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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series Jurnal Varian Jurnal Statistika dan Aplikasinya Sewagati
Oktaviana, Pratnya Paramitha
Departemen Aktuaria Institut Teknologi Sepuluh Nopember Surabaya
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Pemodelan Produk Domestik Regional Bruto Sektor Pertanian dan Penyaluran Kredit menggunakan Two Stage Least Square Prilyandari Dina Saputri; Pratnya Paramitha Oktaviana
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07101

Abstract

Green economy is a concept relating to economic development which aimed to improve people's welfare by paying attention to environmental conditions. One main pillar of a green economy is economic growth which can be calculated through GDP (Gross Domestic Product). Financial institutions can play an important role in raising economic growth through optimal credit allocation. This study aims to identify the causal relationship between credit allocation from financial institutions and regional economic growth (GRDP), particularly in the green industry sector. The causal relationship that influences each other between credit allocation and Gross Regional Domestic Product (GRDP) in the agricultural, hunting, forestry, and fisheries sectors can be analyzed using the simultaneous two stage least square equation. The variables that significantly affect credit allocation are the percentage of NPL and GRDP, while the variables that significantly affect GRDP are the area of agricultural land and credit allocation. A significant causal relationship between credit distribution and GRDP shows that financial institutions can play a role in raising the growth of the green sector economy through credit allocation, especially in the green sector.
Analisis Curah Hujan Ekstrem Daerah Provinsi Papua Barat Menggunakan Max Stable Process Model Schlather Alya Azzahra; Pratnya Paramitha Oktaviana; R. Mohamad Atok
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27433

Abstract

Data from Badan Pusat Statistik (BPS) in 2021 notes the province of West Papua as the province with the 5th highest rainfall in Indonesia with a rainfall of 3,811 mm. The province also recorded 268 rainy days, the most amongst all provinces in the country. The excess amount of rain is one of the causes of disasters such as floods. This research uses rainfall data from the Regencies of Manokwari, Fakfak, and Kaimana. The method used is Spatial Extreme Value particularly Schlather's Model of the Max Stable Process. The data used is hourly rainfall for the period of 13 March 2022 to 17 October 2022 with the proportion of training and testing data respectively 85.84% and 14.16%. Extreme data collection was carried out using the Block Maxima method with a fitting to the Generalized Extreme Value (GEV) distribution before being transformed into the Frechet Z margin units. The calculation of the extreme coefficient resulted in a value between 1.4 to 1.85, indicating a relationship between the locations. Next, the best trend surface model was determined, which involves latitude coordinates for the calculation of the location parameter and both longitude and latitude coordinates for the calculation of the scale parameter. The spatial parameter estimation is carried using the powered exponential correlation function. Then, model validation was carried out using MAPE based on a comparison of return levels and testing data. The MAPE values obtained was 22.61% for the BFGS iteration method. The final step is to calculate return levels for periods of 2, 4, 6, 8, and 10 years ahead. All the results were categorized under very heavy rain. These results can be used by related parties to carry out disaster mitigation efforts.
Estimasi Return Level pada Pemodelan Spatial Extreme Value Kecepatan Arus Laut Bali dengan Pendekatan Max-Stable Process Model Smith dan Brown-Resnick Nyoman Gede Trisna Sanjaya; Pratnya Paramitha Oktaviana; Galuh Oktavia Siswono
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27436

Abstract

Bali is the world's second most popular tourist destination in 2023. One of the best tourisms is the beauty of its coasts. Even though it is the best tourism destination, it is not uncommon for disasters to occur in the coastal areas of Bali. One important factor in the occurrence of coastal disasters from waters such as tidal flooding and abrasion is ocean currents. Spatial analysis of sea currents velocity was carried out using the Smith and Brown-Resnick Max-Stable Process Approach. The purpose of this study was to determine parameter estimation and comparison of the results of Spatial Extreme Value modeling with the Smith and Brown-Resnick Max-Stable Process approach, and to determine the Return Level of Bali Sea current velocity for the same period after data testing with the best model. The data used is daily data for the period March 2, 2017 to December 30, 2020. Extreme data selection with Block Maxima uses 14 daily blocks, so there are 100 blocks for each water location. The proportion of training and testing data is 80:20. The training data follows the Generalized Extreme Value distribution and has no pattern trend (stationary). The results of the extremal coefficient measurements ranged from 1.18604 to 1.59485 indicating a fairly strong dependency between locations. The best trend surface model is a model that only has longitude coordinates on the location parameter and latitude on the scale parameter. The estimated value of the spatial parameters of the Smith model tends to be greater than that of the Brown-Resnick model. The Root Mean Square Error and Mean Absolute Percentage Error for the Smith model are 0.15503 and 7.75076%. Meanwhile, the Brown-Resnick model is 0.29576 and 14.12131%. Return Level values for the same period after data testing are classified as strong currents and are respectively 1.20586 m/s, 1.63592 m/s, 1.51322 m/s and 2.13233 m/s for Serangan, Gianyar, Nusa Dua, and Nusa Lembongan Waters. Information on estimated Return Levels is expected to be a consideration that can be used by related agencies such as the Coastal and Marine Resources Management Agency (BPSPL) and the Bali Province Regional Disaster Management Agency (BPBD) as a coastal disaster mitigation effort to make it more effective, efficient and on target.  
Analisis Risiko Kebakaran Hutan Dan Lahan Daerah Kalimantan Barat Menggunakan Metode Regresi Logistik Dengan Pendekatan Generalized Extreme Value Radit Candra Nugroho; Pratnya Paramitha Oktaviana
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 1 (2023): SEPTEMBER, 2023
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i1.27474

Abstract

Forests in Indonesia have been reduced by half due to fires. Forest and land fires often occur during the long dry season in places such as the island of Borneo. West Kalimantan is an area passed by the equator which is directly above the Pontianak area. The main effect is to make West Kalimantan a tropical area with high air temperatures so that forest and land fires often occur. This study aims to obtain the results of the probability of land and forest fires in each district in West Kalimantan. The method used is binary logistic regression analysis with response variables in the form of data categories based on spatial data and analysis of extreme values with Generalized Extreme Value (GEV). Spatial analysis uses the help of ARCGIS software in processing raster data (grid cells). The data used is data on maximum temperature and maximum wind speed taken from October 7, 2021 to October 31, 2022 from the official NASA website. The spatial data used in this study is forest and land fire vulnerability data taken from the BNPB website in the form of raster data. The results of logistic regression analysis found that the maximum temperature variable has a negative relationship with the response variable, while the maximum speed of wind variable has a positive relationship with the response variable. The temporal probability of the resulting GEV is getting higher with a longer period of years ahead. The probability of forest and land fires is obtained by multiplying the log probability by the GEV temporal probability. In this study, it was found that the highest chance of forest and land fires occurring in Sanggau Regency was suspected to occur due to an increase in temperature every year.  
Co-Authors Achmad Nuruddin Syaifulloh Alya Azzahra Checellya Maitriani Dias Setya Prayogo Dwiatmono Agus Widodo Etienne, Marie-Pierre Fadhela Anindya Rahmadani Fithriasari, Kartika Galuh Oktavia Siswono Gloria Amada Imam Safawi Ahmad, Imam Safawi Irdo Jasmadi Meidyta Sinantryana Widyaswari Mohamad Atok, Raden Nur Afifah Amalia Nur Iriawan Nyoman Gede Trisna Sanjaya Prilyandari Dina Saputri R. Mohamad Atok Radit Candra Nugroho Soehardjoepri Soehardjoepri Wawan Hafid Syaifudin Wiwiek Setya Winahju