Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

1.259

P-Index

This Author published in this journals

All Journal Statistika MediaTor: Jurnal Komunikasi Jurnal Riset Statistika Pattimura Proceeding : Conference of Science and Technology Bandung Conference Series: Statistics

Suliadi Suliadi

Statistika, Universitas Islam Bandung

Author-ID : 3237910

Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics

Published : 14 Documents Claim Missing Document

Claim Missing Document

Articles

1 2

EKSPLORASI SISA USIA BEARING MENGGUNAKAN DISTRIBUSI WEIBULL Sutawanir Darwis; Nusar Hajarisman; Suliadi Suliadi; Achmad Widodo
Pattimura Proceeding 2021: Prosiding KNM XX
Publisher : Pattimura University

Bearing merupakan komponen penting dalam suatu sistem mekanikal, berperansebagai elemen penghubung dua komponen mesin yang bergerak. Perawatan bearingmerupakan aspek utama dalam kelangsungan operasional sistem. Vibrasi yang dihasilkancacat pada bearing dimodelkan sebagai impulse, tingkat kerusakan dinyatakan oleh suatufungsi konstan. Sisa usia bearing merupakan merupakan suatu indikator degradasi padaanalisis survival bearing, didefinisikan sebagai ekspektasi residual diketahui survive hinggawaktu t. Sisa umur telah dijabarkan untuk beberapa distribusi antara lain: eksponensial,gamma, Weibull. Penelitian model sisa usia bearing dengan asumsi distribusi Weibullmerupakan masalah penelitian terbuka. Paper ini bertujuan meneliti pola sisa usia bearingsebagai fungsi dari parameter bentuk dan parameter skala. Parameter bentuk dan parameterskala ditaksir menggunakan data time to failure bearing menggunakan data real dan datasimulasi. Model simulasi bearing merupakan fungsi dari geometri, laju bearing dan distribusibeban. Dengan nilai taksiran parameter diperoleh kurva sisa usia bearing digunakansebagai prediksi sisa usia.

Pengujian Hipotesis untuk Dua Sampel Saling Bebas dengan Menggunakan Pendekatan Bayesian Fithri Amalia Rahma; Suliadi
Bandung Conference Series: Statistics Vol. 1 No. 1 (2021): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Abstract. In this thesis, we will discuss hypothesis testing for two independent samples using the Bayesian approach. One of the alternative bayesian methods for classical hypothesis testing is the Bayesian factor. Wang & Liu (2015) proposed a two-sample testing method with a bayesian approach. Where the Bayes factor used is simple, and free from the Bartlett paradox and the information paradox. The data used for the application of this method is the Human Development Index (IPM) data based on the province of West Java and Central Java in 2020. The results of the two independent sample test using the Bayesian approach are that there is no difference in the average Student Development Index (IPM). between West Java and Central Java provinces in 2020. Abstrak. Dalam skripsi ini akan dibahas pengujian hipotesis untuk dua sampel saling bebas dengan menggunakan pendekatan bayesian. Salah satu metode alternatif bayesian untuk pengujian hipotesis klasik adalah faktor bayes. Wang & Liu (2015) mengajukan metode pengujian dua sampel dengan pendekatan bayesian. Dimana faktor bayes yang digunakan sederhana, serta terbebas dari paradoks Bartlett dan paradoks informasi. Data yang digunakan untuk penerapan metode ini adalah data Indeks Pembangunan Manusia (IPM) berdasarkan provinsi Jawa Barat dengan provinsi Jawa Tengah pada tahun 2020. Hasil dari uji dua sampel saling bebas menggunakan pendekatan bayesian adalah tidak ada perbedaaan rata-rata Indeks Pembangunan Mahasiswa (IPM) antara provinsi Jawa Barat dengan provinsi Jawa Tengah pada tahun 2020.

