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All Journal Jurnal Geografi, Edukasi dan Lingkungan BAREKENG: Jurnal Ilmu Matematika dan Terapan
Giovani, Muhammad
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Astronomy Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analisis Spasial Risiko Bencana Kebakaran Hutan Dan Lahan Di Kabupaten Pesawaran Simarmata, Nirmawana; Nadzir, Zulfikar Adlan; Agustina, Lea Kristi; Wijayanti, Goldie Melinda; Giovani, Muhammad
Jurnal Geografi, Edukasi dan Lingkungan (JGEL) Vol. 9 No. 1 (2025): Edisi Bulan Januari
Publisher : Pendidikan Geografi Universitas Muhammadiyah Prof. Dr. Hamka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/jgel.v9i1.14520

Abstract

Forest and land fires are common natural disasters in Indonesia, impacting ecology, economy, and community welfare. This study aims to calculate the spatial risk of Karhutla in Pesawaran Regency as an initial step in disaster management. The data comes from Landsat 8 (OLI) satellite images, slope maps, road maps, rivers, and land cover of Pesawaran Regency. This disaster risk assessment method refers to Decree from the BNPB Head Number 2 on year 2012 with three risk components in the form of vulnerability, hazard, and capacity indices. The hazard index was analyzed using hazard assessment variables, spatial analysis, and attribute analysis. The index of vulnerability is conducted by analyzing the conditions and characteristics of the community and its environment to calculate which factors reduce the ability to cope with disasters. The capacity index is calculated based on five leading indicators: disaster management rules and institutions, early warning systems and disaster risk assessments, disaster education, essential risk factor reduction, and preparedness development at all levels. The calculated hazard, vulnerability, and capacity indices were then combined to assess the risk level of land and forest fire disasters. An analysis from forest and land fire disaster risk assessment results in Pesawaran Regency shows that majority of the area have a medium risk. The largest sub-district with a low-risk level is in Pandan Island Sub-district with an area of 11,332.6 hectare, and the largest sub-district with a high level is in Tegineneng Sub-district of 11,558.3 hectare area, followed by Gedong Meneng sub-district. These sub-districts could be prioritized for a holistic and integrated scheme to add capacity index by all stakeholders.
COMPARISON IN PREDICTING THE SHORT-TERM USING THE SARIMA, DSARIMA AND TSARIMA METHODS Giovani, Muhammad; Anggriani, Indira; Simatupang, Syalam Ali Wira Dinata
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (529.483 KB) | DOI: 10.30598/barekengvol16iss4pp1487-1496

Abstract

The flow of data and information is growing rapidly and rapidly in various sizes and means which is called Big Data. In the face of a change for the better in the future, a careful analysis and design of a data processing system is needed, in which a predictive framework can formulate the right policy to be one of the efforts to make a good decision. This is one of the appropriate Big Data processing efforts, which can be realized through one of the methods, namely prediction or forecasting is an effort to predict future values or trends as a reference for analyzing conditions in the past. One example of Big Data in the City of Balikpapan, namely the temperature within 2 meters obtained from the NASA satellite published on the website power.larc.nasa.gov. One of the methods used in this research is the ARIMA method and it is developed according to the data used. Based on the data to be used, namely temperature data within a distance of 2 meters in the city of Balikpapan, the development of data processing is carried out to pay attention to three seasonal patterns or the so-called Triple Seasonal ARIMA model. In this study, it can be seen how to build the Triple Seasonal ARIMA model and comparison with alternative models, namely Seasonal ARIMA and Double Seasonal ARIMA, and can see how the results of the Triple Seasonal ARIMA model accuracy when compared with alternative models. The method used in this study is the Seasonal ARIMA, Double Seasonal ARIMA and Triple Seasonal ARIMA methods. The results obtained in this study obtained a comparison of methods in making predictions with a specified time span, the results obtained from the Seasonal ARIMA model that it was very good at predicting a time span of 2 weeks, Double Seasonal ARIMA for a period of 1 month, Double Seasonal ARIMA for a period of 3 months, and Triple Seasonal ARIMA for a period of 6 months.
Co-Authors Anggriani, Indira Lea Kristi Agustina Nadzir, Zulfikar Adlan Simarmata, nirmawana Simatupang, Syalam Ali Wira Dinata Wijayanti, Goldie Melinda