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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Tropical Diversity
Saniyawati, Fang You Dwi Ayu Shalu
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Spatial Panel Regression Modelling of Rainfall in Indonesia Saniyawati, Fang You Dwi Ayu Shalu; Astutik, Suci; Pramoedyo, Henny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29882

Abstract

Rainfall is amount of water that falls to the earth's surface in the form of rain during a certain period of time, usually measured in millimeters. Rainfall data in Indonesia usually includes temporal and spatial dimensions, so the appropriate method for its analysis is spatial panel regression analysis. This study aims to identify factors that influence the amount of rainfall in Indonesia. This type of research is quantitative using secondary data from the central statistics agency website. The predictor variables used include air temperature, sunshine radiation, humidity, wind speed, and air pressure, while the response variable is amount of rainfall in 34 provinces in Indonesia. Spatial panel regression analysis is carried out using maximum likelihood estimation, which is used to estimate the regression coefficient and intercept that maximizes the likelihood of the existing data. Based on the lagrange multiplier test, spatial autocorrelation was found in the lag, so the appropriate model is SAR-FE. This model can overcome spatial autocorrelation by taking into account spatial interactions between locations, as well as controlling unobserved heterogeneity through fixed effects. The results show that sunshine radiation, humidity, and wind speed have significant effect on the amount of rainfall in Indonesia. The AIC value of SAR-FE model (-4.352594×〖10〗^(-13)) is smaller than SEM-FE model (-1.642001×〖10〗^(-12)), indicating that SAR-FE model is better at explaining the data.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
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/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
Bioprospeksi: Eksplorasi Senyawa Aktif Penurun Penderita Stroke dari Organisme Laut Putri, Radwa Fathin H; Yufani, Rofika An; Saniyawati, Fang You Dwi Ayu Shalu; Safitri, Anna
Journal of Tropical Diversity Vol 1 No 1 (2025): Februari
Publisher : Penerbit Brainy Bee

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64283/jotdiverse.2025.1(1):1

Abstract

Stroke is a deadly disease in Indonesia. Meanwhile, marine organisms can contribute to providing active compounds for anti-stroke. The purpose of this review is to provide an overview of stroke sufferers in Indonesia and to identify potential active compounds from marine organisms that can treat or prevent stroke. The writing method was carried out by tracing the literature related to stroke and organisms that produce anti-stroke active compounds through Google Scholar. After collecting data from various literature, the next step was to process the data using simple statistics. Marine organisms that potentially contain anti-stroke active compounds are sea cucumbers (DHA, Docosahexaenoic acid), sea urchins (flavonoids), starfish (flavonoids), and jellyfish (amino acids), while marine organism biomarkers are biological responses of all living things in the sea to pollution and environmental pressures. This review is expected to be the beginning of the development of bioprospecting based on omics: metabarcoding and metagenomics, transcriptomics, proteomics, glycomics, lipidomics, and metabolomics.
Co-Authors Henny Pramoedyo Irsandy, Diego Lusiana, Evelin Dewi Mashfia, Fidia Raaihatul Putri, Radwa Fathin H Rahmi, Nur Silviyah Risda, Intan Fadhila Safitri, Anna Suci Astutik Susanto, Mohammad Hilmi Yufani, Rofika An