performance is often compromised by severe signal attenuation, ground clutter contamination, and the inadequacy of conventional empirical radar rainfall relationships in capturing the nonlinear microphysical characteristics of tropical precipitation. To overcome these challenges, this study developed an integrated validation framework for rainfall estimation utilizing C-band dual-polarization weather radar and Automated Weather Station (AWS) observations. This framework was further enhanced through Machine Learning (ML) and Deep Learning (DL) techniques to support operational extreme rainfall early warning in the Surabaya metropolitan region. The research methodology was conducted in several stages. Initially, a systematic literature review was conducted to evaluate the current state of ML and DL-based rainfall estimation methods. Subsequently, robust physical quality- control procedures were developed. A novel Bayesian Dual-Polarization Dual-Scan (DPDS) framework was proposed to mitigate ground clutter contamination by integrating polarimetric descriptors with temporal coherence information. Signal attenuation was addressed by comparing phase-based correction techniques, namely Linear PhiDP and ZPHI methods. Following radar preprocessing, a multi- sensor data fusion framework was established by synchronizing radar-derived polarimetric variables with surface meteorological observations from the Advanced Weather Stations (AWS), including air temperature, relative humidity, atmospheric pressure, and wind speed. Finally, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a one-dimensional Convolutional Neural Network–Long Short- Term Memory (1D-CNN-LSTM) model were developed and independently validated during an extreme flood event in East Java from March 14–23, 2026. The findings reveal that physical quality control serves as an indispensable prerequisite for radar-based rainfall estimation. Yet, it falls short of achieving precise quantitative precipitation estimation (QPE) on its own. The proposed DPDS framework effectively differentiated meteorological echoes from non- meteorological echoes, thereby significantly mitigating urban clutter contamination and enhancing radar data reliability. While attenuation correction successfully recovered reflectivity structures behind intense convective cores, statistical analyses indicated that selecting phase-based attenuation correction methods had only a limited impact on final rainfall estimation accuracy. Instead, rainfall estimation performance was primarily governed by the selection of radar rainfall relationships. Even the most proficient locally calibrated empirical relationships consistently underestimated extreme convective rainfall, underscoring the limitations of fixed parametric formulations in tropical maritime environments. The transition from empirical approaches to data-driven algorithms significantly improved rainfall estimation performance. Feature importance analyses revealed that attenuation-corrected near-surface horizontal reflectivity was the primary predictor of rainfall intensity. The incorporation of surface meteorological variables, particularly wind speed, significantly improved estimation accuracy by resolving below-cloud atmospheric processes such as evaporation and wind shear. Among the evaluated models, the Random Forest model integrating radar and meteorological variables exhibited the most robust, operationally reliable performance during independent validation. This model achieved the lowest estimation errors (RMSE = 1.20 mm/6-min; MAE = 0.41 mm/6-min), the highest correlation coefficient (CC = 0.58), and the highest coefficient of determination (R² = 