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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Research in Mathematics Trends and Technology
Muhammad Nasrudin
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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A Negative Binomial Regression Approach to Address Overdispersion in the Analysis of Maternal Mortality in Indonesia Sischa Wahyuning Tyas; Muhammad Nasrudin; Awang Putra Sembada; Robiansyah Putra
Journal of Research in Mathematics Trends and Technology Vol. 8 No. 1 (2026): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v8i1.25387

Abstract

Maternal Mortality Rate (MMR) is one of the major indicators utilized for measuring the performance of public health systems. In Indonesia, there is a considerable level of MMR as shown by its MMR value of 189 per 100,000 live births in 2023 which is far from reaching its SDG target. This paper seeks to determine factors that influence maternal mortality in Indonesia .This research uses MMR as the response variable along with ten independent variables as predictors. The predictors consist of health-related factors and socio-economic conditions. The process starts with performing a descriptive statistic followed by multicollinearity test using Variance Inflation Factor (VIF). Poisson regression is initially applied and overdispersion was detected. Thus, Negative Binomial regression should be used as a better alternative. Selection of models is performed using AIC. Based on the result, factors that influence maternal mortality in Indonesia are percentages of medical personnel, proper sanitation, deliveries in health facilities, and T2 immunization. The optimal Negative Binomial Regression model has an AIC value of 416.0637.
Enhancing Inflation Forecasting in Indonesia Using N-BEATSx with Exogenous Factors Talitha Adilla Fujisai Panglima Putri; Mohammad Idhom; Muhammad Nasrudin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12759

Abstract

Accurate inflation forecasting is crucial for economic stability and effective policymaking, particularly in emerging economies such as Indonesia, where monetary policy, global commodity markets, exchange rate fluctuations, and recurring religious seasonal events simultaneously influence price dynamics. This study proposes an inflation forecasting framework using the N-BEATSx (Neural Basis Expansion Analysis for Time Series with Exogenous Variables) deep learning model, incorporating macroeconomic variables, global oil prices, BI Rate, and the USD/IDR exchange rate, alongside Ramadan and Eid al-Fitr calendar dummy variables as exogenous inputs. The dataset comprises 153 monthly observations spanning January 2013 to September 2025, split into training, validation, and test sets, with a forecasting horizon of six months. The N-BEATSx model is benchmarked against SARIMAX, LSTM, and Prophet. Results on the test set show that N-BEATSx achieves competitive performance (RMSE 0.0067, MAE 0.0058, SMAPE 51.77%) outperforming SARIMAX (RMSE 0.0297, MAE 0.0266) and LSTM (RMSE 0.0098, MAE 0.0084). Although Prophet yields marginally lower absolute errors, the MAE gap is minimal (0.0002), while N-BEATSx offers superior interpretability through an explicit decomposition of forecasts into trend, seasonality, and exogenous components. Component decomposition analysis reveals that macroeconomic exogenous variables dominate the forecast output, confirming their theoretical relevance as inflation drivers. Six-month-ahead forecasts project inflation in the range of 2.47% - 3.54% for October 2025 to March 2026, approaching Bank Indonesia’s upper target corridor, suggesting the need for preemptive monetary policy measures.
Co-Authors Awang Putra Sembada Mohammad Idhom Robiansyah Putra Sischa Wahyuning Tyas Talitha Adilla Fujisai Panglima Putri