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All Journal LOGIK@ Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of Mathematical and Fundamental Sciences Jurnal Fourier Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Zero : Jurnal Sains, Matematika, dan Terapan AXIOM : Jurnal Pendidikan dan Matematika M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics Majalah Ilmiah Matematika dan Statistika (MIMS) Jambura Journal of Biomathematics (JJBM) Jurnal Eurekamatika Innovative: Journal Of Social Science Research The Indonesian Journal of Computer Science
Nina Fitriyati
UIN Syarif Hidayatullah Jakarta
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : CAUCHY: Jurnal Matematika Murni dan Aplikasi

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A Monte Carlo Simulation Study to Assess Estimation Methods in CFA on Ordinal Data Nina Fitriyati; Madona Yunita Wijaya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.14434

Abstract

Likert-type scale data are ordinal data and are commonly used to measure latent constructs in the educational, social, and behavioral sciences. The ordinal observed variables are often treated as continuous variables in factor analysis, which may cause misleading statistical inferences. Two robust estimators, i.e., unweighted least square (ULS) and diagonally weighted least square (DWLS) have been developed to deal with ordinal data in confirmatory factor analysis (CFA). Using synthetic data generated in a Monte Carlo experiment, we study the behavior of these methods (DWLS and ULS) and compare their performance with normal theory-based ML and GLS (generalized least square) under different levels of experimental conditions. The simulation results indicate that both DWLS and ULS yield consistently accurate parameter estimates across all conditions considered in this study. The Likert data can be treated as a continuous variable under ML or GLS when using at least five Likert scale points to produce trivial bias. However, these methods generally fail to provide a satisfactory fit. Empirical studies in the field of psychological measurement data are reported to present how theoretical and statistical instances have to be taken into consideration when ordinal data are used in the CFA model.Keywords: confirmatory factor analysis, diagonally weighted least square, generalized least square, Likert data, maximum likelihood.
A Monte Carlo Simulation Study to Assess Estimation Methods in CFA on Ordinal Data Fitriyati, Nina; Wijaya, Madona Yunita
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 3 (2022): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i3.14434

Abstract

Likert-type scale data are ordinal data and are commonly used to measure latent constructs in the educational, social, and behavioral sciences. The ordinal observed variables are often treated as continuous variables in factor analysis, which may cause misleading statistical inferences. Two robust estimators, i.e., unweighted least square (ULS) and diagonally weighted least square (DWLS) have been developed to deal with ordinal data in confirmatory factor analysis (CFA). Using synthetic data generated in a Monte Carlo experiment, we study the behavior of these methods (DWLS and ULS) and compare their performance with normal theory-based ML and GLS (generalized least square) under different levels of experimental conditions. The simulation results indicate that both DWLS and ULS yield consistently accurate parameter estimates across all conditions considered in this study. The Likert data can be treated as a continuous variable under ML or GLS when using at least five Likert scale points to produce trivial bias. However, these methods generally fail to provide a satisfactory fit. Empirical studies in the field of psychological measurement data are reported to present how theoretical and statistical instances have to be taken into consideration when ordinal data are used in the CFA model.Keywords: confirmatory factor analysis, diagonally weighted least square, generalized least square, Likert data, maximum likelihood.
Transformation of Traditional Models to AI: SLR on the Application of Machine Learning in Mortality Prediction Nuraini, Vita; Fitriyati, Nina
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35972

Abstract

The application of machine learning (ML) in actuarial science and life insurance has driven digital transformation in mortality risk prediction. This article conducts research using the Systematic Literature Review (SLR) methodology with the PRISMA approach to evaluate the performance comparison between ML methods and traditional actuarial models in predicting mortality risk. This study analyzed publication trends, geographic and institutional distribution, and methodologies used in the literature published between 2019 and 2025. The results from SLR show that ML methods, especially Random Forest and XGBoost, have superior predictive accuracy compared to traditional actuarial models such as Traditional Logistic Regression and Cox Proportional Hazards. However, despite the obvious accuracy advantage, issues of interpretability and long-term stability remain a major challenge in implementing ML in the actuarial industry. This study also identifies the need for a hybrid approach combining the strengths of both methodologies to improve prediction accuracy while maintaining high interpretability. This study suggests the need for further development in the application of ML by the regulation and compliance of the insurance industry. The findings provide insights for actuarial practitioners, regulators, and academics regarding the potential and challenges of using ML in mortality risk prediction.
Co-Authors . Mahmudi Aceng Komarudin Mutaqin Adjie, Dharma Syadhi Putra Agus Yodi Gunawan Agus Yodi Gunawan Alkadrie, Syarifah Syila Amelia Emaras, Tamara Anas Qolbu, Adinda Andrew Fiade Anggita Septiani Ayu Puji Rahayu Azzahra Benita Bisyri, M. Alvi Devi Ila Octaviyani Dino Agustin Putra Eliskar, Yulinda Faizah, Salma Abidah Fathu Rahman Gina Isma Kusuma Handarbeni, Zalfa Talitha Haris Hamzah Inna, Suma Irene, Yanne Irma Fauziah Iskandar Fahmi Khusnul Khotimah Mahmudi Mahmudi Mahmudi Mahmudi Maysun Maysun Maysun, Maysun Muhammad Manaqib, Muhammad Nadilia, Nindita Nindita Nadilia Nur Inayah Nuraini, Vita Nurmaleni Nurmaleni NURTITI SUNUSI Rahma, Chelsea Fatihah Rahman, Fathu Rini Marwati Rustam, Rustam S. Sutawanir Saleh, Khaerudin Sari, Flori Ratna Sri Maliska Sri Wahyuningsih Sutanto, Taufik Edy Sutawanir Darwis Taufik Edy Sutanto Wahri Irawan Wijaya, Madona Yunita Wulandari, Fitria Sisca Yupinto, Ikhwal Zharifah, Zharifah Zulkifli, Dhea Urfani Zulkifli, Dhea Urfina