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INDONESIA
Jurnal Varian
Published by Universitas Bumigora
ISSN : -     EISSN : 25812017     DOI : https://doi.org/10.30812/varian
Core Subject : Economy, Science, Education, Social,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences Other
Jurnal Varian adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora. Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal ini terbit 2 (dua) kali dalam 1 tahun pada periode Genap (April) dan Ganjil (Oktober). Jurnal Varian fokus memuat publikasi pada Bidang Matematika dan Statistika.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 178 Documents
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Analysis of the Relationship Between Net Exports and Gross Regional Domestic Product Using the Panel Vector Correction Model (PVECM) Approach Soleha, Salma; Gamayanti, Nurul Fiskia; Sain, Hartayuni; Fadjryani, Fadjryani
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.4898

Abstract

In regional economic growth, various factors play a role, including net exports, a key indicator of international trade. The purpose of this study is to analyze the long-term relationship and causal link between net exports and Gross Regional Domestic Product (GRDP) in Indonesia. The method used in this study is the Panel Vector Error Correction Model (PVECM), applied to panel data from 34 provinces in Indonesia for the period 2010–2023. The results of the study indicate a cointegration relationship between net exports and GRDP, in which a 1-unit increase in net exports decreases GRDP by 5.445139 units. The Granger Causality test shows a significant bidirectional relationship between the variables, indicating that they influence each other. The R² value of 54.99% indicates that the model explains 54.99% of the variation in net exports. The implication of these findings suggests that policymakers need to consider the quality and composition of export and import activities, as well as regional trade structures, to ensure that international trade contributes positively to regional economic growth.
Prediction of CO2 Emissions Using ANN, ARIMAX, and Hybrid ARIMAX-ANN Models Syaharani, Afifah Dayan; Shafira, Hervira Nur; Irianto, Hikmal Mardian; Kartiasih, Fitri
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5045

Abstract

The escalation of carbon dioxide (CO2) emissions has emerged as a critical environmental concern, particularly in the context of Indonesia’s pursuit of sustainable development. This study aims to forecast CO2 emissions in Indonesia using annual time-series data spanning 1967–2023. Three methodological approaches are employed: an artificial neural network (ANN), an autoregressive model with exogenous variables (ARIMAX), and a hybrid ARIMAX-ANN model. The dataset comprises Gross Domestic Product obtained from the World Bank, along with per capita CO2 emissions, per capita natural gas consumption, and per capita hydropower consumption sourced from Our World in Data. The findings of this research demonstrate that the hybrid ARIMAX-ANN model provides the best forecasting performance, as evidenced by the lowest RMSE, MAPE, and MAE values among the other two models. These results suggest that the hybrid model is currently the most reliable for predicting CO2 emissions in the Indonesian context. The study enriches the expanding literature on emission forecasting by providing empirical evidence to support data-driven policymaking for climate change mitigation and sustainable energy development in Indonesia. 
Mixed Geographically Weighted Regression Modeling Using the MM-Estimator Method on Data of Poverty Amalia Mentari Djalumang; Raupong, Raupong; Siswanto, Siswanto
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5146

Abstract

The mixed geographically weighted regression model combines a global linear regression model with a geographically weighted regression model, with some parameters global and others local. When analyzing data with this model, outliers are common, which can significantly affect the regression coefficients and lead to biased parameter estimates. Therefore, a more robust estimation method that is resistant to outliers is needed to improve accuracy. This study aims to estimate the parameters of the mixed geographically weighted regression model using the Method of Moments (MM) Estimator method, which is more robust to outliers, and to identify the factors that significantly influence the percentage of the poor population in South Sulawesi Province in 2023. The results show that the poverty depth index has a significant global effect on the percentage of the population living in poverty. Meanwhile, the percentage of the population, the open unemployment rate, and the expected years of schooling have significant local effects. Based on these findings, it can be concluded that neighboring regions share common factors influencing poverty rates. These findings can assist policymakers in designing povertyalleviation programs that account for regional differences and support further research on robust spatial modeling approaches. 
Ensemble Quick Robust Clustering Using Links for Clustering Hypertension Patients at a Health Center Niftayana, Neli; Fajri, Mohammad; Gamayanti, Nurul Fiskia
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5151

