cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Aceng Komarudin Mutaqin
Contact Email
aceng.k.mutaqin@gmail.com
Phone
+628179289987
Journal Mail Official
jstat.unisba@gmail.com
Editorial Address
Jl. Ranggagading No. 8 Bandung 40116
Location
Kota bandung,
Jawa barat
INDONESIA
Statistika
Published by Universitas Islam Bandung
ISSN : 14115891     EISSN : 25992538     DOI : https://doi.org/10.29313/jstat.v19i2.4898
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics
STATISTIKA published by Bandung Islamic University as pouring media and discussion of scientific papers in the field of statistical science and its applications, both in the form of research results, discussion of theory, methodology, computing, and review books.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 8 Documents
 1 
Search results for , issue "Vol 6, No 1 (2006)" : 8 Documents clear
Improvisasi Penaksir Model Linier Mulyana Mulyana
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.927

Abstract

-
Measurement System Analysis Using Repeatability and Reproducibility Techniques Norizan Mohamed; Yamene Davahran
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.932

Abstract

To monitor and improve any manufacturing process in quality control, it is important to measure thecause of the attribute for that process. The quality of measurement data depends on the repeatedmeasurements obtained through measurement systems which will operate at a given condition. Thisstudy uses a type of statistical quality control technique called Repeatability and Reproducibility(R&R) to determine how much of the process variation is caused by the variation of the measurementsystem that is being used. The two types of charts that were used for this study were the ANOVAchart and the Mean & Average chart.
Proses Kelahiran dengan Imigrasi dan Kematian Password Sri Mulyani Sanro’i; Neneng Sunengsih; Gatot Riwi Setyanto
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.928

Abstract

Dalam penelitian dibahas mengenai sebuah model stokastik pertumbuhan populasi password.Dimisalkan bahwa populasi berkembang melalui kedatangan sejumlah netter melalui proses poisson.Populasi kemudian terpecah menjadi beberapa subpopulasi karena adanya mutasi password, denganjenis password dalam sebuah subpopulasi sama dan berbeda antar subpopulasi. Distribusi stasionerbanyak netter dalam sebuah subpopulasi ternyata poisson.
Detection Procedure for A Single Outlier in a Bilinear Model 1azami Zaharim, 2mohammad Said Zainol, 3ibrahim Azami Zaharim; Mohammad Said Zainol; Ibrahim Mohamed; Mohd Sahar Yahaya
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.934

Abstract

A single outlier detection procedure for data generated from BL(1,1,1,1) models is developed. It iscarried out in three stages. Firstly, the measure of impact of an IO, AO, TC and LC, denoted by IO  ,AO  , TC  and LC  , respectively are derived based on least squares method. Secondly, test statisticsand test criteria are defined for classifying an observation as an outlier of its respective type. Finally,a general single outlier detection procedure is presented to distinguish a particular type of outlier at atime point t.
Risiko Kredit Berstokastik dengan Kadar Inflasi Nurfadhlina Binti Abdul Halim; Alif Asraf Bin Entali; Wan Muhamad Amir Bin Wan Ahmad
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.929

Abstract

Kajian ini adalah merupakan lanjutan daripada kajian oleh Nurfadhlina Binti Abdul Halim (2004).Kajian adalah memodelkan risiko kredit bagi bon korporat berkadar faedah tetap dengan kaedahstokastik. Pendekatan ini digunakan bagi mendapatkan kebarangkalian kemungkiran dan jangkaanmasa sebelum berlakunya kemungkiran bon korporat yang berisiko dan mengesahkan intuisi awalpelabur adalah benar. Kebarangkalian kemungkiran dan jangkaan masa sebelum berlakunyakemungkiran adalah berguna dalam meminimumkan kerugian kerana dengan mengetahuikebarangkalian kemungkiran, pelabur dapat membuat penilaian dan pilihan pelaburan yang lebihbermanfaat pada masa itu dan dapat mengurangkan risiko kerugian dalam pelaburan. Model risikokredit dalam kajian ini dibina dengan mengambil kira kebergantungan di antara penarafan kreditbon korporat (dimodelkan dengan proses rantai Markov) dengan keadaan yang ditentukanberdasarkan kadar inflasi serta premium risiko.
Pendugaan Regresi Spline Terpenalti dengan Pendekatan Model Linear Campuran Anik Djuraidah; Aunuddin Aunuddin
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.935

