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INDONESIA
FORUM STATISTIKA DAN KOMPUTASI
ISSN : 08538115     EISSN : -     DOI : -
Core Subject : Education,
Forum Statistika dan Komputasi (ISSN:0853-8115) was published scientific papers in the area of statistical science and the applications. It is issued twice in a year. The papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.
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Articles 5 Documents
Search results for , issue "Vol. 17 No. 1 (2012)" : 5 Documents clear
DETEKSI DINI RISIKO KREDIT MELALUI RATING TRANSITION STOCHASTIC MATRIX DAN VALUE AT RISK (Early Detection of Credit Risk Through Rating Transition Stochastic Matrix and Value at Risk) _ Haryono; Sri Pingit Wulandari; Sri Mumpuni Retnaningsih
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Credit risk is the risk occurs when the debtors fail to meet their obligation in accordance with agreed term to the bank. This research is made to analyze the credit risk for industrial and trade sector in Bank X, both sectors contribute about 80% loan credit. The calculation of the VaR 95% used Markov Chain regular and ergodic and adjusted by macro economic variable which significance influence the movement of those quality rating. The result of Markov chain for industrial sector show that the ability debtor increase for repay the loan in the long run but for trade sector became worst. The VaR 95% results for industrial sector is Rp 2,17 billion or about 3,27% and for trade sector is Rp 4,46 billion or about 2,03% from outstanding credit those sectors. This results is not appropriate with the New Basel Capital Accord which recomennded to allocate capital 8% from outstanding credit to cover credit risk. The calculation of the TVaR 95% for industrial sector is Rp 4,89 billion or about 7,38% and for trade sector is Rp 16,60 billion or about 7,55% from outstanding credit both sectors. For the TVaR 95% portofolio give the results is Rp 18,99 billion or about 6,5% from outstanding credit.Keywords : Credit Risk, Markov chain, Regression, Macroeconomics, VaR, TVaR, Portofolio Risk.
PEMODELAN DATA PANEL SPASIAL DENGAN DIMENSI RUANG DAN WAKTU (Spatial Panel Data Modeling with Space and Time Dimensions) Tendi Ferdian Diputra; Kusman Sadik; Yenni Angraini
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The modeling of spatial panel data is a method of analysis that include the dimension of space and time. In this analysis, the set of data that is required is a combination of cross sections and time series data, that is, either the data observed in each observation location periodically from time to time. On modeling of panel data, there are three approaches, namely pooled least square model, fixed and random effects model. While on modeling of spatial panel data there are several approaches which is a combination of these three approaches in modeling panel data with spatial autoregression model (SAR) and spatial error model (SEM). This research aims to apply a spatial panel data model analysis to include the dimension of space and time in a model. The data that used in this research is GDP, local revenues, a total population and total regional expenditures of ten districts in Jambi province during the years 2000-2008. The results from spatial panel data analysis obtained that model regression of spatial panel data corresponding to the data is panel data models with fixed effect model and spatial error model. From the results of such analysis can also be seen an increase in R2 compared with panel data analysis.Keywords : the modeling of panel data, the modeling of spatial panel data, SAR, SEM
PENGGUNAAN SOCIOGRAM UNTUK MENGIDENTIFIKASI POLA JARINGAN SOSIAL PEMBELAJARAN MANDIRI MAHASISWA (Identification of Social Network of Student’s Independent Learning using Sociogram) Yenni Angraini; Bagus Sartono; Dian Kusumaningrum
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

This paper presents a useful tool to help universities to increasing the level of their graduate outcome by using the information about social network among students. Such a quantitative tool is a sociogram which depicts how students interact with others. The graph can be easily generated when the pattern of the connectivity among individuals is known. We apply sociogram to portray the network of a class of students in Department of Statistics – Bogor Agricultural University which represent the way they interact when they want to discuss the academic related problems. We found some interesting results are practically valuable for the one who is responsible to the study result of the students. Some results are not new, but this approach could provide more informative features than conventional tables or such things.Keywords : sociogram, social network analysis
PENERAPAN RANTAI MARKOV PADA PENGEMBANGAN UJI KETERDUGAAN KUNCI (Markov Chain Technique in Key Predictability Test Development) Sari Agustini Hafman; Anang Kurnia; Agus Buono
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

One Time Key (OTK) system with key from alphabetical sequences is one of symmetric encryption algorithm that used in Indonesia to protect secret information. Alphabetic sequences in OTK system must be cryptographically secure pseudorandom sequences.  OTK system in Indonesia only tested by overlapping m-tuple test developed by Marsaglia (2005). Overlapping m-tuple test doesn’t check the unpredictability of alphabetical sequences, it just tests distribution form and indpendency of alphabetical sequences. So, an alphabetical sequence in OTK system cannot be used in cryptography application by the reason of unpredictability sequence is unknown.  Because some of Pseudorandom Number Generator (PRNG) algorithm based on block cipher algorithm that has markovian properties, markov chain model used to detect predictability alphabetical sequences. Data in this study consists of two data sources i.e. simulation data that generated from four classes PRNG and OTK system keys in 2005 that used in three communication units of foreign ministry. Simulation data is used to develop key predictability test methodology by find predictability threshold value based on characteristic of match level.  OTK system keys will be predictability tested by comparing characteristic of match level with threshold value that is obtained from simulation data. The first result of this study shows the alphabetical sequence generated by first, second and fourth PRNG class can't be modeled with first-order markov chain until third-order. The third PRNG class, except PRNG LCG1, LCG2, coveyou, rand and randu, also can't be modeled with first order markov chain until third-order. Sequence generated by  LCG2, coveyou, rand and randu are not fit for use in cryptography because it has a high probability to be modeled by  high orders of markov chain (above the order of three). The second result obtains predictability threshold value  with markov chains based on the minimum and maximum match level on the second-order and third-order. The last result shows the size of training data must be greater than the size of the observation data with the best ratio between the size of training data with observational data is 100: 10. The results of testing using 10 times repeated shows that the match level average of the OTK system key match on the all of three-order less than  4.5 x 10-2, so the OTK system the is feasible to  secure information in three communication units. Keywords: One Time Key (OTK), markov chain, PRNG, probability transition, match level 
PENDEKATAN KEKAR UNTUK MODEL BERSAMA (JOINT MODEL) ATAS DASAR SEBARAN t (A Robust Approach for Joint Model Based on t Distribution) _ Indahwati; _ Aunuddin; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Existing methods for joint modeling are usually based on normality assumption of random effects and intra subject errors. We propose a joint model based on t distribution of the intra subject errors  to improve robustness of the estimation. Our model consists of two submodels: a mixed linear mixed effects model for the longitudinal data, and a generalized linear model for continuous/binary primary response. The proposed method is evaluated by means of simulation studies as well as application to HIV data. Keywords:  joint modeling, longitudinal data, robust, t distribution

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