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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 353 Documents
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MM*INDO : INTERACTIVE STATISTICS LEARNING IN INDONESIAN LANGUAGE Hizir Sofyan; Noer Azam Achsani
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.886

Abstract

In line with the development of computer and information technology, interactive learning become analternative choice to the conventional one. MM*Indo is an interactive introductory to the world of statistics usingIndonesian Language. This software would help the student to understand the statistic lectures, especially in theelementary phase, through it’s dynamic explanation and many practical exercises. The software is supported by the XploRestatistical programming language and written in HTML and Javascript, so that it can be executed via World Wide Web andalso CD-ROM. It consists of 12 chapter covering all introductory themas of statistics, from the descriptive statistics,introduction to the probability, hypothesis testing until linear regression.
Perbandingan Metode Partial Least Square (PLS) dengan Regresi Komponen Utama untuk Mengatasi Multikolinearitas Nurhasanah Nurhasanah; Muhammad Subianto; Rika Fitriani
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 12, No 1 (2012)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v12i1.1059

Abstract

Dalam mengatasi multikolinearitas pada suatu data, ada beberapa metode yang dapat digunakan,diantaranya yaitu metode Partial Least Square (PLS) dan metode regresi komponen utama (RKU).Data yang digunakan dalam penulisan ini adalah data sekunder yang diperoleh dari JurnalTechnometrics (Naes, 1985). Hasilnya menunjukkan bahwa metode PLS lebih baik dari pada RKUberdasarkan nilai koefisien determinasi (R2) yang tinggi, nilai Mean Square Error Prediction (MSEP)dan nilai Root Mean Square Error Prediction (RMSEP) yang minimum.
Pengembangan Model Regresi pada Peubah Respon Diskrit (Model Regresi Poisson ) Nusar Hajarisman
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 1, No 1 (2001)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v1i1.484

Abstract

Dalan banyak bidang penelitian mengenai habungan antord peubah rcsryn denganpcubah-peubahp redilaor, dimanap eubah rcsponnyam erupakanp eubah diskrit yangtidak biner. Botyalorya suatu kejadian dalam suatu unit tertentu diasurnsikans ebagaifingsi dari satu anu lebih peubah prediktor. Dalarn malcalah ini kita asumsilcanbahwa bahwa rata-rata dari banyalotya kejdian dalam unit tertentu merupakantpararneter dari distribusi Poisson. Rata-rata Poisson ini juga merupalcot fwgsi furipeubah-peubahp rediktor yang dapat dikembangkanm cnjadi model regresi Poisson.Dalam penerapannya, proses penaluiran poraneternya diperoleh melalui metodekzmwgkinan naksimum. Sebagai gambamn dari penerapan model regresi Poissonalcan dherila n sebuah contoh pemalcaiannya
INTERVAL ESTIMATION FOR TWO PARAMETERS EXPONENTIAL DISTRIBUTION UNDER MULTIPLE TYPE-II CENCORING ON SIMPLE CASE WITH BOOTSTRAP PERCENTILE Akhmad Fauzy; Noor Akma Ibrahim; Isa Daud; Mohd. Rizam Abu Bakar
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.515

Abstract

In the article, two methods are proposed to give the interval estimation for twoparameters exponential distribution under multiple type-II cencoring on simple case. Balakrishnan(1990) use approximate maximum likehood estimator to construct interval estimation. All of thesemethod need an assumtion that sample is exounentially distributed. Method give shorter intervalthan the traditional method and this method does not need an assumtion that the sample isdistributed exponentially
Production Function Modeling of the Relationship between Quantity of Graduates and Federal Government Grants Case Studies: Universiti Sains Malaysia R. Gobithasan; Anton Abdulbasah Kamil
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 6, No 2 (2006)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v6i2.943

Abstract

In recent years, much work has been constructed in the area of productivity and growth in order toidentify the link between factor inputs and output based on production function. However, in the fieldof tertiary education, there are less research to classify and discover a model to estimate theproduction of graduates in accord with the factor inputs. This paper discusses the usage ofproduction function in which the properties are specified in order to fit the tertiary education sectorwith reference to the data of Universiti Sains Malaysia (USM). It is then estimated with the Cobb-Douglas Production function (C-D). Aspects such as the inferences caused by multicollinearity,heteroscedasticity, and autocorrelation are also analyzed. In this approach, OLS and GLS type ofregression analysis have been carried out in order to analyze the productivity and growth of USM inproducing graduates. A suitable model produced by using two independent variables namelyemolument (from federal government operating expenditure) and capital (remaining federalgovernment grant plus federal government developing grant), is in fact presentable in the form of C-Dproduction function. The outcome of this study indicated a value greater than 1 for β1 and less than0 for β2 which implies that USM is experiencing an increasing marginal product of emolument, E andnegative marginal product of capital plus development grant, C.
PEMODELAN PANEL SPASIAL PADA DATA KEMISKINAN DI PROVINSI PAPUA Yulial Hikmah
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 17, No 1 (2017)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v17i1.2318

