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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI UNEJ e-Proceeding BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM ESTIMASI: Journal of Statistics and Its Application Journal of Social Outreach Journal of Research Applications in Community Services Progressive Physics Journal Eksponensial STATISTIKA
Darnah, Darnah
Mulawarman University
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Mengatasi Multikoliniearitas Dalam Regresi Linier Berganda Menggunakan Principal Component Analysis Chairunnisa, Niken Harel; Darnah, Darnah; Syaripuddin, Syaripuddin
EKSPONENSIAL Vol. 16 No. 1 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i1.1155

Abstract

Multiple linear regression analysis has assumptions that must be met, one of which is multicollinearity. Multicollinearity occurs when the independent variables correlate with each other, resulting in the regression coefficient produced by multiple linear regression analysis being very weak or unable to provide analysis results that represent the nature or influence of the independent variable concerned. The detection of multicollinearity can be known through the VIF value. In this study, human development index data on Kalimantan Island in 2019 detected multicollinearity because some independent variables have a VIF value of more than 10 so that the method used to overcome multicollinearity in this study is Principal Component Analysis (PCA). Based on the results of research using the Principal Component Regression method, There are five independent variables that influence the IPM that is Percentage of Poor Population, Number of Health Workers, Number of Workforce, Number of High Schools, and Number of High School Teachers.
ANALISIS KORELASI SOMERS’D PADA DATA TINGKAT KENYAMANAN SISWA-SISWI SMP PLUS MELATI SAMARINDA Kriesniati, Prastika; Yuniarti, Desi; Nohe, Darnah A.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 7 No 2 (2013): BAREKENG : Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (703.64 KB) | DOI: 10.30598/barekengvol7iss2pp31-40

Abstract

Somers'd correlation is a analysis of correlate is used for data with ordinal scale and formed in a contingency table. Somers'd correlation can be used for symmetric and asymmetric relationships. In this case, will be explained about Somers’d asymmetric correlation. Somers'd dYX correlation for asymmetrical association applied to data from questionnaires about their comfortable level of Students live in dorms of Melati formed into 2 contingency table, the contingency table for boarding facilities with the comfortable level of students and the quality of the food with the comfortable level of students. Based on the analysis of correlation Somers'd dYX, it can be seen that there is relationship between boarding facility with comfortable level of students and quality of food with comfortable level of students, and then correlation coefficient from 2 contingency table is 0,330 and 0,345 respectively
Pemilihan Model Regresi Logistik Ordinal Terbaik Menggunakan Metode Stepwise: (Studi Kasus: Data Indeks Prestasi Kumulatif Lulusan Program Sarjana FMIPA Unmul) Selsi, Selsi; Darnah, Darnah; Wahyuningsih, Sri
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.44426

Abstract

Ordinal logistic regression is one of the statistical methods used to model response variables with two or more categories that have levels. This study aims to model the cumulative grade point average data of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University in 2023 using ordinal logistic regression. Estimation of ordinal logistic regression model parameters is done using the Maximum Likelihood Estimation (MLE) method and Newton-Raphson iteration. The best model selection was conducted using the stepwise method based on the smallest Akaike Information Criterion (AIC) value and significant predictor variables. The selection process started with six predictor variables, then gradually eliminated three predictor variables because they were not significant and did not reduce the AIC value. The stepwise stage stopped at the model with three significant predictor variables that had an AIC value of 349,22. The results showed that the factors that had a significant effect on the cumulative grade point average of undergraduate graduates of the Faculty of Mathematics and Natural Sciences, Mulawarman University based on the best ordinal logistic regression model were study program, age, and admission pathway.
Structural Modeling and XRD Analysis of (PVA:LiOH)–Fe₃O₄ Composite Electrolyte for Supercapacitor Applications Rahmawati Munir; Dadan Hamdani; Darnah Andi Nohe; Rahmiati Munir; Igor Levi Satriani; Siti Fatimah; Sahara Hamas Intifadhah
Progressive Physics Journal Vol. 7 No. 1 (2026): Progressive Physics Journal
Publisher : Program Studi Fisika, Jurusan Fisika, FMIPA, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/1twm3k85

Abstract

The development of supercapacitors requires electrolyte membranes with high ionic conductivity and magnetic properties to enhance energy storage performance. This study aims to visualize the crystal structure and simulate the X-ray diffraction (XRD) patterns of the (PVA:LiOH)–Fe₃O₄ composite electrolyte membrane using the VESTA software as the basis for analyzing its potential application in magnetic supercapacitors. The material was synthesized through the sol–gel method, with PVA serving as the polymer matrix, LiOH as the lithium ion source, and Fe₃O₄ as the magnetic filler. Crystal structure characterization was performed using XRD measurements, followed by modeling of the Fe₃O₄ and LiOH crystalline phases based on reference CIF data, while PVA was represented as an amorphous matrix. The simulated multiphase XRD pattern was validated against experimental data to confirm the agreement between diffraction peaks and crystal phases. The three-dimensional supercell visualization revealed the spatial distribution of Fe₃O₄ and LiOH particles within the polymer matrix. Electrical measurements demonstrated an increase in ionic conductivity from the order of 10⁻⁴ S/cm in PVA:LiOH membranes to 10⁻³ S/cm after Fe₃O₄ incorporation. This enhancement is attributed to the formation of more efficient ion transport pathways resulting from the interaction between the magnetic filler and the polymer matrix. The simulated XRD results reinforce the correlation between crystal structure, phase distribution, and ionic conductivity performance. These findings suggest that the (PVA:LiOH)–Fe₃O₄ composite possesses strong potential as an electrolyte membrane for magnetic supercapacitors, opening opportunities for developing materials with combined electrochemical and magnetic properties to improve energy storage efficiency.
Co-Authors Alfina Damayanti Chairunnisa, Niken Harel Dadan Hamdani Dani, Andrea Tri Rian Dini Elizabeth Eka Nur Amaliah Fauziyah, Meirinda Ghony N Nurhuda Igor Levi Satriani Ika Purnamasari Khairun Nida Kriesniati, Prastika Krisna Rendi Awalludin M. Fathurahman M. RIZKY RAMADHAN Meiliyani Siringoringo Memi Nor Hayati Memi Norhayati Nadia Serena Rahmawati Munir Rahmiati Munir Rito Goejantoro, Rito Rut Esra Sahara Hamas Intifadhah Sekar Nur Utami Selsi, Selsi Septian, Rama Sifriyani, Sifriyani SITI FATIMAH Surya Prangga Syaripuddin Syaripuddin Wasono Wasono Yuniarti, Desi