0.32) during the March 2026 flood event. Furthermore, it consistently produced the highest Critical Success Index (CSI) across multiple rainfall thresholds, from light to extreme. In contrast, the 1D-CNN- LSTM architecture demonstrated limited capability in representing the full dynamic range of extreme tropical rainfall, primarily because its one-dimensional temporal representation was unable to adequately characterize the spatial morphology and structural complexity of convective storm systems. In summary, this study elucidates that reliable tropical rainfall estimation cannot be achieved solely through empirical radar equations or single-sensor observations. The integration of dual-polarization weather radar observations with surface meteorological measurements, employing optimized ML frameworks such as Random Forest, provides a scalable, accurate, and operationally viable solution for quantitative precipitation estimation in tropical environments. These findings advocate a paradigm shift from conventional radar rainfall estimation to integrated, artificial intelligence-based frameworks for operational hydrometeorological applications. Consequently, it is recommended that operational meteorological agencies adopt combined radar-meteorological ML frameworks in conjunction with standardized 6-minute rainfall intensity thresholds to enhance real-time flash flood early warning systems. Future research should focus on multidimensional DL architectures that leverage spatial tensor representations better to capture the intricate spatial structures of tropical convective precipitation.; Estimasi Curah Hujan Kuantitatif (Quantitative Precipitation Estimation/QPE) yang Akurat sangat penting untuk mitigasi bencana hidrometeorologi, khususnya banjir perkotaan di wilayah metropolitan seperti Surabaya. Curah hujan di wilayah ini menunjukkan variabilitas spasial dan temporal yang signifikan karena sistem konvektif tropis yang kompleks. Meskipun radar cuaca dual-polarisasi pita C memberikan pengamatan resolusi tinggi, kinerja operasionalnya seringkali terganggu oleh pelemahan sinyal, kontaminasi ground clutter dan ketidakcukupan hubungan curah hujan radar empiris konvensional dalam menangkap karakteristik mikrofisika nonlinier dari curah hujan tropis. Untuk mengatasi tantangan ini, penelitian ini mengembangkan kerangka kerja validasi terintegrasi untuk estimasi curah hujan menggunakan radar cuaca dual-polarisasi pita C dan pengamatan Stasiun Cuaca Otomatis (Automated Weather Station/AWS). Kerangka kerja ini selanjutnya ditingkatkan melalui teknik pembelajaran mesin dan pembelajaran mendalam untuk mendukung peringatan dini operasional cuaca ekstrim di wilayah metropolitan Surabaya. Metodologi penelitian dilakukan dalam beberapa tahap. Awalnya, tinjauan literatur sistematis dilakukan untuk mengevaluasi kondisi terkini metode estimasi curah hujan berbasis pembelajaran mesin dan pembelajaran mendalam. Selanjutnya, prosedur kontrol kualitas fisik yang kuat dikembangkan. Kerangka kerja Bayesian Dual-Polarization Dual-Scan (DPDS) yang baru diusulkan untuk mengurangi kontaminasi ground clutter dengan mengintegrasikan deskriptor polarimetrik dengan informasi koherensi temporal. Atenuasi sinyal diatasi dengan membandingkan teknik koreksi berbasis fase, yaitu metode Linear PhiDP dan ZPHI. Setelah pra-pemrosesan radar, kerangka kerja fusi data multi-sensor dibentuk dengan menyinkronkan variabel polarimetrik yang berasal dari radar dengan pengamatan meteorologi permukaan dari Stasiun AWS, termasuk suhu udara, kelembaban relatif, tekanan atmosfer, dan kecepatan angin. Akhirnya, model Random Forest (RF), Extreme Gradient Boosting (XGBoost), dan 