cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota bogor,
Jawa barat
INDONESIA
FORUM STATISTIKA DAN KOMPUTASI
Published by Institut Pertanian Bogor
ISSN : 08538115     EISSN : -     DOI : -
Core Subject : Education,
Mathematics
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.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol. 16 No. 2 (2011)" : 5 Documents clear
PENGGUNAAN ALGORITMA SIMULATED ANNEALING UNTUK MENYELESAIKAN TEKA-TEKI BINARY DAN SUDOKU ( Solving Binary and Sudoku Puzzles with a Simulated Annealing Algorithm ) Bagus Sartono
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (257.631 KB)

Abstract

Binary and Sudoku puzzles could be seen as optimization problems by using a score  of  rules  violation  as  the  objective  function  which  is  minimized. The simulated annealing algorithm is a good alternative to solve the puzzles.  This paper  describes the  approach  which  implements  the  algorithm  and  presents the  SAS/IML  program  of  it.    Empirical  trials  show  that  the  approach  works well to find the solution of the puzzles in a satisfying run time.  Keywords : meta-heuristic, simulated annealing
MODEL VEKTOR AUTOREGRESSIVE UNTUK PERAMALAN CURAH HUJAN DI INDRAMAYU (Vector Autoregressive Model for Forecast Rainfall In Indramayu ) Dewi Retno Sari Saputro; Aji Hamim Wigena; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.491 KB)

Abstract

There  are  three  regions  of  rainfall  that  has  been  formed,  each  rainfall  regions has a variety of homogeneous and there is a correlation between rainfall stations. In  each  region  can  be determined  rainfall  prediction  model simultaneously.  The model  is  a  model  of  Vector Autoregressive  (VAR)  which  is  an extension  of  the autoregressive  model  (AR).  Based  on  this  research,  we  can  determine  the  VAR model by lag 1 or VAR (1) for each region. Region 1 (Anjatan and Sumurwatu), region  2  (Salamdarma  and  Gantar)  and  region  3  (Kedokan  Bunder  and Sudimampir), each of which has a Root Mean Square Error Prediction (RMSEP) of  3.93;  5:03;  4:48;  5.3;  2:18  and  3:53.  Correlation  value  of  observations  with predictions of rainfall respectively, 0.71; 0.62; 0:57; 0:59; 0.89, and 0.91.  Keywords: AR, VAR, RMSEP, correlation
REGRESI KUADRAT TERKECIL PARSIAL MULTI RESPON UNTUK STATISTICAL DOWNSCALING (Multi Response Partial Least Square for Statistical Downscaling) Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (203.349 KB)

Abstract

In  climatology  partial  least  square  regression  (PLSR)  can  be  used  as  an alternative  technique  in  statistical  downscaling  based  on  global  circulation model  (GCM)  output.  PLSR  is  the  technique  to  forecast  not  only  one  response but  also  multi  responses  to  accommodate  the  correlation  among  responses. PLSR is compared to PCR (Principal Component Regression). The results show that PLSR is better than PCR and can be used to forecast rainfall simultaneously in more than one rainfall stations relatively as well as in one station.  Keywords: statistical downscaling, PLSR, PCR, multi responses
PENDETEKSIAN PERILAKU HERDING PADA PASAR SAHAM INDONESIA DAN ASIA PASIFIK (Detection of Herding Behavior on Indonesia and Asia Pacific Stock Market) . Gunawan; Hari Wijayanto; Noer Azam Achsani; La Ode Abdul Rahman
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (461.889 KB)

Abstract

Herding  Behavior  is  an  irrational  investor  behavior, because  investors  do  not make investment decisions based on economic fundamentals of  risky assets, but based  on  others  investor  in  the  same  condition,  or  following  market  consensus. Herding  behavior  indications  can  be  seen  from  relation  between  dispersion  of stock  return  (Cross  Sectional  Absolute  Deviation,  CSAD)  and  market  portfolio return. If herding behavior exist, CSAD increases lower than increase of market portfolio  return  moreover,  CSAD  will  decrease  even  though  market  portfolio return increases. Herding behavior in stock market can trigger mislead in stock pricing  because  is  bias  among  investors  in  analyzing  risk  and  return.  To understand  relationship  between  CSAD  and  market  portfolio  return  in  some conditions, Quantil regression is used. Result gained from this research is that in Indonesian  and  global  Asia  Pacific  stock  market,  herding  behavior  occurs  in  a market stress condition, whereas in normal condition or in condition of very high stock return, investor behavior tends to be more rational.  Keywords : herd behavior, Quanrtile regression, CSAD
KAJIAN SIMULASI KETAKNORMALAN PENGARUH ACAK DAN BANYAKNYA DERET DATA LONGITUDINAL DALAM PEMODELAN BERSAMA (JOINT MODELING) (Simulation Study of Random Effects Nonnormality and Number of Longitudinal Data Series in Joint Modeling) Indahwati .; Aunuddin .; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 2 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (335.827 KB)

Abstract

Joint modeling is intended to model longitudinal response process that affect the other primary response based on  assumption that both  processes induced by the same random effects. One of the assumptions that must be met in joint modeling is  normality  of  random  effects  and  intra-subject  error.  The  simulation  results show that the robustness of parameter estimates of joint model to the assumption of  random  effects  normality  can  be  achieved  by  increasing  the  frequency  of longitudinal observations.  Keywords:  longitudinal data,  joint modeling, robust

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2011 2011


Reset
Filter By Issues
All Issue Vol. 20 No. 2 (2015) Vol. 18 No. 1 (2013) Vol. 17 No. 2 (2012) Vol. 17 No. 1 (2012) Vol. 16 No. 2 (2011) Vol. 16 No. 1 (2011) Vol. 15 No. 2 (2010) Vol. 15 No. 1 (2010) Vol. 14 No. 2 (2009) Vol. 14 No. 1 (2009) Vol. 13 No. 1 (2008) Vol. 12 No. 2 (2007) Vol. 12 No. 1 (2007) Vol. 11 No. 2 (2006) Vol. 11 No. 1 (2006) Vol. 10 No. 2 (2005) Vol. 10 No. 1 (2005) Vol. 9 No. 2 (2004) Vol. 8 No. 2 (2003) Vol. 8 No. 1 (2003) Vol. 7 No. 2 (2002) Vol. 6 No. 2 (2001) Vol. 6 No. 1 (2001) More Issue