Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Jurnal Teknik Industri EKSAKTA: Journal of Sciences and Data Analysis Media Statistika Perfecting a Video Game with Game Metrics Jurnal Teknik Industri
Anna Chadidjah
Statistics Department, Universitas Padjadjaran
Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Industrial & Manufacturing Engineering Materials Science & Nanotechnology

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Optimal Retention for a Quota-Share Reinsurance Noviyanti, Lienda; Soleh, Achmad Zanbar; Chadidjah, Anna; Rusyda, Hasna Afifah
Jurnal Teknik Industri Vol 20, No 1 (2018): June 2018
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.882 KB) | DOI: 10.9744/jti.20.1.25-32

Abstract

The Indonesian Financial Services Authority (OJK) has instructed all insurance providers in Indonesia to apply a mandatory tariff for property insurance. The tariff has to be uniformly applied and the rule of set the maximum and minimum premium rates for protection against losses. Furthermore, the OJK issued the new rule regarding self-retention and domestic reinsurance. Insurance companies are obliged to have and implement self-retention for each risk in accordance with the self-retention limits. Fluctuations of total premium income and claims may lead the insurance company cannot fulfil the obligation to the insured, thus the company needs to conduct reinsurance. Reinsurance helps protect insurers against unforeseen or extraordinary losses by allowing them to spread their risks. Because reinsurer chargers premium to the insurance company, a properly calculated optimal retention would be nearly as high as the insurer financial ability.  This paper is aimed at determining optimal retentions indicated by the risk measure Value at Risk (VaR), Expected Shortfall (ES) and Minimum Variance (MV). Here we use the expectation premium principle which minimizes individual risks based on their quota share reinsurance. Regarding to the data in an insurance property, we use a bivariate lognormal distribution to obtain VaR, ES and MV, and a bivariate exponential distribution to obtain MV. The bivariate distributions are required to derive the conditional probability of the amount of claim occurs given the benefit has occurred.
The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali Yosep Oktavianus Sitohang; Yudhie Andriyana; Anna Chadidjah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.7293

Abstract

In order to achieve a targeted number of foreign tourist arrivals set by the Indonesian government in 2017, we need to predict the number of foreign tourist arrivals. As a major tourist destination in Indonesia, Bali plays an important role in determining the target. According to the characteristic of the tourist arrivals data, one shows that we need a more flexible forecasting technique. In this case we propose to use a Support Vector Machine (SVM) technique. Furthermore, the effects of noise components have to be filtered. Singular Spectrum Analysis (SSA) plays an important role in filtering such noise. Therefore, the combination of these two methods (SSA-SVM) will be used to predict the number of foreign tourist arrivals to Bali in 2017. The performance of SSA-SVM is evaluated via simulation studies and applied to tourist arrivals data in Bali. As the results, SSA-SVM shows better performances compare to other methods.
Utilization Copula in Determination of Shallot Insurance Premium Based on Regional Harvest Results Hasna Afifah Rusyda; Achmad Zabar Soleh; Lienda Noviyanti; Anna Chadidjah; Fajar Indrayatna
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 1, ISSUE 2, August 2020
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol1.iss2.art11

Abstract

Abstract: Shallot is one of the highest-yielding horticultural crops in Indonesia and has the tendency to increase the profits of farmers in Indonesia. But until now in Indonesia there is no insurance for horticultural crops other than corn, whereas the shallot farmers face various sources of risk such as weather changes, pest attacks, or other technical factors that ultimately lead to uncertainty of agricultural yields (revenue risk). To overcome this loss, insurance companies can make products based on shallot yields and shallot market prices. Therefore it is essential to grasp the distribution of risk variables (shallot yields and shallot market prices) that interact simultaneously, not separate from one another. Omitting dependencies among risk variables can cause biased risk estimation. Copula can model the non-linear dependencies and can identify the structure of the dependencies between variables. The suitable copula for modeling yield and price risk of shallot is simulated to compute the premium. Result show that clayton copula is suitable for dependence modelling between risk variables.
Optimal Retention for a Quota-Share Reinsurance Lienda Noviyanti; Achmad Zanbar Soleh; Anna Chadidjah; Hasna Afifah Rusyda
Jurnal Teknik Industri Vol. 20 No. 1 (2018): June 2018
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.882 KB) | DOI: 10.9744/jti.20.1.25-32

Abstract

The Indonesian Financial Services Authority (OJK) has instructed all insurance providers in Indonesia to apply a mandatory tariff for property insurance. The tariff has to be uniformly applied and the rule of set the maximum and minimum premium rates for protection against losses. Furthermore, the OJK issued the new rule regarding self-retention and domestic reinsurance. Insurance companies are obliged to have and implement self-retention for each risk in accordance with the self-retention limits. Fluctuations of total premium income and claims may lead the insurance company cannot fulfil the obligation to the insured, thus the company needs to conduct reinsurance. Reinsurance helps protect insurers against unforeseen or extraordinary losses by allowing them to spread their risks. Because reinsurer chargers premium to the insurance company, a properly calculated optimal retention would be nearly as high as the insurer financial ability.  This paper is aimed at determining optimal retentions indicated by the risk measure Value at Risk (VaR), Expected Shortfall (ES) and Minimum Variance (MV). Here we use the expectation premium principle which minimizes individual risks based on their quota share reinsurance. Regarding to the data in an insurance property, we use a bivariate lognormal distribution to obtain VaR, ES and MV, and a bivariate exponential distribution to obtain MV. The bivariate distributions are required to derive the conditional probability of the amount of claim occurs given the benefit has occurred.
ANALISIS PERBANDINGAN KINERJA CART KONVENSIONAL, BAGGING DAN RANDOM FOREST PADA KLASIFIKASI OBJEK: HASIL DARI DUA SIMULASI Yogo Aryo Jatmiko; Septiadi Padmadisastra; Anna Chadidjah
MEDIA STATISTIKA Vol 12, No 1 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.528 KB) | DOI: 10.14710/medstat.12.1.1-12

Abstract

The conventional CART method is a nonparametric classification method built on categorical response data. Bagging is one of the popular ensemble methods whereas, Random Forests (RF) is one of the relatively new ensemble methods in the decision tree that is the development of the Bagging method. Unlike Bagging, Random Forest was developed with the idea of adding layers to the random resampling process in bagging. Therefore, not only randomly sampled sample data to form a classification tree, but also independent variables are randomly selected and newly selected as the best divider when determining the sorting of trees, which is expected to produce more accurate predictions. Based on the above, the authors are interested to study the three methods by comparing the accuracy of classification on binary and non-binary simulation data to understand the effect of the number of sample sizes, the correlation between independent variables, the presence or absence of certain distribution patterns to the accuracy generated classification method. Results of the research on simulation data show that the Random Forest ensemble method can improve the accuracy of classification.
Co-Authors Achmad Zabar Soleh Achmad Zanbar Soleh Achmad Zanbar Soleh, Achmad Zanbar Fajar Indrayatna Hasna Afifah Rusyda Lienda Noviyanti Septiadi Padmadisastra Yogo Aryo Jatmiko Yosep Oktavianus Sitohang Yudhie Andriyana