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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika)
Hamdan, Rosita
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Mathematics

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Multigroup Analysis on Partial Least Square-Structural Equation Modeling in Modeling College Students' Saving Behavior Asaliontin, Lisa; Sumarminingsih, Eni; Solimun, Solimun; Sepriadi, Hanifa; Iriany, Atiek; Hamdan, Rosita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27692

Abstract

This study aims to determine the factors that influence college students' saving behavior, with gender as a moderating variable. The analysis used is Partial Least Square-Structural Equation Modeling (PLS-SEM) with Multigroup Analysis. This study was conducted on 200 college students in City X who were selected by purposive sampling. Data collection was carried out using a structured questionnaire that measures Perceived Benefits, Perceived Ease of Use, Saving Intentions, and Saving Behavior. Confirmatory Factor Analysis (CFA) and Bootstrapping were used to validate the measurement model and structural relationships. The results showed that Perceived Benefits and Perceived Ease had a significant effect on Saving Intentions and Saving Behavior. In addition, Saving Intentions had a significant effect on Saving Behavior. This relationship applies to both male and female groups, with a determination coefficient of 86.2% for males and 86.7% for females. Moderation analysis shows that gender moderates the relationship between Perceived Benefits and Saving Behavior, as well as between Perceived Ease and Saving Behavior. These findings highlight the importance of considering gender differences in efforts to improve students' savings behavior. 
Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors Pratama, Yossy Maynaldi; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya; Hamdan, Rosita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20192

Abstract

The nonparametric approach is an appropriate approach for patterns of relationships between predictor variables and response variables that are not or have not been known in form. In other words, there is no complete information about the pattern of relationships between variables. Curve estimation is determined based on relationship patterns in existing data. The nonparametric approach has great flexibility for estimating regression curves. This study aims to form a model on investor interest factors in improving tourism investment decisions with a nonparametric approach. The nonparametric method used is the smoothing spline regression method. The smoothing spline method is used because the modeling results from the smoothing spline approach can follow the relationship model between variables contained in the data. Thus, this method really helps researchers to model relationships between variables that are not linear and whose linear form is unknown. The results of the analysis showed that the nonparametric smoothing spline regression analysis method could model data by 94.63%, indicates that data variance can be explained by 94.63% with models, while other variance outside the study explain the remaining 5.37%. That is, investment motivation is one of the most important factors to improve investment decisions. 
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis Hardianti, Rindu; Solimun, Solimun; Nurjannah, Nurjannah; Hamdan, Rosita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i1.17559

Abstract

This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%. 
Co-Authors Asaliontin, Lisa Atiek Iriany Eni Sumarminingsih Fernandes, Adji Achmad Rinaldo Hardianti, Rindu Ni Wayan Surya Wardhani Nurjannah Nurjannah Pratama, Yossy Maynaldi Sepriadi, Hanifa Solimun, Solimun