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All Journal Epsilon: Jurnal Matematika Murni dan Terapan
Nur Salam
Program Studi Matematika FMIPA Universitas Lambung Mangkurat
Decision Sciences, Operations Research & Management Transportation

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PERKIRAAN SELANG KEPERCAYAAN UNTUK PARAMETER PROPORSI PADA DISTRIBUSI BINOMIAL Jainal Jainal; Nur Salam; Dewi Sri Susanti
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 10, No 2 (2016): JURNAL EPSILON VOLUME 10 NOMOR 2
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (202.571 KB) | DOI: 10.20527/epsilon.v10i2.32

Abstract

Selang kepercayaan adalah sebuah selang antara dua angka yang diperoleh dari perkiraan titik sebuah parameter. Karena besar nilai parameter tidak diketahui, sehingga yang dipakai dalam perkiraan adalah sebuah peluang. Nilai parameter yang diperkirakan adalah proporsi. Tujuan penelitian ini adalah menentukan perkiraan selang kepercayaan untuk parameter proporsi pada distribusi Binomial. Hasil dari penelitian ini adalah perkiraan selang kepercayaan untuk parameter proporsi pada distribusi Binomial dengan menggunakan metode besaran pivot dengan ukuran sampel ????????≥30 dan ????????<30.Kata Kunci: Selang Kepercayaan (1−????????), Distribusi Binomial, Proporsi, Metode Kemungkinan Maksimum, Metode Besaran Pivot
REGRESI POISSON TERGENERALISASI I DALAM MENGATASI OVERDISPERSI PADA REGRESI POISSON Zakiah Zakiah; Nur Salam; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 9, No 1 (2015): JURNAL EPSILON VOLUME 9 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (157.462 KB) | DOI: 10.20527/epsilon.v9i1.8

Abstract

Regression analysis is one method to determine and test the causality relationship (cause-effect) between the dependent variable (Y) with the independent variables (X). In general, regression analysis is used to analyze non-free variable data in the form of continuous data and normal distribution. However, in some applications, non-free variable data to be analyzed in the form of discrete data and not normally distributed. One of the regression models that can be used to analyze the relationship between the dependent variable (Y) in the form of discrete data is Poisson regression model whose dependent variable is Poisson distributed. Poisson regression has the assumption of equidispersion that is the condition in which the mean and variance values of the dependent variable are equal, but sometimes there is an assumption violation, where the value of variance is greater than the so-called overdispersion value, so to overcome it can be used one of the extensions of the regression model Poisson is Poisson regression model generalized, this is because the assumption does not require the same mean value with the value of variance. The purpose of this study is how to estimate the Poisson regression model and Poisson regression model generalized I and explain how the generalized Poisson regression model I in overcoming the overdispersion in Poisson regression.
METODE TAGUCHI UNTUK PENINGKATAN KUALITAS MUTU PRODUK Akhriyandi Wijanarta; Nur Salam; Dewi Anggraini
EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN Vol 8, No 1 (2014): JURNAL EPSILON VOLUME 8 NOMOR 1
Publisher : Mathematics Study Program, Faculty of Mathematics and Natural Sciences, Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (123.893 KB) | DOI: 10.20527/epsilon.v8i1.103

Abstract

Costumers tend to choose a better product so that the quality improvement of a product is crucial. Quality control is a continuous process to ensure the quality of the products. The Taguchi method that was introduced by Dr. Genichi Taguchi in 1940 used to improve the quality of product and process as well as to reduce the production cost incurred by the company to minimize damage or defect in the products. The purpose of this research is to explain the procedures of Taguchi method to improve product quality. The results of the research show that the procedures using Taguchi method, are: the first step is counting the number of experiments and choosing the form of orthogonal arrays from the number of factors and levels that will be tested. The second step is conducting experiment and obtains data than calculating the mean value, and determining signal to noise rasio that is consistent to the quality characteristics of the experiment. The third step is analyzing experiment data using analysis of variance to determine factors that have a significant influence, then calculating the contribution value of each factor. If the contribution value of factor is smaller than the contribution value of error value then the factor will be pooling up. After getting the optimal alternative factors the fourth step is confirming experiment to examine the conclusion of the obtained data experiment. Furthermore, the five step is calculating the confidence intervals of response mean value betwen the prediction result of Taguchi method and the result of confirming experiment. After that, the sixth step is calculating Taguchi loss function to determine the amount of damage cost spent to improve the quality of product.
Co-Authors Akhriyandi Wijanarta Dewi Anggraini Dewi Sri Susanti Jainal Jainal Zakiah Zakiah