Abstract

Regresi spline terpenalti, atau P-spline, adalah regresi yang ditentukan dengan kuadrat terkecil danpenalti kekasaran. P-spline dapat direpresentasikan dalam bentuk model linear campuran dengankomponen ragam mengontrol tingkat ketidaklinearan dari penduga fungsi mulusnya. Pendugaan Psplinedengan pendekatan model linear campuran mempunyai tiga keuntungan. Keuntunganpertama adalah P-spline dapat diduga dengan metode kemungkinan maksimum (ML) atau denganmetode kemungkinan maksimum berkendala (REML). Keuntungan kedua adalah komputasi lebihcepat karena menggunakan basis pemulus berdimensi rendah. Keuntungan ketiga adalah P-splinedapat dikembangkan untuk model dengan peubah penjelas lebih dari satu.
Algoritma untuk Membangkitkan Missing Data Aceng Komarudin Mutaqin
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.930

Abstract

Makalah ini membahas suatu algoritma untuk membangkitkan missing data (data hilang) untukkasus univariat. Pendekatan yang digunakan untuk membangkitkan jenis data tersebut adalahteknik komposisi (composition technique). Untuk mengimplementasikan algoritma tersebut,diberikan contoh program komputer yang ditulis dalam makro lokal MINITAB.
Identification of Time Series Model: An Application Part Wan Muhamad Amir Bin W Ahmad; Norhayati Rosli; Norizan Mohamed; Zalila Binti Ali
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 1 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i1.931

Abstract

Time series analysis generally referred to any analysis which involved to a time series data. In thisanalysis, any of the continuous observation is commonly dependent. If the continuous observation isdependable, then the values that will come are able to be forecasted from the previous observation(Weir 2006). If the behaviour of coming time series are able to be exactly forecasted based on previoustimes series, so it’s called deterministic time series. The objective of times series can be summarizedas to find the statistical model to describe the behaviour of the time series data and afterwards madeuse of skilled statistical techniques for estimation, forecasting but also the controlling. The use oftime series analysis very much spread in various fields like biology, medical and many more that hada purpose for forecasting. In this paper the recognition of concerning the Autoregressive Processmodel AR (p), Moving Average Process MA (q), Autoregressive Moving Average ARMA (p,q),Autoregressive Integrated Moving Average ARIMA (p,d,q) was given attention through the approach tothe Autocorrelation Function ACF and Partial Autocorrelation Function (PACF) theory plot.

Page 1 of 1 | Total Record : 8

 1 

Filter by Year

2006 2006


Reset
Filter By Issues
All Issue Vol 21, No 1 (2021) Vol 20, No 2 (2020) Vol 20, No 1 (2020) Vol 19, No 2 (2019) Vol 19, No 1 (2019) Vol 18, No 2 (2018) Vol 18, No 1 (2018) Vol 17, No 2 (2017) Vol 17, No 1 (2017) Vol 16, No 2 (2016) Vol 16, No 1 (2016) Vol 15, No 2 (2015) Vol 15, No 1 (2015) Vol 14, No 2 (2014) Vol 14, No 1 (2014) Vol 13, No 2 (2013) Vol 13, No 1 (2013) Vol 12, No 2 (2012) Vol 12, No 1 (2012) Vol 11, No 2 (2011) Vol 11, No 1 (2011) Vol 10, No 2 (2010) Vol 10, No 1 (2010) Vol 9, No 2 (2009) Vol 9, No 1 (2009) Vol 8, No 2 (2008) Vol 8, No 1 (2008) Vol 7, No 2 (2007) Vol 7, No 1 (2007) Vol 6, No 2 (2006) Vol 6, No 1 (2006) Vol 5, No 2 (2005) Vol 5, No 1 (2005) Vol 4, No 2 (2004) Vol 4, No 1 (2004) Vol 3, No 2 (2003) Vol 3, No 1 (2003) Vol 2, No 2 (2002) Vol 2, No 1 (2002) Vol 1, No 1 (2001) More Issue