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 119 Documents
 1   2   3   4   5 
Indeks Harga Saham Gabungan (IHSG) merupakan salah satu indikator yang digunakan pemerintah dalam mengambil kebijakan dalam bidang ekonomi. Selain itu pemerintah menganggap pentingnya pasar modal sebagai alternatif pembiayaan selain perbankan. Fluktuasi yang sangat besar terjadi di pasar bursa, karena setiap transaksi tercatat dengan skala waktu yang kecil sehingga perubahan nilai yang terjadi begitu cepat. Pada kasus ini asumsi kehomogenan ragam tidak terpenuhi. Pada pasar bursa juga memperliha Nirawita Untari; Ahmad Ansori Mattjik; Asep Saefuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 1 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indeks Harga Saham Gabungan (IHSG) merupakan salah satu indikator yang digunakan pemerintah dalam mengambil kebijakan dalam bidang ekonomi. Selain itu pemerintah menganggap pentingnya pasar modal sebagai alternatif pembiayaan selain perbankan. Fluktuasi yang sangat besar terjadi di pasar bursa, karena setiap transaksi tercatat dengan skala waktu yang kecil sehingga perubahan nilai yang terjadi begitu cepat. Pada kasus ini asumsi kehomogenan ragam tidak terpenuhi. Pada pasar bursa juga memperlihatkan adanya pengaruh asimetrik(leverage), yaitu hubungan yang negatif antara perubahan nilai return dengan pergerakan volatilitasnya. Model EGARCH yang memodelkan ragam bersyarat sebagai fungsi log-linear digunakan sebagai fungsi ragam dalam memodelkan nilai harian IHSG, sehingga nilai ragam bersyarat yang diprediksi tidak akan pernah negatif. Model EGARCH terpilih adalah MA(1)-EGARCH(1,1). Model EGARCH terbukti sangat baik dalam memodelkan nilai harian IHSG, tetapi belum cukup baik untuk meramalkan nilai IHSG yang akan datang. Selain ramalan terhadap nilai harian IHSG, pemodelan fungsi ragam juga menghasilkan peramalan terhadap ragam bersyaratnya. Ramalan ragam bersyarat sangat berguna bagi pemegang aset dalam melihat perilaku pergerakan IHSG dan untuk menghitung besarnya resiko memegang suatu aset di masa yang akan datang.
JARINGAN SYARAF TIRUAN DAN ALGORITMA GENETIKA DALAM PEMODELAN KALIBRASI (STUDI KASUS : TANAMAN OBAT TEMULAWAK) Bartho Sihombing; . Erfiani; Utami Dyah Syafitri
FORUM STATISTIKA DAN KOMPUTASI Vol. 16 No. 1 (2011)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.262 KB)

Abstract

The problems in prediction of calibration model are multicolinearity and the number of variables is larger than the number of observations. Principal Component Analysis-Artificial Neural Network-Genetic Algorithm (PCA-ANN-GA) models were applied for the relationship between sample of concentration which is limited and transmittance data which is in large dimensions. A large number of variables were compressed into principal components (PC’s). From these PC’s, the ANN was employed for prediction of concentration. The principal components computed by PCA were applied as inputs to a backpropagation neural network with one hidden layer. The models was evaluated using GA for the best network structure on hidden layer. Root Mean Square Error (RMSE) for 80% training set and 20% testing set are 0.0314 and 0.5225, respectively. Distribution of data according to the percentage of training data and testing data were also very influential to obtain the best network structure with the smallest RMSE achievement. The best model for these methods is two layers Neural Network with eight neuron in the hidden layer.
Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum  dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP)  merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah al Ismah .; Aji Hamim Wigena; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 1 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Aplikasi model kalibrasi di bidang kimia adalah pemodelan hubungan antara kandungan senyawa aktif yang ditentukan dari High Performance Liquid Chromatography (HPLC) dengan bentuk spektrum  dari spektrometer Fourier Transform Infrared (FTIR). Permasalahan utama dalam kalibrasi adalah multikolinear dan pengamatan pencilan. Regresi Kuadrat Terkecil Parsial (RKTP)  merupakan sebuah teknik prediktif yang mampu mengatasi masalah multikolinearitas.. SIMPLS (Straightforward Implementation PLS) adalah algoritma pendugaan RKTP yang  tidak resisten terhadap pengamatan pencilan. Hubert and Brande (2003) mengemukakan algoritma RSIMPLS yang bersifat resisten terhadap pencilan. RSIMPLS dibentuk dari matriks ragam-peragam robust dan regresi linear robust. Pada penelitian ini dilakukan modifikasi fungsi bobot pada  RSIMPLS dengan penduga-M Huber dimana setiap pengamatan akan diberikan nilai bobot kecil  jika jarak robust dan jarak ortogonal pengamatan ke-i melebihi nilai batas yang ditentukan, dan  untuk lainnya. Dengan demikian besar  tidak hanya 0 dan 1, melainkan . Hasil penelitian menunjukkan RMSEP (root mean square error) pada metode modifikasi bobot lebih kecil dibandingkan RSIMPLS
METODE POHON GABUNGAN: SOLUSI PILIHAN UNTUK MENGATASI KELEMAHAN POHON REGRESI DAN KLASIFIKASI TUNGGAL Bagus Sartono; Utami Dyah Syafitri
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (282.023 KB)

