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INDONESIA
RAGAM: Journal of Statistics and Its Application
Published by Universitas Lambung Mangkurat
ISSN : -     EISSN : 29628539     DOI : https://doi.org/10.20527/ragam.vXXX
Core Subject : Economy, Humanities, Health, Science, Education,
Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health
RAGAM Journal publishes scientific articles in the field of statistics and its applications, including: * Biostatistics * Parametric and nonparametric statistics * Quality control * Econometrics and business * Industrial statistics * Time series analysis * Spatial statistics * Data mining * Computational statistics * Applications of statistics in the medical, economic, social, environmental, industrial, technological, and other related fields
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 64 Documents
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FACTORS INFLUENCING POVERTY RATES IN SOUTH KALIMANTAN 2018–2022 WITH A PANEL DATA REGRESSION APPROACH Ade Irawan; Herlina Wati; Nur Salam; Al Hujjah Asianingrum
RAGAM: Journal of Statistics & Its Application Vol 4, No 2 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i2.15871

Abstract

This study aims to analyze the effects of population growth rate, Human Development Index (HDI), and per capita expenditure on poverty levels in 13 regencies/cities of South Kalimantan Province during the period 2018–2022. The study uses secondary data obtained from the Central Bureau of Statistics and applies panel data regression using the Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). Model selection was conducted using the Chow test, Breusch–Pagan Lagrange Multiplier test, and Hausman test, as well as considering variable significance and model explanatory power. The results indicate that the Fixed Effect Model with time effects (FEM-time) is the most appropriate model. Initial estimation results show that population growth rate and per capita expenditure have significant negative effects on poverty, while HDI has a significant positive effect. However, after applying robust standard errors to address autocorrelation and heteroskedasticity, only per capita expenditure remains statistically significant. These findings suggest that improvements in household purchasing power play a central role in reducing poverty in South Kalimantan, while the impacts of demographic and human development factors tend to vary over time. This study is expected to provide empirical evidence to support more adaptive and region-specific poverty alleviation policies.
WILCOXON RANK-SUM HYPOTHESIS TESTING OF MILITARY BUDGETS AS A PERCENTAGE OF GROSS DOMESTIC PRODUCT AND GOVERNMENT SPENDING Endro Tri Susdarwono; Alma Wiranta
RAGAM: Journal of Statistics & Its Application Vol 4, No 2 (2025): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v4i2.17181

Abstract

This study aims to describe the policy of determining military budget size as a percentage of Gross Domestic Product (GDP) and government spending in countries in Asia, Oceania, and Africa. The approach used in this study is descriptive quantitative. The data used are national defense allocations published by SIPRI. Data analysis used a hypothesis test with a Wilcoxon rank sum. This study concluded that for hypothesis testing with a Wilcoxon rank sum and using the same sample size and no more than 20 samples, the policy of determining military budget size as a percentage of Gross Domestic Product (GDP) in Asian and African countries differs. For testing with a different sample size and no more than 20 samples, the policy of determining military budget size as a percentage of Gross Domestic Product (GDP) in Asian and African countries is the same. Based on hypothesis testing with a Wilcoxon rank sum and using a sample size greater than 20 samples, the policy of determining military budget size as a percentage of government spending in Asian, Oceania, and African countries differs.
AN APPLICATION OF THE PANEL VECTOR AUTOREGRESSIVE MODEL TO ANALYZE INFLATION AND GRDP GROWTH RATES Khairunnisa Khairunnisa; Khoirin Nisa; Misgiyati Misgiyati; Nusyirwan Nusyirwan
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.17067

Abstract

The Panel Vector Autoregressive (PVAR) model is an extension of the traditional Vector Autoregressive (VAR) framework that is specifically designed for panel data, allowing the integration of time-series and cross-sectional information across multiple regions. One of the main advantages of the PVAR approach is its ability to treat all variables as endogenous and to capture dynamic interdependencies simultaneously. This study aims to examine the dynamic relationship between inflation and regional economic growth, as measured by Gross Regional Domestic Product (GRDP) growth, across provinces in Indonesia using a PVAR model. The empirical analysis begins with panel unit root testing employing the Im–Pesaran–Shin (IPS) test to ensure stationarity of the variables, followed by optimal lag length determination based on the Moment and Model Selection Criteria (MMSC). Model estimation is conducted using the Generalized Method of Moments (GMM) to address endogeneity and unobserved heterogeneity. The validity of the instruments is evaluated through the Sargan–Hansen test, while causal linkages between variables are investigated using the Granger causality test. The results reveal evidence of a bidirectional relationship between inflation and economic growth in several provinces, indicating dynamic feedback effects. Stability tests confirm that the estimated PVAR model is stable. Furthermore, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses illustrate how shocks to inflation and economic growth propagate over time. These findings provide valuable insights for designing more effective and regionally targeted economic policies in Indonesia.
MODEL REGRESI COX PROPORTIONAL HAZARD DENGAN PENDEKATAN DISTRIBUSI POISSON PADA LAJU SURVIVAL PASIEN HIV/AIDS Ahmad Amaludin; Raihan Nuur Hakim; Samsul Arifin
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18041