1D- Convolutional Neural Network–Long digunakan. dikembangkan dan divalidasi secara independen selama peristiwa banjir ekstrem di Jawa Timur dari tanggal 14– 23 Maret 2026. Temuan menunjukkan bahwa kontrol kualitas fisik berfungsi sebagai prasyarat yang sangat diperlukan untuk estimasi curah hujan berbasis radar. Namun, hal itu sendiri masih kurang untuk mencapai estimasi curah hujan kuantitatif yang tepat. Kerangka kerja DPDS yang diusulkan secara efektif membedakan gema meteorologi dari gema non-meteorologi, sehingga secara signifikan mengurangi kontaminasi kekacauan perkotaan dan meningkatkan keandalan data radar. Meskipun koreksi atenuasi berhasil memulihkan struktur reflektivitas di belakang inti konvektif yang intens, analisis statistik menunjukkan bahwa pemilihan metode koreksi atenuasi berbasis fase hanya memiliki dampak terbatas pada akurasi estimasi curah hujan akhir. Sebaliknya, kinerja estimasi curah hujan terutama diatur oleh pemilihan hubungan curah hujan radar Z-R. Bahkan hubungan empiris yang dikalibrasi secara lokal yang paling mahir secara konsisten meremehkan curah hujan konvektif ekstrem, menggarisbawahi keterbatasan formulasi parametrik tetap di lingkungan maritim tropis. Transisi dari pendekatan empiris ke pendekatan berbasis data Algoritma secara signifikan meningkatkan kinerja estimasi curah hujan. Analisis feature importance mengungkapkan bahwa reflektivitas horizontal dekat permukaan yang dikoreksi adalah prediktor utama intensitas curah hujan. Penggabungan variabel meteorologi permukaan, khususnya kecepatan angin, secara signifikan meningkatkan akurasi estimasi dengan menyelesaikan proses atmosfer di bawah awan seperti penguapan dan wind shear. Di antara model yang dievaluasi, model RF yang mengintegrasikan radar dan variabel meteorologi menunjukkan kinerja yang paling kuat dan andal secara operasional selama validasi independen. Model ini mencapai kesalahan estimasi terendah (RMSE = 1,20 mm/6-menit; MAE = 0,41 mm/6-menit), koefisien korelasi tertinggi (CC = 0,58), dan koefisien determinasi tertinggi (R² = 0,32) selama peristiwa banjir Maret 2026. Lebih lanjut, model ini secara konsisten menghasilkan Indeks CSI tertinggi di berbagai ambang batas curah hujan, dari ringan hingga ekstrem. Sebaliknya, arsitektur 1D-CNN-LSTM menunjukkan kemampuan terbatas dalam merepresentasikan rentang dinamis penuh curah hujan tropis ekstrem, terutama karena representasi temporal satu dimensi tidak mampu menggambarkan morfologi spasial dan kompleksitas struktural sistem badai konvektif secara memadai. Penelitian ini menjelaskan bahwa estimasi curah hujan tropis yang andal tidak dapat dicapai hanya melalui persamaan radar empiris atau pengamatan sensor tunggal. Integrasi pengamatan radar cuaca polarisasi ganda dengan pengukuran meteorologi permukaan, menggunakan kerangka kerja pembelajaran mesin yang dioptimalkan seperti RF memberikan solusi yang terukur, akurat, dan layak secara operasional untuk estimasi curah hujan kuantitatif di lingkungan tropis. Temuan ini mendukung pergeseran paradigma dari estimasi curah hujan radar konvensional ke kerangka kerja berbasis kecerdasan buatan terintegrasi untuk aplikasi hidrometeorologi operasional. Akibatnya, disarankan agar lembaga meteorologi operasional mengadopsi kerangka kerja pembelajaran mesin radar-meteorologi gabungan bersamaan dengan ambang batas intensitas curah hujan 6 menit yang distandarisasi untuk meningkatkan sistem peringatan dini secara real-time. Penelitian masa depan harus fokus pada arsitektur pembelajaran mendalam multidimensi yang memanfaatkan representasi tensor spasial dengan lebih baik untuk menangkap struktur spasial rumit dari curah hujan konvektif tropis.