Abstract

Classification and regression tree has been a widely used tool in various applied fields due to its capability to give excellent predictive analysis. Later on, ensemble tree appeared to enhance simple tree analysis and deals with some of the weakness found in simple techniques. The ensemble tree basically combines predictions values of many simple trees into a single prediction value. This paper is intended as an introductory article to give a brief overview of the available ensemble tree methods which might be found in detail in a variety of reading materials.
PEMODELAN RESIKO PENYAKIT KAKI GAJAH (FILARIASIS) DI PROVINSI PAPUA DENGAN REGRESI ZERO-INFLATED POISSON Sri Pingit Wulandari; Brodjol Sutijo; Ika Rahmawati
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (428.958 KB)

Abstract

The goverment has established elimination of filariasis tropical disease as one of the priority programs. One of the districts that has become a target is Papua. The total amount of  filariasis victim on every regency/city in Papua district can be assumed to follow a Poisson distribution. So Poisson regression method is a suitable method to know the influence factor of filariasis disease. Poisson regression model assumes equidispersion, that is equality of mean and variance of the response variable. Overdispersion test shows that the variance of the response variable exceeds its mean value. So the model is modified into zeroinflated Poisson (ZIP) regression model (logit and log). ZIP logit regression model shows that the quantity of filariasis victim in every regency/city in Papua district with zero count is influenced by the percentage of household members who sleep inside mosquito net, the percentage of household members who sleep inside insecticide musquito net, and the percentage of house-holds who keep pet (dog/cat/rabbit). While ZIP regression on log model shows that the increasing number of percentage household who keeps their pet will enhance the quantity of filariasis victim  in Papua district as many as two people. Regencies/cities which need to get special attention through an elimination program of filariasis are Asmat, Tolikara, Supiori, Yapen Waropen, and Jayapura city.
There have been two main topics developed by statisticians in a survey, i.e. sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistic Kusman Sadik
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 2 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

There have been two main topics developed by statisticians in a survey, i.e. sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistics for a small area, have unacceptably large standard errors, due to the circumstance of small sample size in the area. The most commonly used models for this case, usually in small area estimation, are based on generalized linear mixed models. Some time happened that some surveys are carried out periodically so that the estimation could be improved by incorporating both the area and time random effects. In this paper we propose a state space model which accounts for the two random effects and is based on two equation, namely transition equation and measurement equation. Based on a evaluation criterion, the proposed hierarchical Bayes estimator turns out to be superior to both estimated best linear unbiased prediction (BLUP) and the direct survey estimator. The posterior variances which measure accuracy of the hierarchical Bayes estimates are always smaller than the corresponding variances of the BLUP and the direct survey estimates.
MODELLING INGREDIENT OF JAMU TO PREDICT ITS EFFICACY Farit Mochamad Afendi; Sulistiyani .; Aki Hirai; Md. Altaf-Ul-Amin .; Hiroki Takahashi; Kensuke Nakamura; Shigehiko Kanaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 2 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (555.672 KB)

