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All Journal Jurnal TIMES BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Seminar Nasional Official Statistics
Amelia, Reni
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Strategi Menjaga Ketahanan Industri Furnitur Jawa Tengah di Era Digitalisasi Amelia, Reni; Mun'im, Akhmad
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2000

Abstract

The era of the 4.0 industrial revolution is driving digitalization in the furniture industry, including the expansion of marketing through the internet. This study examines the impact of digitalization on the income of furniture industry entrepreneurs and the characteristics of those who adopt it in Central Java, using data from the National Labor Force Survey in August 2022. The results of the t-Student test show that entrepreneurs who utilize digitalization have higher average incomes. An association test also indicates a link between digitalization and income. The results of random undersampling and oversampling classification and regression trees identify two characteristics of furniture industry entrepreneurs who utilize digitalization: those who work with the help of permanent/paid workers, or work alone, or with the help of non-permanent/unpaid workers, and those from the Pre Boomers, Millennials, or Z generations. The proposed strategies to maintain the resilience of the furniture industry in Central Java in the digital era include improving digital literacy among entrepreneurs, especially for older individuals or those with limited access to technology.
KOMPARASI TEKNIK UNDERSAMPLING DAN OVERSAMPLING PADA REGRESI LOGISTIK BINER DALAM MENDUGA FAKTOR DETERMINAN BERHENTI MEROKOK PENDUDUK LANJUT USIA Amelia, Reni; Indahwati; Erfiani; Fitrianto , Anwar; Rizki, Akbar
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.97 KB) | DOI: 10.51351/jtm.10.2.2021652

Abstract

Teknik resampling adalah salah satu teknik pre-processing untuk menyeimbangkan distribusi data sehingga mengurangi efek distribusi kelas atau kategori yang tidak seimbang. Teknik resampling yang biasa digunakan adalah random oversampling dan random undersampling. Dalam penelitian ini, random oversampling digunakan untuk menyeimbangkan data dengan cara oversampling secara acak pada kelas minoritas (penduduk lansia yang berhenti merokok). Random undersampling digunakan untuk menyeimbangkan data dengan cara undersampling (mengeliminasi) secara acak kelas mayoritas (penduduk lansia yang masih merokok). Data yang telah diproses dengan resampling selanjutnya dilakukan pemodelan dengan model regresi logistik biner. Model regresi logistik biner dengan random undersampling merupakan model terbaik karena memiliki balanced accuracy terbesar. Peubah yang signifikan memengaruhi berhenti merokok adalah pendidikan, pekerjaan, akses internet, dan usia lansia.
THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY Amelia, Reni; Indahwati, Indahwati; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (461.793 KB) | DOI: 10.30598/barekengvol16iss4pp1355-1364

Abstract

Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights.
Co-Authors Erfiani Erfiani Erfiani Fitrianto, Anwar Indahwati Indahwati Mun'im, Akhmad Rizki, Akbar