2026-01-01T00:00:00Z Murniati, Dewi http://repository.ipb.ac.id/handle/123456789/179345 2026-08-15T04:19:36Z 2026-01-01T00:00:00Z

Pengembangan Metode Deteksi Miristisin Dan Metil Eugenol Pada Pala Menggunakan Elektrode Pasta Karbon Termodifikasi Murniati, Dewi Miristisin merupakan senyawa penciri utama tanaman pala (Myristica fragrans Houtt.), sehingga deteksinya penting untuk evaluasi kualitas pala. Penelitian ini bertujuan untuk memfabrikasi, mengoptimasi, dan memvalidasi sensor elektrokimia berbasis komposit Fe3O4-grafena sebagai metode alternatif yang efisien untuk mengkuantifikasi analit miristisin pada pala, dengan tingkat keandalan dan akurasi yang setara dengan instrumen standar Kromatografi Gas-Spektrometri Massa (KG-SM). Penelitian ini terdiri atas 2 (dua) tahap terdiri dari: tahap pertama yaitu pengembangan metode sensor miristisin dengan Glassy Carbon Electrode (GCE) termodifikasi Fe3O4-grafena dan tahap kedua optimasi ekstraksi miristisin dari pala menggunakan Response Surface Methodology-Box-Behnken Design (RSM-BBD). Ekstrak hasil optimasi digunakan sebagai sampel nyata untuk mevalidasi keandalan elektrode yang dikembangkan. Penelitian tahap pertama diawali dari sintesis Fe3O4 menggunakan metode hidrotermal dan menggabungkan Fe3O4 hasil sintesis dengan grafena membentuk komposit Fe3O4-grafena. Hasil sintesis dikarakterisasi menggunakan X-Ray Diffraction (XRD), Scanning Electron Microscope- Energy Dispersive X-Ray (SEM-EDX), Transmission Electron Microscopy (TEM) dan XPS (X-ray Photoelectron Spectroscopy) untuk mengevaluasi struktur kristal, morfologi, komposisi unsur, serta karakteristik material pada skala nano. Hasil karakterisasi menunjukkan bahwa partikel kristal Fe3O4 terdistribusi secara homogen pada struktur grafena dua dimensi. Keberhasilan modifikasi fisik ini berbanding lurus dengan peningkatan performa elektrokimianya. Fe3O4 bersifat elektrokatalitik yang mempercepat transfer elektron setelah analit miristisin diadsorbsi oleh permukaan grafena yang luas. Komposit tersebut kemudian diaplikasikan untuk pengembangan sensor elektrokimia miristisin. Evaluasi perilaku elektrokimia telah dilakukan dengan penentuan pH optimum larutan uji yaitu pada pH 5 dalam Britton-Robinson Buffer (BRB) dan komposisi massa material pemodifikasi Fe3O4:grafena terbaik pada rasio 1:2 b/b. Selanjutnya, estimasi luas permukaan elektroaktif dihitung berdasarkan Persamaan Randles-Sevcik melalui variasi laju pemindaian 25 mV/s hingga 150 mV/s menggunakan larutan probe redoks K3[Fe(CN)6] 1 mM dalam KCl. Hasil perhitungan menunjukkan terjadinya perluasan area aktif yang masif, dari A=0,108 cm2 pada GCE tanpa modifikasi meningkat 5 kali menjadi A=0,515 cm2 setelah dimodifikasi. Fenomena peningkatan area serah-terima elektron ini didukung oleh data Electrochemical Impedance Spectroscopy (EIS) pada kurva Nyquist, di mana nilai hambatan transfer muatan (R2) menurun 7 kali dari 3770 O (GCE tanpa modifikasi) menjadi hanya 501,6 O pada elektrode GCE/Fe3O4-grafena yang menunjukkan peningkatan sifat konduktivitas sensor. Keandalan sensor dalam mendeteksi miristisin dilakukan evaluasi kinerja analitik. Kurva linearitas memberikan nilai sensitivitas yang tinggi sebesar 0,21509 µA/µM, dengan Batas Deteksi (Limit of Detection, LOD) mencapai 0,19 µM dan Batas Kuantifikasi (Limit of Quantitation, LOQ) sebesar 0,58 µM. Tingkat selektivitas elektrode yang diuji terhadap senyawa interferen struktural (safrol) maupun interferen elektroaktif (asam askorbat, dopamin, dan tiamin) pada rasio konsentrasi 1:1 menunjukkan ketahanan sistem yang baik dengan nilai %RSD sebesar 3,45%. Selain itu, aspek durabilitas sensor terbukti andal berdasarkan uji stabilitas melalui 6 kali pengukuran berulang dengan %RSD sebesar 3,88% serta uji ketersalinan pada 6 elektrode berbeda yang menghasilkan nilai %RSD sebesar 4,29%. Meskipun fabrikasi sensor GCE/Fe3O4-grafena ini telah teruji keandalan analitiknya pada sistem standar murni miristisin, validasi aplikasi pada sampel nyata juga diujikan pada ekstrak pala, dalam penelitian ini menggunakan ekstrak biji pala. Tahap kedua penelitian adalah strategi optimasi ekstraksi menggunakan pendekatan statistik RSM-BBD dengan teknik maserasi menggunakan serbuk biji pala dan fuli