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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Indonesian Journal of Statistics and Its Applications Jurnal Agro Ekonomi Xplore: Journal of Statistics
Rahma Anisa
Department of Statistics, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Other

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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

Abstract

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.
ANALISIS AMMI DENGAN RESPON GABUNGAN PADA UJI STABILITAS TANAMAN PADI GOGO DI KABUPATEN PACITAN Abdullah Ilman Fahmi; Rahma Anisa; Anang Kurnia; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
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.v3i1.173

Abstract

Gogo rice is one of the results of various rice cultivation development by planting in a dry land. Gogo rice is expected to give yield a better production of paddy in dry rice fields. The varieties Inpago 7, Inpago 8, Inpago 8 IPB, Inpago 9, Inpago 10, Situ Gintung, Situ Patenggang, Situ Bagendit, Gajah Mungkur, Slengreng TG, Slegreng GK, Srijaya, Towuti, Merah Wangi, dan Inpari 24 were used in this study. This study aims to identify the Gogo rice varieties that are stable and superior in six Pacitan Garden Experimental Plant locations based on a combined response using the AMMI method. The AMMI analysis combines an additive variety analysis as the main effects of treatment with multiple principle component analysis by bilinier modeling for interaction effect. This study used two combined responses, which described the plant productivity and the resistancy. The result of this study explained that some varieties, Inpago 8, Inpago 10, and Situ Patenggang, were stable varieties in all planting location based on the combined responses. According to productivity stability and plant resistancy superior gogo rice variety is Inpago 8 and Inpago 10.
CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH Rahma Anisa; Bagus Sartono; Pika Silvianti; Aam Alamudi; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
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.v3i1.198

Abstract

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study. The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.
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

Abstract

 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.
Seleksi Peubah menggunakan Algoritme Genetika pada Data Rancangan Faktorial Pecahan Lewat Jenuh Dua Taraf Ani Safitri; Rahma Anisa; Bagus Sartono
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (606.618 KB) | DOI: 10.29244/xplore.v10i1.473

Abstract

In certain fields, experiments involve many factors and are constrained by costs. Reducing runs is one of the solutions to reduce experiment costs. But that can cause the number of runs to become less than the number of factors. This case of experimental design also is known as a supersaturated design. The important factors in this design are generally estimated by involving variable selection such as forward selection, stepwise regression, and penalized regression. Genetic algorithm is one of the methods that can be used for variable selection, especially for high dimensional data or supersaturated design. This study aims to use a genetic algorithm for variable selection in the supersaturated design and compare the genetic algorithm results with a stepwise regression which is generally used for a simple design. This study also involved fractional factorial design principles. The result showed that the main factors and interactions of the genetic algorithm and stepwise regression were quite different. But the principle was the same because the variables correlated. The genetic algorithm model had a smaller AIC and BIC and all of the main factors and interactions which had chosen were significant on the 0.1%. Therefore genetic algorithm model was chosen although computation time was much longer than stepwise regression.
PENDUGAAN FAKTOR – FAKTOR YANG MEMENGARUHI KASUS STUNTING DI JAWA BARAT TAHUN 2021 MENGGUNAKAN REGRESI SPASIAL BINOMIAL NEGATIF Anik Djuraidah; Mely Amelia; Rahma Anisa
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.v20i1.26984

Abstract

Stunting is a childhood growth and development disorder characterized by below-normal height.  West Java, with its stunting rate of 24.5 percent, is one of the provinces included in the top 12 priority provinces in implementing the National Action Plan to Accelerate Stunting. Stunting cases are count data and their occurrence is rare. The analysis for the count data is Poisson regression with the assumption that equidispersion must be met. One way to overcome overdispersion is to use negative binomial regression. This study aimed to determine predictors/factors affecting stunting cases in West Java province in 2021 using negative binomial spatial regression. The data in this study comes from the publication of the West Java Health Service and the West Java Central Statistics Agency in 2021 with districts/cities as the object of observation. There is a spatial effect in the stunting data, so the spatial regression model is suitable. The results show that there is an overdispersion in the Poisson regression. The spatial effect test shows that there is a spatial dependence on the response variable and some predictors. The negative spatial autoregressive binomial is the best model with the lowest AIC value. The factors that have a significant effect are the percentage of infants aged less than six months who are breastfed, the percentage of food processing establishments that meet the requirements, and the percentage of infants with low birth weight.
Co-Authors Aam Alamudi Abdullah Ilman Fahmi Aidi, Muhammad N Anang Kurnia Ani Safitri Anik Djuraidah Aurellia Layvieda Bagus Sartono Dian Kusumaningrum Haryastuti, Rizqi I Made Sumertajaya Indonesian Journal of Statistics and Its Applications IJSA Mely Amelia Pasaribu, Sahat M. Pika Silvianti Sutomo, Valantino A Valantino Agus Sutomo