Abstract

Model panel spasial merupakan hasil pengembangan dari model data panel. Pengembangan ini berSdasarkan pada adanya pengaruh spasial atau lokasi pada data panel. Informasi spasial sangat penting karena dapat mengetahui hubungan suatu daerah dengan daerah lainnya yang saling berdekatan. Data yang mengandung unsur spasial tidak akan akurat jika hanya menggunakan analisis regresi sederhana karena akan terjadi kesalahan asumsi. Sementara jika hanya menggunakan regresi panel saja tanpa memasukkan spasial akan menghasilkan galat yang heterogen yang diakibatkan keterkaitan antar wilayah. Tujuan penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi persentase penduduk miskin di Provinsi Papua berdasarkan model panel spasial terbaik diantara model panel SAR, SEM, dan GSM.
ESTIMATION OF TIME SERIES MODELS FOR MISSING DATA Sri Subanti
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 3, No 1 (2003)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v3i1.562

Abstract

Runtun waktu merupakan himpunan observasi berurut dalam waktu, pada makalah ini yang dibicarakan adalah runtunyang diskrit dengan observasi Zt pada waktu t = 1, ... , N. Sehingga pengalaman yang lalu hanya dapat menunjukan strukturprobalistik keadaan yang akan datang dari runtun waktu ini merupakan runtun waktu statistik.Runtun waktu statistik dapat dipandang sebagai salah satu realisasi dari suatu proses stokastik. Dengan demikianuntuk sembarang Zt dapat dipandang sebagai suatu realisasi suatu variabel random Zt yang mempunyai distribusi dengan fungsikepadatan probabilitas (f k p) tertentu misal f(Zt).
Theoretical Welfare Cost Analysis to Reduce Carbon Dioxide Emissions Anton Abdulbasah Kamil
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 8, No 2 (2008)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v8i2.981

Abstract

This paper focuses on insurance against the small probability of causing really catastrophic climatechange may justify significantly curbing CO2 emissions. Such extreme non-linearities maybe exist.However predicting future global climate changes is extremely hazardous, and no-one can rule outthe possibility of surprise. This paper uses indirect method to get the possible scenarios that couldoccur, nor what costs or subjective probabilities to attach to most of the catastrophes that have beensuggested.
ANALISIS DISKRIMINAN LINEAR ROBUST PADA BERAT BAYI LAHIR DI RSUD LUWUK Nur'eni Nur'eni; Surni’a Surni’a; Lilies Handayani
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 19, No 1 (2019)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v19i1.4759

Abstract

Analisis diskriminan linear robust digunakan untuk mengklasifikasikan suatu pengamatan apabila dalam pengamatan tersebut terdapat pencilan. Pencilan akan menyebabkan matriks varians kovarians menjadi tidak robust. Minimum Covariance Determinant (MCD) digunakan untuk menduga sebagian pengamatan dengan meminimumkan determinan matriks kovariansi. Berat bayi lahir menurut WHO (1961) terbagi menjadi dua kategori yaitu berat bayi lahir rendah (BBL  2500 gram) dan berat bayi lahir normal (BBL > 2500 gram). Hasil dari klasifikasi berat bayi lahir di RSUD Luwuk Kabupaten Banggai dengan menggunakan metode analisis diskriminan linear robust diperoleh tingkat akurasi sebesar 81%.
ANALYZING THE CONSUMER’S RICE PRICE USING MULTIPLE LINEAR REGRESSION AND X-12 ARIMA Dian Kusumaningrum,; Asep Saefuddin; Anang Kurnia
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.876

Abstract

Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. Onthe other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to therice price, as well as forecasting the consumer’s rice price are needed in order to maintain the stabilization of rice price.The second objective is to find key factors which influence the rice price by using multiple linear regression models. Theparameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which arethe consumer’s rice price at the previous period, rice production at the current and previous period, farmer’s GKP price,realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domesticstock increase whereas total rice import and the consumer’s rice price at the previous period have negative influencestowards the rice price. In this model rice production at the current and previous period have positive signs, contradictory tothe microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop.That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but inIndonesia, rice is affected by the government’s policy and the rice productivity is left behind by the demand. Forecastingthe consumer’s rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12ARIMA, Winter’s Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer’srice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. Thisresult is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal andtrading day factors which usually causes fluctuations in an economic time series data. Besides that, the X-12 ARIMAmethod also enhances the lack of other forecasting techniques used in this research to add regression effects. TheregARIMA makes it possible to add the user defined parameters, in this case the length of month parameter. The length ofmonth parameter rescales the monthly observation by a weight corresponding to the month relative length with respect tothe average length. The seasonal adjusted data from the original time series data is aimed to simplify the data withoutloosing important information.

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