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All Journal Agromet Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Communication in Biomathematical Sciences
Indratno, Sapto Wahyu
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Implementation of Bayesian Model Averaging Method to Calibrate Monthly Rainfall Ensemble Prediction over Java Island Muharsyah, Robi; Hadi, Tri Wahyu; Indratno, Sapto Wahyu
Agromet Vol. 34 No. 1 (2020): JUNE 2020
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1296.784 KB) | DOI: 10.29244/j.agromet.34.1.20-33

Abstract

Bayesian Model Averaging (BMA) is a statistical post-processing method for producing probabilistic forecasts from an ensemble prediction in the form of predictive Probability Density Function (PDF). BMA is commonly used to calibrate Ensemble Prediction System (EPS) in a shorter-range forecast. Here, we applied the BMA for a longer forecast at a seasonal interval. This study aimed to develop the implementation of the BMA method to calibrate the seasonal forecast (long range) of monthly rainfall from the RAW output of the EPS European Center for Medium-Range Weather Forecasts (ECMWF) system 4 model (ECS4). This model was calibrated with observational data from 26 stations over Java Island in 1981-2018. BMA predictive PDF was generated with a gamma distribution, which was obtained based on two training schemes, namely sequential (BMA-JTS) and conditional (BMA-JTC) training windows. Generally, both of BMA-JTS and BMA-JTC were able to produce better distribution characteristics of ensemble prediction than that of RAW model ECS4. Both BMA methods showed a good performance as indicated by a high accuracy, small bias, and small uncertainty to the observed rainfall. Our findings revealed that BMA-JTC was able to improve the quality of probabilistic forecasts of below and above normal events. The improvement was shown in most stations over Java Island, in which the model was a good skill forecast based on Brier Skill Score (BSS).
Cluster analysis of Sumatra Island earthquake distribution Anggraini, Dian; Indratno, Sapto Wahyu; Mukhaiyar, Utriweni; Puspito, Antonius Nanang T.
Desimal: Jurnal Matematika Vol. 7 No. 3 (2024): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v7i3.25108

Abstract

Sumatra Island is highly vulnerable to earthquakes due to multiple seismic sources, including megathrusts, faults, and volcanic activities spanning from Aceh to Lampung. The International Seismological Centre (ISC) has recorded 9,414 earthquakes in Sumatra with magnitudes ranging from 4.0 to 9.1 since 1907. Insufficient preparedness in responding to sudden earthquakes challenges local and central governments in managing impacts. To address this, a risk classification of earthquake-prone areas was conducted using cluster analysis. The "K-means cluster" method identified five earthquake clusters in Sumatra. Cluster 4 has the most events (3,787) but with generally lower magnitudes, resulting in minimal damage. Cluster 2, however, is more concerning due to shallow earthquakes from subduction zones, faults, and volcanoes. This clustering analysis provides critical information for government planning in earthquake risk mitigation and preparedness.
LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX Anggasari, I Gusti Ayu Wulan; Zainuddin, Ahmad Fuad; Indratno, Sapto Wahyu; Yunus, Muhammad Haekal
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0893-0902

Abstract

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.
Linear Mixed Model for Oil Palm Parents Selection Sonhaji, Abdullah; Pasaribu, Udjianna Sekteria; Indratno, Sapto Wahyu; Pancoro, Adi
Communication in Biomathematical Sciences Vol. 8 No. 1 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.1.3

Abstract

The objective of plant breeding is to obtain superior seeds. These seeds originated from parents that can pass their superior traits to their progeny. The observed characteristics of the progeny (phenotype) determined the traits of these seeds. Therefore, we performed a progeny analysis. In this analysis, the data samples were collected from Riau in Sumatera and Kumai in Kalimantan (two locations). The main objective is to find superior parents from these two locations. The superiority of the selected parents lies not only in passing high production traits but also in adaptability (fit) to the diversity or variability of the environment or locations. This analysis calculates the General Combining Ability (GCA) values for both male and female parents using the Linear Mixed Model (LMM). The experimental design, as the source of data, was an alpha lattice design, so the LMM contains locations, replicas, blocks, male and female parents, and the progeny factors. The analyzed phenotype is Fresh Fruit Bunches of third-year production. Since the data sets of the two locations were nonintersect, the model uses the coefficient of parentage (additive relationship matrix) to link both. The results of the GCA analysis showed that selected female parents were 137, 155, 126, 147, and 159 (Dura), and 101, 113, 109, and 117 for male parents. They are among the parents with highly productive progenies. There are also new potential crossings not currently available on the plantation - for example, the crossing 137 x 101 with the additive genetic value of 35.37.
Co-Authors Adi Pancoro Anggasari, I Gusti Ayu Wulan Dian Anggraini Muhammad Haekal Yunus Muharsyah, Robi Pasaribu, Udjianna Sekteria Puspito, Antonius Nanang T. Sonhaji, Abdullah Tri Wahyu Hadi, Tri Wahyu Utriweni Mukhaiyar Zainuddin, Ahmad Fuad