Abstract

Jamu is an Indonesian herbal medicine made from a mixture of several plants.  Nowadays, many jamu are  produced commercially by many industries in Indonesia.  Each producer may have their own jamu formula. However, one is certain; the efficacy of jamu is determined by the composition of the plants used.  Thus, it is interesting to model the ingredient of jamu which consist of plants and use it to predict efficacy of jamu.  In this analysis, Partial Least Squares Discriminant Analysis (PLSDA) is used in modeling jamu ingredients to predict  the  efficacy.  It  is  obtained  that  utilizing the prediction of  y ij obtained  from  PLSDA  directly  rather  than  use  it  to calculate probability of jamu i belong to efficacy j and then use the probability to predict efficacy produces lower False Positive Rate (FPR) in predicting efficacy group.  Keywords: Jamu, PLSDA
Multi-locations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction is needed in the selection of genotype to be released. AMMI (Additive Main Effect and Multiplicative Interaction) is one of the statistical techniques used to analyze data from multi-locations trials. The analysis of AMMI is a combination of analysis between additive main effect and principal component analysis. Multi-location sampling data which were collecte Pika Silvianti; Khairil Anwar Notodiputro; I Made Sumertajaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Multi-locations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction is needed in the selection of genotype to be released. AMMI (Additive Main Effect and Multiplicative Interaction) is one of the statistical techniques used to analyze data from multi-locations trials. The analysis of AMMI is a combination of analysis between additive main effect and principal component analysis. Multi-location sampling data which were collected several years on several planting season used these analyzed separately. To obtain more comprehensive information of multi-location sampling data, an analysis which combines all of the information through out the years are needed. One of the alternatives is the Bayesian approach. This method utilizes initial information on the estimated parameters and information from samples. The simulation states that prediction with Bayesian methods will produce a better estimator, because the MSE of the Bayesian estimator is smaller than the MSE estimator generated using least squares method.
Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions,  the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research ar Titin Agustin; Anikk Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Based on the six indicators provided by the State Ministry for Acceleration Development Backward Regions,  the backward regions were clustered into 4 groups: fairly backward region, backward region, highly backward region, and severely backward region. This clustering used weighted average method. The weakness of this method was that the weight determination on each indicator was decided without distinct reference. Besides, there are many outlier in KNDPT data. The objectives of this research are to study the non-hierarchy cluster methods, that is C-Means and Fuzzy C-Means. Both methods have difference on membership value and weighted membership value. The result of this research showed that Fuzzy C-Means was more robust than C-Means.
Additive Main Effects Multiplicative Interaction (AMMI) is a widely known analysis used in understanding genotype and environment interaction (GEI) in plant breeding research. The interpretation of AMMI based on biplot visualizes the first two component of principle components analysis. Biplot of AMMI is only an exploration analysis and it does not provide the hypothesis testing, so it can conduct  different  interpretation by plant breeding researchers. The aim of this research is to find a sys Pepi Novianti; Ahmad Ansori Mattjik; I Made Sumertajaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 15 No. 1 (2010)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Additive Main Effects Multiplicative Interaction (AMMI) is a widely known analysis used in understanding genotype and environment interaction (GEI) in plant breeding research. The interpretation of AMMI based on biplot visualizes the first two component of principle components analysis. Biplot of AMMI is only an exploration analysis and it does not provide the hypothesis testing, so it can conduct  different  interpretation by plant breeding researchers. The aim of this research is to find a systematic approach through bootstrap resampling method. Bootstrap resampling method in AMMI model produces confidence region of the first two interaction principle component ( and ) for genotype and environment respectively. Bootstrap confidence region of  and  estimated the stability of genotype, thus making AMMI analysis more precise and realiable for characterization and selection of  genetic  environment.

Page 3 of 12 | Total Record : 119

 1   2   3   4   5 

Filter by Year

2001 2015


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