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Maylita Hasyim
Faculty of Social and Humanities, Universitas Bhinneka PGRI
Mathematics

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COMPARATIVE BANDWITCH EVALUATION OF KERNEL WEIGHTING FUNCTION FOR PARAMETER ESTIMATION OF GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODEL Imam Safawi Ahmad; Rosalina Monica; Maylita Hasyim
JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) Vol 11, No 2 (2025)
Publisher : Universitas Bhinneka PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jp2m.v11i2.10123

Abstract

East Java Province has a fairly high economic significance, which is in second place in the province with the highest Gross Regional Domestic Product (GRDP) value in Indonesia. This is inseparable from the contribution of each district / city in the acquisition of GRDP, it is known that there are variations in the acquisition of GRDP which indicates the existence of uneven economic growth in East Java. Uneven economic growth has various risks that can affect various aspects. Therefore, it is very important to understand the factors that influence economic growth in each district/city. The Geographically Weighted Panel Regression (GWPR) approach is used to explore the dynamics of change that takes into account the spatial influence between regions. The data used in this study are data on the acquisition of GRDP at constant prices from 38 districts / cities in East Java from 2018 to 2022. The Adaptive Tricube Kernel is used in Geographically Weighted Panel Regression (GWPR) to effectively handle spatial heterogeneity in datasets where observation points are irregularly spaced. The results showed that the GWPR adaptive kernel model with an adaptive tricube kernel weighting function was the best model with R2 of 90.3% and RMSE of 1,033.8. This is in line with the concept that The tri-cube kernel is compact and has two continuous derivatives at the boundary of its support, while the another kernel has none.Adaptive kernel GWPR modelling produces different models for each district/city and produces 7 regional groups based on factors that have a significant effect. In general, there are four variables that have a significant effect on the acquisition of GRDP at constant prices in East Java, namely Regional Original Revenue (PAD), the number of workers, the number of large and medium industries, and the amount of domestic investment.
TIME SERIES MODELING OF INDONESIA’S INFLATION RATE USING ARIMA: A CASE STUDY OF 2015–2025 INFLATION DATA Vita Fibriyani; Maylita Hasyim; Any Tsalasatul Fitriyah
JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) Vol 11, No 2 (2025)
Publisher : Universitas Bhinneka PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jp2m.v11i2.10110

Abstract

Inflation is a key macroeconomic indicator that reflects price stability and forms the basis for monetary policy formulation. This study aims to model and evaluate the ability to forecast Indonesia's inflation rate using monthly data from 2015 to 2025 with the Augmented Integrated Moving Average (ARIMA) approach. The ARIMA method is used to capture the dynamics of the inflation time series and identify the existence of inflation inertia. The analysis stages include stationarity testing, model identification based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) patterns, parameter estimation, and residual diagnostic and forecasting accuracy evaluation. The analysis results show that inflation data is stationary at a level that does not require differencing. The best model obtained is ARIMA (1,0,0) with a statistically significant first-order autoregressive component. Model performance evaluation using Mean Absolute Percentage Error (MAPE) yields a value of 15.37%, indicating that the model has a fairly good forecasting accuracy. Empirically, these findings support the theory of inflation inertia, in which previous periods of inflation play an important role in explaining current inflation. The results of this study show that the simple ARIMA model is still relevant and effective for short-term inflation forecasting in Indonesia, especially in conditions of relatively stable monetary policy.
Co-Authors Any Tsalasatul Fitriyah Imam Safawi Ahmad Rosalina Monica Vita Fibriyani