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All Journal ELP (Journal of English Language Pedagogy) UNP Journal of Statistics and Data Science
Fenni Kurnia Mutiya
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Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Journal : UNP Journal of Statistics and Data Science

 1 
Comparison of Nadaraya-Watson and Local Polynomial Methods in Analyzing the Relationship Between Consumer Price Index and Inflation in South Kalimantan Salwa Hifa Fadilah; Fadhilah Fitri; Fenni Kurnia Mutiya
UNP Journal of Statistics and Data Science Vol. 3 No. 3 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss3/401

Abstract

This study compares the performance of two nonparametric regression methods, namely Nadaraya-Watson and Local Polynomial, in analyzing the relationship between the Consumer Price Index (CPI) and inflation in South Kalimantan Province. Nonparametric approaches were chosen for their greater flexibility in capturing nonlinear relationships that conventional parametric models may fail to explain. The data were obtained from the Central Statistics Agency (BPS) for the period from January 2022 to December 2024, with missing values in the inflation variable handled through mean imputation. The optimal bandwidth was selected using the direct plug-in method (dpill).Visually, the Nadaraya-Watson method produced a more fluctuating curve that is highly sensitive to local variations, while the Local Polynomial method yielded a smoother and more stable curve. Quantitatively, the Local Polynomial method demonstrated better performance with lower MSE (0.1839), MAE (0.3507), and a higher R² (0.1811) compared to Nadaraya-Watson. These findings indicate that the Local Polynomial method is more effective in balancing curve flexibility and stability. This study also addresses a methodological gap by highlighting the relevance of nonparametric approaches in regional economic analysis. Future research is encouraged to explore alternative bandwidth selection methods and different kernel functions to improve estimation accuracy.
Peramalan Konsentrasi PM2.5 di Kota Medan Menggunakan Metode ARIMAX dengan Faktor Meteorologi sebagai Variabel Eksogen Fauzan Arrahman; Tessy Octavia Mukhti; Dony Permana; Fenni Kurnia Mutiya
UNP Journal of Statistics and Data Science Vol. 3 No. 4 (2025): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol3-iss4/429

Abstract

Particulate Matter 2.5 (PM2.5) is a fine particle measuring less than 2.5 micrometers which is dangerous for human health because it can penetrate the respiratory system and cause cardiovascular disorders. High PM2.5 concentrations reflect a decline in air quality, so forecasting efforts are needed to support pollution control and environmental policies. This study aims to forecast daily PM2.5 concentrations in Medan City using the Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method by considering meteorological factors as exogenous variables. The data used consist of PM2.5 concentrations and average temperature, humidity, rainfall, and wind speed data for the period from June 1, 2024 to June 10, 2025. The analysis results show that the best model is ARIMAX (4,1,0) with exogenous variables of average temperature and rainfall, where temperature has a positive effect and rainfall has a negative effect on PM2.5. This model meets the assumptions of white noise and residual normality, with a MAPE value of 20.635%, indicating a fairly good level of forecasting accuracy. The forecasting results show PM2.5 concentrations in the range of 19–26 µg/m³ with a downward trend at the end of June 2025, indicating improved air quality in Medan City. Thus, the ARIMAX method with meteorological factors is considered effective in modeling and forecasting PM2.5 dynamics in urban areas.
Co-Authors Chairina Wirdiastuti Dony Permana Elviza Yeni Putri Fadhilah Fitri Fauzan Arrahman Lucy Oktavani Salwa Hifa Fadilah Sepyanda, Marsika Tessy Octavia Mukhti Titiek Fujita Yusandra Yummi Meirafoni