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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi
Gita Fitriyana
Universitas Tanjungpura
Mathematics

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Spatio-Temporal Forecasting and Continuous Spatial Reconstruction of Fire Radiative Power Using Sequential GSTARX-IDW and Ordinary Kriging Nurfitri Imro'ah; Nur'ainul Miftahul Huda; Yundari Yundari; Gita Fitriyana
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 2 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i2.44480

Abstract

This study presents a sequential hybrid spatio-temporal forecasting framework combining the Generalized Space-Time Autoregressive with Exogenous Variables (GSTARX-IDW) model and Ordinary Kriging (OK) to model and map weekly Fire Radiative Power (FRP) dynamics in West Kalimantan from January 2021 to October 2025. A strong dominance of spatial contagion was observed, with the spatial autoregressive parameter (p1) being statistically significant across 95.56% of operational grid centroids, providing empirical validation of Tobler's First Law of Geography. Locally, Land Surface Temperature (LST) serves as a key exogenous forcing variable, exhibiting geographical dichotomies driven by localized microclimatic conditions and peatland hydrology. To overcome the limitation of discrete point forecasts at grid centroids, Ordinary Kriging was applied directly to the k-step ahead GSTARX-IDW point forecasts, successfully reconstructing continuous spatial risk surfaces for October 2025. Evaluated through robust out-of-sample metrics, the framework achieved a Root Mean Squared Error (RMSE) of 1.1380, a Mean Absolute Error (MAE) of 0.8736, and a Mean Absolute Scaled Error (MASE) of 1.0008, demonstrating competitive temporal point forecasting on par with baseline dynamics while offering superior spatial continuous risk mapping. This sequential framework provides a mathematically grounded baseline for short-term spatio-temporal risk assessment in highly fragmented tropical landscapes.
Sustainable Stock Screening Based on Fundamental and Technical Indicators using Gaussian Naive Bayes Classifier Risky Gunawan; Cinta Priscillia Maharani; Gita Fitriyana; Dwi Indah Maharani
Jurnal Matematika, Statistika dan Komputasi Vol. 22 No. 3 (2026): May 2026
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v22i3.49168

Abstract

Investors increasingly require systematic methods for sustainable stock screening, particularly for ESG-focused benchmarks like Inodnesia's SRI-KEHATI Index. This Study addresses a gap by developing and evaluating a stock screening framework using a Gaussian Naive Bayes (GNB) classifier to integrate both fundamental and technical analysis. The model utilized quarterly data from 25 SRI-KEHATI stocks from Q1 2024 to Q2 2025, training on 11 indicators to predict future quarterly returns, classifying stocks as "Investable" (Label 1) or "Non-investable" (Label 0). The model achieved an average training accuracy of 73.6%. Feature importance analysis revealed that technical indicators, such as Average Log Return, Average MACD, Average RSI, and key fundamental ratios, PBV and ROA, were the most influential predictors. Model predictions were evaluated through a simple equal-weighted portfolio simulation for Q3 2025. The simulation results showed the model-selected "Investable" portfolio generated a 29.9% return, substantially outperforming and the "Non-investable" portfolio (3.56%). These findings demonstrate that the GNB classifier is an effective framework for sustainable stock screening, successfully identifying ESG-compliant stocks that also deliver superior financial returns and providing a practical tool for responsible investing in the Indonesian capital market.
Co-Authors Cinta Priscillia Maharani Dwi Indah Maharani Nurfitri Imro'ah NUR’AINUL MIFTAHUL HUDA Risky Gunawan Yundari, Yundari