Abstract

Hypertension is a chronic disease with a high risk of cardiovascular complications and requires treatment according to patient characteristics. At the health center, the number of hypertensive patients is 6953, the highest recorded. Therefore, this study aims to classify and determine the characteristics of hypertensive patients at a health center. The method used in this study is Ensemble Quick Robust Clustering Using Links. This method combines the clustering results of Quick Robust Clustering Using Links and Agglomerative Nesting. Where this method is more efficient in clustering. The results of this study show the number of clusters in the Quick Robust Clustering Using Links method is 3, Agglomerative Nesting is 3 and in the Quick Robust Clustering Using Links Ensemble produces 9 clusters with the following distribution: Cluster 1 shows low hypertension, cluster 2 shows high hypertension, cluster 3 to cluster 6 shows high hypertension, cluster 7 shows moderate hypertension, cluster 8 shows high hypertension and cluster 9 shows moderate hypertension. Thus, grouping patients based on a combination of numerical and categorical variables can provide more detailed information about the severity of hypertension. 
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. 
Spatio-Temporal Using Geographically Weighted Panel Regression for Modeling Environmental Quality Index Mar'ah, Zakiyah; Ruliana, Ruliana; Fikriani, Nurul Azurah; Ikhwana, Nur
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5416

Abstract

The Environmental Quality Index (EQI) represents a numerical measure used to assess Indonesia’s environmental conditions and is published annually by the Ministry of Environment and Forestry. In 2019, the EQI was recorded at 66.55, reflecting a decline of 5.12 points from 71.67 in 2018. This study aimed to analyze EQI across 34 Indonesian provinces during the 2018–2022 period using the Geographically Weighted Panel Regression (GWPR) approach. Data were obtained from the official Statistics Indonesia website. The purpose of employing GWPR was to capture both spatial and temporal variations in the factors influencing EQI, recognizing that environmental dynamics differ by region. Model selection tests for panel data indicated that the Fixed Effects Model (FEM) was the most appropriate specification. Therefore, GWPR was applied in combination with FEM to improve estimation accuracy. The results showed that the significant determinants of EQI varied across provinces, highlighting the heterogeneous nature of environmental challenges. The GWPR with Fixed Effect Model achieved a global R² of 84.38%, a substantial improvement compared to the 42.52% R2 from the conventional global Fixed Effect panel regression. This finding confirmed that GWPR provided stronger explanatory power by incorporating local variations into the analysis. The study concluded that adopting GWPR is essential for more precise modeling of environmental quality. Furthermore, the results highlighted the importance of region-specific environmental policies tailored to each province’s unique conditions in Indonesia 
PENERAPAN ANN DAN GARCH PADA ANALISIS VOLATILITAS PERAMALAN TINGKAT KLAIM BIAYA RAWAT INAP TINGKAT PERTAMA (RITP) BPJS KESEHATAN Pahrany, Andi Daniah; Wakhidah, Melani Nur; Norrulashikin, Siti Mariam Binti
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5422

Abstract

The volatility of First-Level Inpatient Care (RITP) claim costs poses a substantial challenge to BPJS Health’s financial management, underscoring the need for accurate forecasting methods. This study employs Artificial Neural Network and Generalized Autoregressive Conditional Heteroscedasticity models to examine volatility dynamics and assess predictive performance. The results indicate that both models capture nonlinear patterns, heteroskedasticity, and temporal dependencies, with evidence that past fluctuations largely influence current volatility. Forecast accuracy is generally high, as reflected in the small discrepancies between predicted and actual values across most provinces. Nevertheless, the models exhibit limitations in capturing extreme peaks and troughs, where abrupt claim variations are not fully represented. These findings highlight the effectiveness of Artificial Neural Networks and Generalized Autoregressive Conditional Heteroscedasticity in modeling claim volatility, while emphasizing the need for model refinement, such as parameter optimization or integration with complementary approaches, to enhance forecasting reliability. 
Comparison of Farmer Exchange Rate Index Forecasting with Decomposition and Single Exponential Smoothing Method Muthahharah, Isma; Hafid, Hardianti
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5491

Abstract

NTP forecasting is crucial for supporting appropriate policy-making. Therefore, this study aims to address the problem of selecting the most accurate forecasting method for predicting the Farmers’ Terms of Trade Index (FTTI). Specifically, the objective is to compare the accuracy of two time series forecasting methods, namely Decomposition and Single Exponential Smoothing (SES), in forecasting the price index received by food crop farmers for the period 2020 to 2024. Both methods were evaluated using Root Mean Square Error (RMSE) as a measure of forecasting accuracy. The results show that the Decomposition method provides better forecasting accuracy, as indicated by lower RMSE values (RMSE = 1.846) than the SES method, both with α = 0.1 (RMSE = 7.37) and α = 0.6 (RMSE = 3.23). This finding suggests that the Decomposition method is better at capturing seasonal patterns and trends in the FTTI data than the SES method, which tends to produce larger errors. 

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