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All Journal JOIV : International Journal on Informatics Visualization Jurnal Varian
Zakaria, Roslinazairimah
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other

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Visualization Tools for Backward Elimination Technique in Multiple Regression Time Series Modelling of CO2 Emissions in Malaysia Mansor, Mahayaudin M.; Ibrahim, Nurain; Zakaria, Roslinazairimah; Suhaila, Jamaludin; Miswan, Nor Hamizah; Shaadan, Norshahida
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3012

Abstract

Understanding multiple regression time series modelling is crucial because the procedures involve intricate statistical methods. This study incorporates a flowchart that clearly illustrates the steps for modelling a response variable affected by several explanatory variables via the backward elimination technique. The first objective of this study is to utilise ten graphical tools, comprising charts and tables, for visual assessment to support formal evaluations in model diagnostics using R programming. The aim is to provide comprehensive insights and improve the overall understanding of the modelling procedures. The visualisation tools include criteria for multicollinearity, goodness-of-fit, and underlying assumptions of normality, homoscedasticity, zero serial correlation, and volatility in the residuals. The second objective involves implementing modelling procedures to obtain a well-specified model in a real-world context, demonstrating its practical value and implications. In this instance, the selected response variable is carbon dioxide (CO2) emissions, significantly contributing to global warming. In Malaysia, CO2 emissions increased continuously from 1990 to 2022, with an alarming average annual growth rate of 4.9%. The visual diagnostics have helped guide the elimination of some explanatory variables in the initial model and refined the models, resulting in a well-specified final model that is parsimonious and explains 98.6% of the variability in CO2 emissions. The final model suggests that high fossil fuel use and GDP per capita are contributing factors to increased CO2 emissions in Malaysia. The study recommends government action and investment in renewable energy to reduce CO2 emissions by 45% by 2030 and achieve net-zero emissions by 2050.
Evaluating Fisherman Insurance Participation using Bagging Multivariate Adaptive Regression Splines Azmi, Ulil; Soehardjoepri, Soehardjoepri; Saputri, Prilyandari Dina; Salsabila, Thalia Rizki; Iswara, Widya; Zakaria, Roslinazairimah
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5373

Abstract

The Fishermen’s Insurance Premium Assistance Program and the Independent Fishermen’s Insurance Scheme are initiatives by the Indonesian government aimed at enhancing the protection of fishermen, whose occupations are considered high-risk compared to other professions. One of the regions actively participating in both programs is Lekok District, located in Pasuruan Regency, East Java Province. The objective of this research is to analyze the factors influencing fishermen’s participation in self-funded insurance schemes using the Multivariate Adaptive Regression Spline method. The research is based on primary data collected through direct surveys and structured questionnaires distributed to fishermen in Lekok District. The results of this research are that five key variables significantly influence participation, with the most influential factor being participation in outreach or socialization activities. Other important factors include the number of family members (X4), income (X3), and age (X1), while fishing experience (X5) does not show a significant effect. The model’s classification accuracy on the training data reached 82%, while on the test data it was 75.8%. Furthermore, applying the bootstrap aggregation technique to Multivariate Adaptive Regression Splines models significantly improved classification accuracy to 92% on the training data and 100% on the test data. The findings are expected to support stakeholders in formulating strategies to increase fishermen’s engagement in independent insurance programs. Strengthening such participation is crucial for reducing occupational risks, ensuring the sustainability of fishing activities, and improving the welfare and resilience of the fishing community. 
Co-Authors Ibrahim, Nurain Iswara, Widya Mansor, Mahayaudin M. Miswan, Nor Hamizah Salsabila, Thalia Rizki Saputri, Prilyandari Dina Shaadan, Norshahida Soehardjoepri, Soehardjoepri Suhaila, Jamaludin Ulil Azmi