masing-masing 10 g untuk setiap percobaan. Percobaan maserasi menggunakan pelarut etanol dirancang dengan perangkat lunak Design Expert 13.0 dengan formulasi variabel bebas, terdiri dari 3 faktor, yaitu konsentrasi etanol (A), volume etanol (B), dan waktu maserasi (C) secara ringkas diambil 12 percobaan (runs). Berdasarkan hasil maserasi diperoleh ekstrak biji pala yang dilanjutkan dengan analisis KG-SM untuk ditentukan kadar miristisinnya yang akan digunakan sebagai respons pada rancangan percobaan RSM-BBD. Hasil evaluasi statistik dari ANOVA Design Expert menunjukkan bahwa efisiensi ekstraksi paling optimum untuk kedua matriks tercapai pada penggunaan 50 mL pelarut etanol 90%. Kondisi volume etanol optimum pada 50 mL merupakan volume yang tepat agar tidak terjadi pengenceran ekstrak sedangkan konsentrasi etanol 90% disebabkan karakteristik miristisin yang bersifat semipolar hingga nonpolar. Perbedaan kinetika ekstraksi hanya terletak pada waktu maserasi: matriks biji pala (serbuk tepung) mampu mencapai kesetimbangan difusi dalam 2 jam, sedangkan matriks fuli (berserat) membutuhkan penetrasi pelarut selama 17 jam. Ekstrak dengan profil optimum inilah yang selanjutnya dipersiapkan sebagai matriks sampel nyata untuk divalidasi menggunakan sensor elektrokimia GCE/Fe3O4-grafena pada tahapan akhir. Analisis pada sampel nyata ekstrak biji pala menggunakan sensor elektrokimia GCE/Fe3O4-grafena untuk deteksi miristisin menggunakan metode adisi standar. Pengukuran dilakukan dengan teknik DPV pada konsentrasi 1-50 µM yang menunjukkan hubungan linier antara arus dan konsentrasi, dengan nilai R² sebesar 0,99824 pada konsentrasi 176,334 ± 16,250 µM. Nilai koefisien korelasi (R2) yang tinggi tersebut menunjukkan bahwa sensitivitas dan akurasi pada setiap konsentrasi melebihi 97%. Konsentrasi miristisin yang diperoleh menggunakan sensor elektrokimia GCE/Fe3O4-grafena sebanding dengan hasil pengukuran miristisin menggunakan KG-SM, yaitu sebesar 181,423 ± 16,171 µM. Komparasi menggunakan uji statistik ANOVA menunjukkan bahwa p > 0,05 membuktikan bahwa tidak terdapat perbedaan signifikan antara kedua metode tersebut. Secara keseluruhan, alur penelitian ini menunjukkan bahwa metode yang dikembangkan memiliki tingkat akurasi yang ekuivalen dengan metode standar untuk evaluasi mutu pala melalui deteksi miristisin sebagai senyawa penciri, sehingga metode ini dapat menjadi deteksi awal yang cepat dan akurat.; Myristicin is a key characteristic compound of nutmeg (Myristica fragrans Houtt.), making its detection crucial for evaluating nutmeg quality. This study aims to fabricate, optimize, and validate an electrochemical sensor based on a Fe3O4-grafena composite as an efficient alternative method for quantifying myristicin in nutmeg, with a level of reliability and accuracy comparable to standard Gas Chromatography-Mass Spectrometry (GC-MS) instruments. This study consisted of two stages: the first stage was the development of a myristicin sensor method using a Glassy Carbon Electrode (GCE) modified with Fe3O4-grafena, and the second stage was the optimization of myristicin extraction from nutmeg using Response Surface Methodology-Box-Behnken Design (RSM-BBD). The optimized extract was used as a real-world sample to validate the reliability of the developed electrode. The first stage of the study began with the synthesis of Fe3O4 using the hydrothermal method and combined the synthesized Fe3O4 with grafena to form a Fe3O4-grafena composite. The synthesis results were characterized using XRD (X-Ray Diffraction), SEM-EDX (Scanning Electron Microscope- Energy Dispersive X-Ray), TEM (Transmission Electron Microscopy) and XPS (X-ray Photoelectron Spectroscopy) to evaluate the crystal structure, morphology, elemental composition, and material characteristics at the nanoscale. The characterization results showed that Fe3O4 crystal particles had been distributed homogeneously over the two-dimensional structure of grafena. The success of this physical modification is directly proportional to the increase in its electrochemical performance. Fe3O4 exhibits electrocatalytic properties that accelerate electron transfer after myristicin analyte adsorbs onto the large graphene surface. The composite was then