Pemodelan New Ridge Regression Estimator pada Tingkat Kemiskinan di Kabupaten/Kota Provinsi Jawa Barat Tahun 2020 Ridho Febriansyah Tambunan; Suliadi
Bandung Conference Series: Statistics Vol. 2 No. 2 (2022): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Abstract. Linear regression is a statistical method used to predict value dependent variable or response with one or more independent variables. If there is more than one predictor variable, multiple linear regression analysis is used. Ridge regression estimator has been introduced as an alternative to the ordinary least squares estimator (OLS) in the presence of multicollinearity. Ridge regression minimizes the mean square residual by introducing a bias constant and produced biased but stable coefficients estimate. The aim of this research is to apply a method introducing by Al-hassan (2010) to obtaine the bias constant in ridge regression that produces smaller bias than method given by Hoerl & Kennad. We apply this method to model the poverty rate in districts/cities in West Java in 2020. The dependent variable (Y) is the proverty rate and the independet variables are (average length of school), (unemployment rate), (gross domestic regional product), (human development index), (number of labor force participation rate). The value of the ridge constant using the Al-hassan (2010) method is 1.377633. The ridge regression model for the standardized variables is with , & that significanly affect the reponse. The regression model based on the original variable is Abstrak. Analisis regresi linier adalah metode statistika yang digunakan untuk membentuk model hubungan antara variabel terikat (dependent atau respon ) dengan satu atau lebih variabel bebas (independent atau prediktor). Apabila variabel prediktor lebih dari satu maka digunakan analisis regresi linier berganda. Ada beberapa asumsi yang harus terpenuhi dalam regresi linier berganda diantaranya asumsi multikolinearitas. Salah satu metode untuk mengatasi masalah multikolinieritas adalah menggunakan metode regresi ridge. Regresi ridge meminimumkan residual dengan menambahkan tetapan bias (k). Namun metode ini masih memiliki kelemahan yaitu masih terdapat bias. Untuk memperbaiki kelemahan tersebut Al-hassan mengajukan metode baru. Metode ini bertujuan untuk memperkecil nilai bias dari suatu penduga dengan cara memodifikasi nilai k. Dalam skripsi ini kami menerapkan metode tersebut untuk memodelkan tingkat kemiskinan di Kabupaten/Kota di Jawa Barat Tahun 2020. Variabel responnya adalah Y (tingkat kemiskinan) dan variabel bebasnya (lama rata-rata sekolah), (tingkat pengangguran terbuka), (produk domestik regional bruto), (indeks pembangunan manusia), (jumlah angkatan kerja). Nilai konstanta ridge menggunakan metode Al-hassan (2010) sebesar Sehingga didapatkan model persamaan ridge yaitu : Dengan variabel baku , dan varibel baku yang signifikan terhadap variabel . Dan model berdasarkan variabel aslinya adalah

Pengujian pada Regresi Ridge dan Penerapannya terhadap Data Produk Domestik Regional Bruto Provinsi Jawa Barat Weni Nuryati; Suliadi Suliadi
Bandung Conference Series: Statistics Vol. 3 No. 2 (2023): Bandung Conference Series: Statistics
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v3i2.8424

Abstract. Ridge regression is one of the methods used to stabilize the value of the regression coefficient caused by multicollinearity. In ridge regression, to reduce the impact of multicollinearity is carried out by adding ridge parameter c to the hat matrix. This ridge parameter makes the regression coefficients have a smaller variance than the least squares method estimator variance. However, the ridge estimates are biased. Thus, hypothesis testing using the usual method cannot be applied to the coefficients ridge regression. Therefore Bae, et al., (2014) developed a method for testing the hypothesis of the coefficients of ridge regression. This thesis aims to apply this method to the gross regional domestic product data for West Java province in 2022. Based on the results of the research, it shows that there is a multicollinearity problem in the data, so it is modelLed using ridge regression. it was obtained The ridge regression model : . From the results of testing the hypothesis, it can be concluded that the independent variables, namely local original income (X1), general allocation funds (X2), profit sharing funds (X3), regional expenditures (X4) and labor (X5) together have a significant effect on the PDRB (Y) of West Java Province in 2022. The ridge regression model is returned to the original model . Abstract. Ridge regression is one of the methods used to stabilize the value of the regression coefficient caused by multicollinearity. In ridge regression, to reduce the impact of multicollinearity is carried out by adding ridge parameter c to the hat matrix. This ridge parameter makes the regression coefficients have a smaller variance than the least squares method estimator variance. However, the ridge estimates are biased. Thus, hypothesis testing using the usual method cannot be applied to the coefficients ridge regression. Therefore Bae, et al., (2014) developed a method for testing the hypothesis of the coefficients of ridge regression. This thesis aims to apply this method to the gross regional domestic product data for West Java province in 2022. Based on the results of the research, it shows that there is a multicollinearity problem in the data, so it is modelLed using ridge regression. it was obtained The ridge regression model : . From the results of testing the hypothesis, it can be concluded that the independent variables, namely local original income (X1), general allocation funds (X2), profit sharing funds (X3), regional expenditures (X4) and labor (X5) together have a significant effect on the PDRB (Y) of West Java Province in 2022. The ridge regression model is returned to the original model .

Co-Authors Achmad Widodo Achmad Widodo Anneke Iswani A. Bambang Saiful Ma’arif Fithri Amalia Rahma Fitriana Novitasari Nusar Hajarisman Parihat, Parihat Riani Shifa Rahmadani Ridho Febriansyah Tambunan Riyani Desriawati Selma Yulistiani Seviyenti Fikroh Siti Nurhayati Sutawanir Darwis Sutawanir Darwis Umar Yusuf Weni Nuryati

Title

Found 14 Documents
Search

Abstract

Abstract

Abstract

Abstract

Title Search

Found 14 Documents Search

Abstract

Abstract

Abstract

Abstract

Title

Found 14 Documents
Search