Abstract

Human Immunodeficiency Virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS) represent a global health crisis requiring long-term management. Understanding the factors influencing patient survival duration is crucial for effective clinical management. This study aims to model the survival rate of HIV/AIDS patients and determine dominant risk factors using Cox Proportional Hazard regression. This study utilized secondary data from the ACTG 175 clinical trial involving 2,139 patients. Parameter estimation was performed using the Maximum Partial Likelihood method, validated by the Schoenfeld residuals test. The results indicated that the Proportional Hazard assumption was met for all research variables. Simultaneously, the predictor variables significantly influenced the model. Partial testing identified Didanosine combination therapy (HR=0.715) and a history of drug use (HR=0.720) as protective factors. Conversely, clinical symptoms (HR=1.773), a history of Zidovudine use (HR=1.605), and low Karnofsky scores (HR=1.403) were identified as primary risk factors. Treatment factors and clinical conditions proved to have a more dominant influence on survival rates compared to demographic factors.
DATA MINING-BASED AIR QUALITY MODELING IN JAKARTA USING RANDOM FOREST, K-NAEREST NEIGBORS, AND NAIVE BAYES ALGORITHMS Naufalarizqa Ramadha Meisa Putra
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18196

Abstract

Air quality prediction plays an important role in supporting public health monitoring in highly urbanized regions such as DKI Jakarta. This study aims to predict the Air Pollutant Standard Index (ISPU) category using three supervised learning algorithms, namely Random Forest, k Nearest Neighbors (kNN), and Naive Bayes, based on five pollutant parameters: PM10, SO2, CO, O3, and NO2. The dataset used in this study consists of validated daily air‑quality records that have undergone preprocessing steps including handling missing values and applying min max normalization. Model evaluation is conducted using the Test and Score feature in the Orange Data Mining software, which provides a visual programming environment for machine learning analysis. The results show that Random Forest achieves the highest performance with an accuracy of 97 percent, followed by kNN with 94 percent and Naive Bayes with 88 percent. Feature ranking using the Chi Square test indicates that PM10 is the most dominant factor influencing ISPU category with a value of 870.174, followed by O3 and NO2. These findings highlight that ensemble-based models are well suited for multiclass air quality classification and confirm that particulate matter remains a key determinant of air quality conditions in Jakarta.
FORECASTING THE RUPIAH EXCHANGE RATE AGAINST THE EURO UTILISING LONG SHORT-TERM MEMORY AND GATED RECURRENT UNIT TECHNIQUES Rahmat Hidayat
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18027

Abstract

The global economic recession predicted for 2023 poses significant risks to Indonesia, particularly regarding the weakening of the national currency against foreign currencies. Forecasting serves as a crucial systematic approach to predict future exchange rate fluctuations based on historical data. This study aims to forecast the Rupiah exchange rate against the Euro using Artificial Intelligence approaches: Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Both methods are variants of Recurrent Neural Networks (RNN) capable of learning long-term dependencies through memory cells, with GRU offering a simpler and more efficient architecture than LSTM. This research compares the performance of LSTM and GRU models to determine the most accurate method for predicting the exchange rate. The findings are expected to identify the optimal forecasting model and provide valuable information for anticipating currency changes amidst economic instability.
ACHIEVING SUSTAINABILITY VIA OFFICIAL STATISTICS: INSIGHTS FROM LITERATURE AND BIBLIOMETRIC APPROACHES William Ben Gunawan
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.18154

