COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD
DOI:
https://doi.org/10.25077/jmua.14.1.31-45.2025Keywords:
GSTAR, Uniform Weight Matrix, Queen ContiguityAbstract
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.
References
Wasis, B., Saharjo, B.H., Putra, E.I., 2019, Impacts of peat fire on soil flora and fauna, soil properties and environmental damage in Riau province, Indonesia, Biodiversitas Vol. 20(6): 1770 – 1775, https://doi.org/10.13057/biodiv/d200639
Khairani, N. A., Sutoyo, E., 2020, Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hotspots in West Kalimantan Province, International Journal of Advances in Data and Information Systems Vol. 1(1): 9 – 16, https://doi.org/10.25008/ijadis.v1i1.13
Sarmiasih, M., Pratama, P.Y., 2019, The Problematics Mitigation of Forest and Land Fire Disputes (Kerhutla) in Policy Perspective (A Case Study : Kalimantan and Sumatra in Period 2015-2019), Journal of Governance and Public Policy Vol. 6(3): 270 – 292, https://doi.org/10.18196/jgpp.63113
Suryowati, K., Bekti, R. D., Faradila, A., 2018, A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation, IOP Conference Series: Materials Science and Engineering Vol. 335: 012052 https://doi.org/10.1088/1757-899X/335/1/012052
Hestuningtias, F., Kurniawan, M.H.S., 2023, The Implementation of the Generalized Space-Time Autoregressive (GSTAR) Model for Inflation Prediction, Enthusiastic: International Journal of Applied Statistics and Data Science Vol. 3(2): 176 – 188, https://doi.org/10.20885/enthusiastic.vol3.iss2.art5
Mukhaiyar, U., Huda, N. M., Novita Sari, R. R. K., Pasaribu, U.S., 2019, Modeling Dengue Fever Cases by Using GSTAR(1;1) Model with Outlier Factor, Journal of Physics: Conference Series Vol. 1366: 012122, https://doi.org/10.1088/1742-6596/1366/1/012122
Abdullah, A.S., Matoha, S., Lubis, D.A., Falah, A.N., Jaya, I.G.N.M., Hermawan, E., Ruchjana, B.N., 2018, Implementation of Generalized Space Time Autoregressive (GSTAR)-Kriging model for predicting rainfall data at unobserved locations in West Java, Applied Mathematics and Information Sciences, Vol. 12(3): 607 – 615, https://doi.org/10.18576/amis/120316
Huda, N.M., Imro’ah, N., Arini, N.F., Utami, D.S., Umairah, T., 2023, Looking at GDP from a Statistical Perspective: Spatio-Temporal GSTAR(1,1,1) Model, JTAM (Jurnal Teori Dan Aplikasi Matematika), Vol. 7(4): 976 – 988, https://doi.org/10.31764/jtam.v7i4.16236
Wijaya, I.M., Sumertajaya, I.M., Erfani, 2015, Comparison of Autoregressive (AR), Vector Autoregressive (VAR), Space Time Autoregressive (STAR), and Generalized Space Time Autoregressive (GSTAR) in forecasting (Case: Simulation study with autoregressive pattern), International Journal of Applied Engineering Research Vol. 20(12): 42388 – 42395
Hu, J., Wang, S., Mao, J., 2019, Short time PM2.5 prediction model for Beijing-Tianjin-Hebei region based on Generalized Space Time Autoregressive (GSTAR), IOP Conference Series: Earth and Environmental Science Vol. 358: 022075 https://doi.org/10.1088/1755-1315/358/2/022075
Huda, N. M., Mukhaiyar, U., Imro’ah, N., 2022, An Iterative Procedure for Outlier Detection in GSTAR(1,1,1) Model, BAREKENG: Jurnal Ilmu Matematika Dan Terapan Vol. 16(3): 975 – 984, https://doi.org/10.30598/barekengvol16iss3pp975-984
Yundari, Y., Jonathan, R., Helmi, H., 2022, GSTAR (1,1) Modeling with Time Correlated Errors for Geoelectric Resistivity Log Data in Pontianak City, In Prime: Indonesian Journal of Pure and Applied Mathematics Vol. 4(2): 91 – 103, https://doi.org/10.15408/inprime.v4i2.26263
Huda, N. M., Imro’ah, N., 2023, Determination of the best weight matrix for the Generalized Space Time Autoregressive (GSTAR) model in theCovid-19 case on Java Island, Indonesia, Spatial Statistics Vol. 54: 100734, https://doi.org/10.1016/j.spasta.2023.100734
Fadlurrohman, A., 2020, Integration of GSTAR-X and Uniform location weights methods for forecasting Inflation Survey of Living Costs in Central Java, Journal of Intelligent Computing and Health Informatics Vol. 1(1): 20 – 25, https://doi.org/10.26714/jichi.v1i1.5583
Huda, N. A. M., Imro’ah, N., 2024, Covid-19 case modeling in Java Island using a spatial model, GSTAR (1; 1), with modified spatial weights: Queen contiguity weight matrix, AIP Conference Proceedings Vol. 2891: 090009
Aprianti, A., Faulina, N., Usman, M., 2024, Generalized Space Time Autoregressive (GSTAR) Model for Air Temperature Forecasting in the South Sumatera, Riau, and Jambi Provinces, InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6(1): 1 – 13, https://doi.org/10.15408/inprime.v6i1.36049
Akbar, M. S., Setiawan, Suhartono, Ruchjana, B. N., Riyadi, M. A. A., 2018, GSTAR-SUR Modeling with Calendar Variations and Intervention to Forecast Outflow of Currencies in Java Indonesia, Journal of Physics: Conference Series Vol. 974: 012060 https://doi.org/10.1088/1742-6596/974/1/012060
Chicco, D., Warrens, M. J., Jurman, G., 2021, The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science Vol. 7: 1 –24, https://doi.org/10.7717/PEERJ-CS.623
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