used to develop myristicin electrochemical sensors. The electrochemical behaviour has been evaluated by determining the optimum pH of the test solution at pH 5 in Britton-Robinson Buffer (BRB) and the optimum mass composition of Fe3O4:graphene modifying material at a 1:2 w/w ratio. Furthermore, the electroactive surface area was estimated using the Randles-Sevcik equation at scan rates ranging from 25 mV s-1 to 150 mV s-1, with a 1 mM K3[Fe(CN)6] redox probe solution in KCl. The calculation results showed a massive expansion of the active area, from A=0.108 cm2 on the unmodified GCE to A=0.515 cm2 after modification, an increase of 5 times. This phenomenon of increasing the electron transfer area is supported by Electrochemical Impedance Spectroscopy (EIS) data on the Nyquist curve, where the charge transfer resistance (R2) value decreased 7 times from 3770 O (unmodified GCE) to only 501.6 O on the GCE/Fe3O4-graphene electrode, indicating an increase in the sensor's conductivity properties. The sensor's reliability in detecting myristicin was evaluated based on analytical performance. The linearity curve provides a high sensitivity value of 0.21509 µA/µM, with a Limit of Detection (LOD) of 0.19 µM and a Limit of Quantitation (LOQ) of 0.58 µM. The electrode selectivity, tested against structural interfering compounds (safrole) and electroactive interfering compounds (ascorbic acid, dopamine, and thiamine) at a 1:1 concentration ratio, demonstrated good system robustness with a %RSD of 3.45%. Furthermore, the sensor's durability was proven reliable through stability tests comprising six repeated measurements with a %RSD of 3.88%, as well as a copyability test using six different electrodes, which yielded a %RSD of 4.29%. Although the analytical reliability of the GCE/Fe3O4-grafena sensor fabrication has been tested with a pure myristicin standard system, validation of its application to real samples was also tested with nutmeg extract in this study. The second stage of the research was an extraction optimization strategy using the RSM-BBD statistical approach, using a maceration technique using 10 g of nutmeg seed powder and mace for each experiment. The maceration experiment using ethanol solvent was designed in Design-Expert 13.0, with three independent variables: ethanol concentration (A), ethanol volume (B), and maceration time (C). A summary of 12 runs was taken. Based on the maceration results, nutmeg seed extract was obtained, which was then analyzed using GC-MS to determine the myristicin content, which will be used as a response in the RSM-BBD experimental design. The Design-Expert ANOVA results showed that the optimum extraction efficiency for both matrices was achieved with 50 mL of 90% ethanol solvent. The optimum ethanol volume of 50 mL was sufficient to prevent extract dilution, while the 90% ethanol concentration was due to myristicin's semipolar to nonpolar properties. The only difference in extraction kinetics was the maceration time: the nutmeg seed matrix (powder) reached diffusion equilibrium in 2 hours, whereas the mace matrix (fibrous) required 17 hours for solvent penetration. The extract with the optimum profile was then prepared as a real sample matrix for validation using the GCE/Fe3O4-grafena electrochemical sensor in the final stage. Analysis of real nutmeg seed extract samples using the GCE/Fe3O4-grafena electrochemical sensor for myristicin detection using the standard addition method. Measurements were performed using the DPV technique over 1-50 µM, demonstrating a linear relationship between current and concentration (R² = 0.99824 at 176.334 ± 16.250 µM). This high correlation coefficient (R²) indicates sensitivity and accuracy of 97% or higher at each concentration. The myristicin concentration obtained with the GCE/Fe3O4-graphene electrochemical sensor was comparable to the GC-MS measurement (181.423 ± 16.171 µM). A comparison using the ANOVA statistical test showed p>0.05, indicating no significant difference between the two methods. Overall, the developed method has accuracy equivalent to the standard method for evaluating nutmeg quality by detecting myristicin as a marker compound, making it a fast, accurate initial detection method.

