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Journal : Jurnal Matematika UNAND

COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol 14, No 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

Abstract

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.
COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol. 14 No. 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

Abstract

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.
ANALYSIS OF FOREST FIRE CASES USING GSTAR(1;1) MODEL WITH SPATIAL ROOK CONTIGUITY WEIGHTS MATRIX IN WEST KALIMANTAN Ayyash, Muhammad Yahya; Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri; Pratiwi, Hesty
Jurnal Matematika UNAND Vol. 15 No. 2 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.2.259-273.2026

Abstract

In West Kalimantan, forest and land fires cause damage to ecosystems, the loss of biodiversity, and detrimental repercussions on both health and the local econ- omy. Extreme weather and land clearance for agricultural and plantation purposes are the primary reasons. This study aims to investigate forest fires’ spatial and temporal pat- terns by employing the Generalized Space-Time Autoregressive (GSTAR)(1;1) approach with spatial rook contiguity weights. From January 2020 to March 2024, the data used consisted of the number of monthly forest fires that occurred in the Ketapang, Sanggau, Sintang, Landak, and Sekadau Regencies. According to the findings, the spatial pattern demonstrates strong interactions between regions in which flames in one area affect fires in other locations. The temporal pattern demonstrates that prior fires can impact fires that occur in the subsequent period, depending on the area. The model has an aver- age accuracy level of 13%, which indicates that this model has a reasonable degree of accuracy that can be used for making predictions. This study concluded that a better understanding of the spatial-temporal patterns of forest fires can improve early warning systems and rapid responses to probable future fires.