Pemodelan Distribusi Spasial Hotspot di Kabupaten Banjar Menggunakan Log-Gaussian Cox Process

2025-10-23T13:24:52+08:00

Forest and land fires are recurring ecological and socio-economic disasters in Banjar Regency, South Kalimantan Province, with complex triggers. A deep understanding of the spatial distribution of fire risk is crucial for effective mitigation efforts. This study aims to model the spatial intensity of hotspots as a proxy for forest and land fires events in Banjar Regency and produce a fire risk surface map. The data used in this study are hotspot data from the siPongi website in Banjar Regency for the period 2013–2024, along with elevation data analyzed using the Log-Gaussian Cox Process (LGCP) spatial statistical model. The analysis results show that elevation has a negative but statistically insignificant effect on hotspot intensity, where fire risk tends to be higher at lower elevations. The LGCP model proved effective in capturing the complex spatial patterns of hotspot occurrences, separating trends driven by covariates and residual spatial clustering. The resulting risk intensity map successfully identified high-risk clusters, particularly concentrated in western districts dominated by peatlands and agricultural activities.

Pemodelan dan Prediksi Pola Musiman Menggunakan Holt-Winters

2025-10-23T14:13:52+08:00

Samarinda City, with its tropical climate, experiences significant variations in rainfall throughout the year. This instability has the potential to cause impacts such as flooding, disruptions in the agricultural sector, and damage to infrastructure. This study aims to analyze and forecast the seasonal rainfall patterns in Samarinda City by applying the Holt Winters Exponential Smoothing method based on a multiplicative model. Monthly rainfall data were analyzed to identify stationarity properties in both mean and variance. The results indicate that the data are stationary in the mean but not in the variance, thus justifying the use of the Holt-Winters Multiplicative Exponential Smoothing model. Parameter estimation yielded alpha , beta , and gamma values of 1 each, with a MAPE of 50%, indicating a moderate level of accuracy. Despite the relatively high error rate, the model remains effective in illustrating seasonal patterns, which can be useful for preliminary water resource management planning in the region

VARIANSI: Journal of Statistics and Its application on Teaching and Research

Pemodelan Distribusi Spasial Hotspot di Kabupaten Banjar Menggunakan Log-Gaussian Cox Process

Pemodelan dan Prediksi Pola Musiman Menggunakan Holt-Winters