2026-01-01T00:00:00Z Masri, Akma Yeni http://repository.ipb.ac.id/handle/123456789/179211 2026-08-15T02:40:49Z 2026-01-01T00:00:00Z

Climate Finance Transformation Model: A Generic Decision-Support Framework for Mitigation Financing Masri, Akma Yeni AKMA YENI MASRI. Climate Finance Transformation Model: A Generic Decision-Support Framework for Mitigation Financing. Supervised by RIZALDI BOER, MUHAMMAD FIRDAUS, and LILIEK SOFITRI. Climate change has intensified the need not only to mobilize climate finance but also to evaluate whether it effectively delivers greenhouse gas (GHG) mitigation outcomes. Existing research has primarily focused on estimating future investment needs, with limited attention to assessing the effectiveness of realized climate finance. In Indonesia, this gap is reflected in two complementary but disconnected systems: Climate Budget Tagging (CBT), which records climate-related public expenditure, and the national Monitoring, Reporting, and Verification (MRV) system, which documents verified GHG emission reductions. The absence of an analytical framework linking these systems constrains the evaluation of climate finance effectiveness and the development of performance-based financing strategies. This dissertation aimed to develop a Climate Finance Transformation Model (CFTM) as a generic decision-support framework for improving climate finance effectiveness through the integration of empirical evidence, mitigation performance, financing characteristics, and institutional capacity. Although the empirical development and demonstration of the model were undertaken using Indonesia's transportation sector, the proposed framework was designed to be conceptually applicable across sectors and adaptable to different national contexts. Specifically, the study sought to: (1) identify international best practices in climate finance; (2) evaluate the effectiveness of realized climate finance by linking expenditure with verified GHG emission reductions; (3) develop a generic climate finance transformation model for identifying appropriate financing pathways for mitigation actions; (4) demonstrate the application of the model using Indonesia's transportation sector, and (5) formulate policy recommendations for strengthening climate finance governance. The research adopted a sequential analytical framework consisting of four interrelated stages. The first stage employed a systematic literature review using the PRISMA approach to examine international best practices in climate finance, with a more detailed assessment of financing strategies that support transport decarbonization. The review synthesized evidence on financing mechanisms, governance arrangements, institutional frameworks, and policy instruments to identify the fundamental components required for an effective climate finance system. The second stage established the empirical foundation of the research through an evaluation of climate finance in Indonesia's transportation sector using realized expenditure and verified GHG emission reduction data for the period 2018–2022. To bridge the gap between financial accounting and mitigation performance, this dissertation developed the Realized Expenditure Attribution Pathway (REAP), a novel analytical framework that systematically attributes realized climate-related expenditure to verified mitigation outcomes. REAP integrates expenditure information derived from climate expenditures with verified emission reductions recorded in the national MRV system, enabling climate finance to be assessed based on observed implementation rather than projected investment requirements. The analysis also introduced the Cost of Verified Emission Reduction (CoVER), which estimates mitigation costs using realized expenditure and verified GHG reductions under actual implementation conditions. The framework further distinguishes between direct mitigation actions that generate measurable emission reductions and enabling mitigation actions that establish the institutional and infrastructural conditions necessary for long-term decarbonization. Building upon these empirical findings, the third stage developed the CFTM as a generic analytical framework rather than a sector-specific model. The model integrates financing requirements, mitigation