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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 5 Documents
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Search results for , issue "vol 5, no 1 (2026): ragam: journal of statistics " : 5 Documents clear
APPLICATION OF PANEL VECTOR AUTOREGRESSIVE (PVAR) MODEL ON THE ANALYSIS OF INFLATION AND GDRP RATE 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

PVAR is an extension of the VAR model applied to panel data, combining time series with cross-sectional data from various regions. This model enables all variables to be treated as endogenous and analyzed simultaneously. This study aims to examine the relationship between inflation and economic growth (GRDP) across Indonesian provinces using the Panel Vector Autoregressive (PVAR) model. The analysis includes stationarity testing (IPS test), optimal lag selection (MMSC), and parameter estimation using the Generalized Method of Moments (GMM). The validity of instruments is assessed through the Sargan-Hansen test, while causal relationships are analyzed using the Granger causality test. Results indicate a bidirectional relationship between inflation and economic growth in several provinces. The model is proven to be stable. Furthermore, the Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses illustrate how shocks to one variable influence the other over time. These findings are expected to contribute to more effective formulation of regional economic policies.
MODEL REGRESI COX PROPORTIONAL HAZARD DENGAN PENDEKATAN DISTRIBUSI POISSON PADA LAJU SURVIVAL PASIEN HIV/AIDS Amaludin, Ahmad; Hakim, Raihan Nuur; Arifin, Samsul
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.
PEMODELAN KUALITAS UDARA JAKARTA BERBASIS DATA MINING DENGAN ALGORITMA RANDOM FOREST, KNN, DAN NAIVE BAYES Ramadha Meisa Putra, Naufalarizqa
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 Hidayat, Rahmat
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 Gunawan, William Ben
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.

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