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All Journal BIMASTER Jurnal Gaussian CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of the Indonesian Mathematical Society Indonesian Journal of Applied Statistics BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Matematika UNAND Jambura Journal of Biomathematics (JJBM) Journal of Mathematics Education and Science Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Forum Analisis Statistik
Huda, Nur'ainul Miftahul
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Transportation Other

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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.
Analisis Autoregressive Integrated Moving Average (ARIMA) dengan Intervensi Double Input pada Prediksi Harga Saham Maulidya, Gita Arinda; Satyahadewi, Neva; Huda, Nur'ainul Miftahul
Indonesian Journal of Applied Statistics Vol 7, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v7i1.85229

Abstract

Intervention analysis is the time series analysis used in a time series model that experiences an intervention event. Intervention is an event that can cause time series data to change patterns caused by external or internal factors such as changes in government policy, advertising promotions, environmental regulations, and others. This research uses the ARIMA analysis method of double input step function intervention with daily data on the closing share prices of PT Adaro Energy Indonesia for the period 7 March 2022 to 7 March 2023 because in that period there are two points that are thought to be interventions that have an impact on changes in the ADRO’s share prices over a long period of time. The aim of this research is to analyze the intervention ARIMA model and predict the closing price of PT Adaro Energy Indonesia for the next five-days period. The ARIMA analysis steps are based on the ARIMA model through the process of stationarity data (variance and mean), order identification, parameter estimation, and diagnostic examination. The best ARIMA model used to predict ADRO's closing share price is the ARIMA (2,1,2) model, which is obtained based on the smallest AIC, MAPE, and RMSE values. The prediction results in this research show that the predictions produced for the next five-days period are classified as very good because they have a MAPE value on training data of 1,96% and a MAPE value on testing data of 1,74%.
Co-Authors Arini, Nani Fitria Aulia, Alwa Ayyash, Muhammad Yahya Fikadila, Lisa Fitriyana, Gita Fran, Fransiskus Imro'ah, Nurfitri Kartika Sari Mauditia, Lyra Maulidya, Gita Arinda Mujiarti, Eka May Nabil, Ilhan Nail Neva Satyahadewi Pratiwi, Hesty Rachmawati, Febby Rahmi Hidayati Rofatunnisa, Sifa Safira, Shafa Alya Safitri, Fauziah Seftiani, Seftiani Umairah, Tarisa Utami, Dewi Setyo Utriweni Mukhaiyar Yundari, Yundari Zuleha, Zuleha