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Determination of Critical Productivity Level on Cluster-Based Area of Rice Crop Insurance in Java Haryastuti, Rizqi; Pasaribu, Sahat M.; Aidi, Muhammad N; Sumertajaya, I Made; Sutomo, Valantino A; Kusumaningrum, Dian; Anisa, Rahma
Jurnal Agro Ekonomi Vol 39, No 1 (2021): Jurnal Agro Ekonomi
Publisher : Pusat Sosial Ekonomi dan Kebijakan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jae.v39n1.2021.1-13


IndonesianKesenjangan tingkat produktivitas padi di Indonesia cukup besar yang di antaranya dipengaruhi oleh luasnya wilayah pertanaman. Hal ini berdampak pada desain dan penerapan model Asuransi Usaha Tani Padi (AUTP) berbasis produktivitas. Perluasan klaster pada tingkat provinsi diperkirakan dapat mengurangi keragaman produktivitas di tingkat wilayah kota/kabupaten sebagai risiko dasar pemanfaatan skema AUTP berbasis klaster. Klaster, sebagai wilayah atau zona, diperlukan untuk menentukan indeks kritis produktivitas yang akurat dalam rangka penghitungan tingkat premi yang tepat. Kajian ini bertujuan untuk menentukan tingkat produktivitas kritis pada lahan padi yang menerapkan skema AUTP. Kajian ini menggunakan analisis statistik dengan pendekatan batas bawah Two Sigma yang dapat dianggap sebagai batas produktivitas kritis untuk setiap klaster. Teknik ini memberikan persentase yang rendah atas klaim yang terjadi, serta ekspektasi dan simpangan baku dari risiko dasar kerugian. Tarif premi murni yang diperoleh adalah Rp85.191,18, hampir 2,5 kali lipat lebih kecil dibandingkan dengan menggunakan teknik lain sebagai batas poduktivitas. Hasil kajian ini mengungkapkan bahwa penggunaan skema berbasis klaster lebih baik dari skema berbasis provinsi, sebagaimana ditunjukkan oleh nilai TVaR. Kajian ini menyarankan agar Kementerian Pertanian dapat merancang model AUTP berbasis produktivitas berdasarkan klaster dengan setiap klaster memiliki nilai indeks produktivitas kritis yang berbeda untuk menetapkan tingkat premi yang dikenakan.EnglishThere is a large gap in productivity of paddy in Indonesia which is, among others affected by the area size of crop planting. This condition should influence the design and application model of the rice crop insurance scheme. Developing clusters under the province level is recommended to reduce the heterogeneous productivity as basis risk within regencies/municipalities in improving the area yield index of crop insurance policy in Indonesia. Clusters, as the zone, are necessary to determine accurate critical yield index leading to a more precise premium rate making. This study aims to determine critical productivity level on rice crop insurance area. This study applied statistical analysis using the lower bound of Two Sigma as a critical yield for each cluster. This technique provides a small percentage of claim, and the expectation and standard deviation of basis risk loss. The pure premium rate obtained from the analysis is IDR85,191.18, that is almost 2.5 times less than using other methods as trigger productivity. The analysis result emphasized that the use of the cluster-based scheme is better than the province-based as shown by TVaR value. The study suggests that the Ministry of Agriculture could design the area yield index based on clusters as each cluster will have a different critical productivity index with adjusted premium rate value.
PENERAPAN ANALISIS REGRESI SPLINE UNTUK MENDUGA HARGA CABAI DI JAKARTA Hestiani Wulandari; Anang Kurnia; Bambang Sumantri; Dian Kusumaningrum; Budi Waryanto
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.47


The chili is an important commodity in Indonesia, which has a fairly large price fluctuations. Fluctuations in prices often raises the risk of loss even have contributed to inflation. Chili price data is time series data that is not independent between observations (autocorrelation) and do not spread to normal. In addition, chili price data does not have the diversity of homogeneous data. One method that can be used to predict the pattern of the data is spline regression. The data used in this study is data the average weekly price of chili in Jakarta from January, 2010 to October, 2015. The best spline model is a second order spline models with three knots. The model has a value of Mean Absolute Percentage Error (MAPE) of 9.57% and determination coefficient of 86.41%. The model obtained in this research is already well in predicting the pattern of the chili price, but it was only able to predict well for a period of one month. Prediction chili prices in Jakarta for November are in the range of Rp 35.565. Keywords: chili price, regression, spline.
Determining Critical Yield Index of Area Yield Insurance based on Basis Risk Constraint Valantino Agus Sutomo; Dian Kusumaningrum; Aurellia Layvieda; Rahma Anisa
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p205-219


 Area yield index insurance at district level faces heterogeneous basis risk due to geographical conditions which implies to obtain unprecise critical index . Clustering and zone-based area yield scheme can reduce heterogeneous basis risk that leads to determine the suitable alternative for . On the previous research, we have obtained 7 clusters and 2 level of paddy productivity based on clustering assumption from primary data in Java. The suitable clustering assumption for calculating  is cluster based assumption, which gives the homogeneous paddy productivity under 7 clusters in Java. Therefore, our goal is to develop area yield index at district level (cluster based) with minimize basis risk at certain constraints for paddy farmer productivity in Java Indonesia. There are some methods for calculating  such as mean, median, winsor mean, one sigma, two sigma and  (first quartile) method on the basis risk constraints using confusion matrix. Furthermore, two basis risk constraints are the difference between overpayment and shortfall is not extremely far, and total basis risk does not exceed 20% of its total claim occurrence. Two sigma method has the lowest basis risk, overpayment, and shortfall, but it has lowest pure premium, small probability of claim, and low range of claim. Hence, we consider to use  (first quartile) method as alternative and suitable method to calculate  that satisfied two basis risk constraints. In conclusion, our research provides analytical calculation for area yield index at district level with pure premium as Rp 152,151 using  ( method), which is sufficient to cover the total claim and consistent with the simulation.
Structural Equation Model: Intention To Use Mobile Banking of Bottom of Pyramid Customer Dian Kusumaningrum; Dewi Savitri Saraswati; Seprianus Seprianus
STI Policy and Management Journal Vol 4, No 1 (2019): STI Policy and Management
Publisher : Center for Science and Technology Development Studies, Indonesian Institute of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1554.868 KB) | DOI: 10.14203/STIPM.2019.156


The economy is shifting into the digital economy and to overcome it, the banking industry competes through innovation and digital strategy. Smartphone-based mobile banking is the key component of the digital strategy with 70% of the banks agree to focus their strategy on mass customer segment (PWC, 2017).The purposes of the study are to identify the predicting factors influencing the intention to use mobile banking and empirically validate a model explaining the behavioral intention to use it, especially on the Bottom of Pyramid (BOP) segment. The model used was Structural Equation Model (SEM) based on Partial Least Square (PLS). The data used for developing the model was based on a survey to 100 BOP households.The results of this study show that the variables that have the highest significant effect on BOP’s customer intention to use mobile banking are involuntary barriers, followed by perceived risk, and attitude. This result can be further used by researchers and mobile banking providers to evaluate the existing mobile banking services to improve its contribution in providing better market penetration and more appropriate financial services for BOP and ultimately financial inclusion in Indonesia.Keywords: Mobile Banking, Intention, Structural Equation Model