Abstract

In the pursuit of sustainable development, data-driven decision-making is essential. This study explores the pivotal roles that official statistics, systematically produced by national statistical offices and related agencies, play in advancing sustainability agendas. While extensive research has examined big data and sustainability metrics, the specific contributions of official statistics remain underexplored. To bridge this gap, the study employs a mixed-methods approach combining bibliometric analysis and a structured literature review. A total of 865 Scopus-indexed documents were analyzed to map the intellectual landscape, disciplinary scopes, and thematic clusters associated with the intersection of official statistics and sustainability. The results reveal a growing scholarly interest, with a dominant presence in decision sciences, social sciences, economics, and business fields, and emerging contributions in engineering, environmental sciences, and medicine. Official statistics are found to support sustainability by enabling evidence-based governance, monitoring progress toward the Sustainable Development Goals (SDGs), and fostering accountability and transparency. Thematic cluster analysis further highlights the roles of official statistics in policy modeling, health surveillance, environmental monitoring, and socio-economic planning. However, gaps in disciplinary engagement and geographical representation signal the need for greater integration and capacity-building, particularly in data-intensive scientific domains. This study underscores the strategic value of official statistics as a public good, and recommends strengthening statistical systems, fostering interdisciplinary collaboration, and enhancing data accessibility. These actions are vital to align national and global sustainability efforts with reliable, standardized, and inclusive statistical evidence.
ECONOMETRIC MODELING OF REGIONAL GROSS DOMESTIC PRODUCT IN INDONESIA USING DYNAMIC PANEL DATA THROUGH THE ARELLANO-BOND GENERALIZED METHOD OF MOMENTS APPROACH Devi Mulya Astuti; Tantri Crystiana; Febryana Dilla Setyaningrum; Galuh Salwa Salsabila; M. Al Haris; Alwan Fadlurohman
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.19859

Abstract

Economic growth is a primary indicator of regional development performance, as reflected by Gross Regional Domestic Product (GRDP). The dynamic nature of economic variables requires an analytical approach capable of capturing intertemporal relationships. This study examines changes in GRDP across 34 provinces in Indonesia during the period 2016-2023 using annual dynamic panel data estimated with the Arellano-Bond Generalized Method of Moments (GMM) estimator. The explanatory variables include investment, the open unemployment rate (TPT), the Human Development Index (HDI), population size, and the lagged dependent variable. Model specification is evaluated using the Sargan test and the Arellano-Bond serial correlation test. The results indicate that the estimated model satisfies the required diagnostic tests, suggesting that the estimator is appropriate for the analysis. Furthermore, the Wald test indicates that the regressors are jointly significant. Partial estimation results show that lagged GRDP, investment, the open unemployment rate (TPT), and the Human Development Index (HDI) significantly affect changes in GRDP, whereas population size does not have a statistically significant effect. Lagged GRDP and investment exert positive effects on changes in GRDP, while the open unemployment rate and the Human Development Index have significant negative effects.
VOLATILITY AND VALUE AT RISK ANALYSIS OF BANK RAKYAT INDONESIA STOCK USING THE GJR-GARCH MODEL M. Bagus Hernawan; Indira Ihnu Brilliant
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.18238

Abstract

Shares of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) are among the leading stocks in the Indonesian capital market, with price movements that tend to be volatile and exhibit heteroskedastic characteristics. This study aims to examine volatility behavior and estimate investment risk of BBRI shares using the GJR-GARCHmodel, as well as to measure risk through the Value at Risk (VaR) approach based on the Cornish-Fisher expansion. The data used consist of daily closing prices of BBRI shares from May 1, 2022 to May 1, 2025, obtained from Yahoo Finance and analyzed using the R programming language. The analytical procedures include return calculation, data characteristic testing, ARIMA modeling, ARCH effect testing, and volatility estimation using the GJR-GARCH model. Forecasting performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that the GJR-GARCH(1,1) model effectively captures asymmetric volatility effects. VaR estimation using the Cornish-Fisher approach yields risk values of 5.96% at the 99% confidence level, risk values 3.86% at the 95% confidence level, and risk values 2.88% at the 90% confidence level. The 90% confidence level is recommended as it presents the lowest potential risk of loss.
FORECASTING THE AMOUNT OF CURRENCY IN CIRCULATION AND THE VOLUME OF ELECTRONIC MONEY TRANSACTIONS USING A HYBRID VECTOR AUTOREGRESSIVE–GATED RECURRENT UNIT (VAR-GRU) METHOD Zakiah Ulfa; Nusyirwan Nusyirwan; Dina Eka Nurvazly; Khoirin Nisa
RAGAM: Journal of Statistics & Its Application Vol 5, No 2 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i2.18722

Abstract

Vector Autoregressive (VAR) is a method for forecasting multivariate time series data, but VAR is less able to represent nonlinear patterns in the data. One method in deep learning that is able to capture nonlinear patterns in data is the Gated Recurrent Unit (GRU). The hybrid VAR-GRU method combines both methods to complement each other. The data used in this study are the amount of currency in circulation and the volume of electronic money transactions from January 2019 to December 2024. The results show that the hybrid VAR-GRU has better performance than the single VAR model. The MAPE value obtained by the hybrid model for the volume of electronic money transactions is 4.39% and for the amount of currency in circulation is 2.93%, so the combined MAPE is 3.66%. Meanwhile, the VAR model produces a combined MAPE of 5.22%.

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