potential, implementation timeframe, institutional capacity, and financing instrument characteristics into a multidimensional decision-support framework for identifying appropriate financing pathways for mitigation actions. Through a structured compatibility assessment, the model aligns the characteristics of mitigation interventions with the comparative advantages of available financing instruments. The framework is conceptually independent of any particular sector or country, allowing it to be adapted to different mitigation contexts while maintaining a consistent analytical structure. The fourth stage demonstrated the empirical implementation of the CFTM using Indonesia's transportation sector as a case study. The model successfully identified financing pathways that correspond to the characteristics of individual mitigation actions and generated policy recommendations for strengthening climate finance governance through improved institutional coordination, greater integration of public and private finance, expanded use of blended and market-based financing mechanisms, and a transition from expenditure-based budgeting toward performance-oriented climate finance management. This dissertation advances the climate finance literature in five principal ways. First, it shifts the focus of climate finance evaluation from estimating investment needs to assessing how realized climate finance contributes to verified GHG emission reductions. Second, it introduces the REAP framework to systematically link realized climate expenditure with verified GHG emission reductions. Third, it develops the CoVER approach to estimate mitigation costs based on observed implementation rather than projected assumptions. Fourth, it proposes the CFTM as a generic decision-support framework, empirically demonstrated in Indonesia's transportation sector and adaptable across sectors and national contexts. Finally, it establishes an evidence-based climate finance policy architecture to strengthen financing governance and support more effective climate mitigation. Overall, the findings demonstrate that improving climate finance effectiveness requires more than increasing financial resources. It depends on aligning financing instruments with mitigation characteristics, institutional readiness, and implementation requirements within a coherent governance framework. The central conclusion of this dissertation is that public finance initiates climate finance transformation, institutional capacity enables its implementation, and diversified finance sustains its long-term effectiveness. Keywords: climate finance transformation; decarbonization; greenhouse gas mitigation; realized expenditure; verified emission reduction

2026-01-01T00:00:00Z Anwar, Rahma Isartina http://repository.ipb.ac.id/handle/123456789/177949 2026-08-10T02:59:06Z 2026-01-01T00:00:00Z

Pengembangan Antigen Vaksin Berbasis Virus Like Particles (VLPs) Penyakit Mulut dan Kuku (PMK) dengan Karakter Genetik Virus di Indonesia Anwar, Rahma Isartina Penyakit mulut dan kuku (PMK) merupakan penyakit virus yang sangat menular pada ternak berkuku genap seperti sapi, kambing, domba dan babi. Indonesia kembali menghadapi wabah PMK pada tahun 2022. Saat ini telah berkembang vaksin PMK berbasis Virus Like Particles (VLPs) yang dapat dijadikan alternatif vaksin inaktif. Tujuan penelitian ini antara lain mengembangkan kandidat antigen vaksin PMK berbasis VLPs dengan karakter genetik virus PMK yang mewabah di Indonesia sehingga akan mewujudkan kemandirian produksi vaksin nasional. Analisis molekular dan pemodelan epitop menunjukkan gen VP1 memiliki variasi yang lebih tinggi dibandingkan dengan gen VP2 dan VP3. Variasi tersebut juga menyebabkan perbedaan sekuen peptida epitop yang berikatan dengan sel B dan sel T. Gen VP1, VP2 dan VP3 berhasil dikonstruksi dan diekspresikan pada E. coli BL21 (DE3). Uji in vivo kandidat antigen vaksin di mencit menunjukkan antibodi spesifik PMK terbentuk pada kelompok injeksi VP2 dan VP3. Pengukuran konsentrasi IFN? dan IL-22 menunjukkan bahwa terjadi peningkatan konsentrasi sitokin tersebut pada perlakuan injeksi vaksin protein kapsid. Hal tersebut menunjukkan injeksi dengan protein kapsid mampu meningkatkan respon imun spesifik dan non spesifik.

2026-01-01T00:00:00Z