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Analisis Karakteristik Kekeringan DAS Kapuas Kalimantan Barat Berdasarkan Luaran Global Climate Model Ihwan, Andi; Pawitan, Hidayat; Hidayat, Rahmat; Latifah, Arnida Lailatul; Taufik, Muh.
POSITRON Vol 9, No 2 (2019): November Edition
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam, Univetsitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/positron.v9i2.35072

Abstract

Daerah aliran sungai (DAS) Kapuas, walaupun berada di wilayah benua maritim Indonesia dengan curah hujan yang tinggi sepanjang tahun, namun sering mengalami kebakaran lahan dan hutan. Bencana kebakaran lahan dan hutan tersebut merupakan dampak dari kekeringan yang berkepanjangan. Informasi tentang karakteristik kekeringan di wilayah DAS Kapuas masih kurang diungkap terutama terkait dengan penggunaan data iklim global. Penelitian ini bertujuan untuk menganalisis karakteristik kekeringan meteorologis dan kekeringan hidrologis DAS Kapuas. Analisis kekeringan meteorologis digunakan pendekatan Standardize Precipitation Index (SPI) dan kekeringan hidrologis digunakan Standarized Runoff Index (SRI). Data curah hujan dan runoff dari Global Climate Model (GCM) yang telah di-downscaling menjadi 20 km x 20 km digunakan sebagai input data. Berdasarkan indeks kekeringan skala satu bulanan selama 30 tahun (1981-2010), diperoleh bahwa DAS Kapuas telah mengalami kekeringan meteorologis sebanyak 45 kali dan 48 kali kekeringan hidrologis dengan kategori moderat kering sampai dengan ekstrim kering. Luas wilayah yang mengalami kekeringan meteorologis maksimum terjadi pada tahun 1986 yakni 11,01% dari total wilayah DAS, kekeringan hidrologis maksimum terjadi pada tahun 1991 yakni 13,9% dari total wilayah DAS. Durasi kejadian kedua jenis kekeringan tersebut dominan berdurasi satu bulan. Luas wilayah kekeringan, tingkat keparahan, frekuensi, dan durasi kekeringan cenderung meningkat saat kejadian El-Niño. Hasil analisis karakteristik kekeringan menunjukkan bahwa data GCM dapat digunakan untuk analisis kekeringan di DAS Kapuas.
Parallel Algorithms for Spatial Rainfall Distribution Latifah, Arnida Lailatul; Nurhadiyatna, Adi
INKOM Journal Vol 8, No 1 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.197 KB) | DOI: 10.14203/j.inkom.383

Abstract

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.
PENGARUH FAKTOR ALAMI DAN ANTROPOGENIK TERHADAP LUAS KEBAKARAN HUTAN DAN LAHAN DI KALIMANTAN Mareta, Lesi; Hidayat, Rahmat; Hidayati, Rini; Latifah, Arnida Lailatul
Jurnal Tanah dan Iklim (Indonesian Soil and Climate Journal) Vol 43, No 2 (2019)
Publisher : Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21082/jti.v43n2.2019.147-155

Abstract

Abstrak. Kebakaran hutan dan lahan (karhutla) di Indonesia khususnya di Kalimantan menjadi ancaman bagi pembangunan berkelanjutan karena efeknya secara langsung bagi ekosistem, berkontribusi pada peningkatan emisi karbon dan berdampak pada keanekaragaman hayati. Karhutla dipengaruhi oleh faktor alami dan faktor antropogenik oleh aktivitas manusia. Penelitian ini bertujuan untuk mendapatkan gambaran pengaruh faktor alami dan antropogenik secara terpisah terhadap luas kebakaran hutan dan lahan di Kalimantan. Pengaruh faktor alami dan antropogenik terhadap luas karhutla dianalisis dari data luaran model CMIP5 dengan teknik statistik Random Forests. Penelitian menggunakan data iklim dan data indeks karhutla. Data iklim terdiri dari variabel kelembaban relatif permukaan, suhu udara permukaan, dan curah hujan yang diperoleh dari luaran model MRI-CGCM3 CMIP5. Data indeks karhutla di Kalimantan diperoleh dari data Global Fire Emissions Database (GFED). Hasil analisis pada periode data tahun 1997 sampai dengan 2005 memperlihatkan karhutla terluas di Kalimantan terjadi pada tahun 1997 dan 2002. Variasi musiman historis luas karhutla di Kalimantan menunjukkan peningkatan pada bulan Juni, mencapai puncaknya pada bulan September dan mulai berkurang pada bulan November. Pada bulan Juni hingga Juli, faktor antropogenik bernilai positif yang berarti mengurangi kejadian kebakaran, sedangkan pada bulan Agustus hingga Oktober faktor antropogenik bernilai negatif, menyebabkan lebih banyak peristiwa karhutla.Abstract. Forest and land fires in Indonesia, especially in Kalimantan, are considered as a threat to sustainable development because of their direct effect on ecosystems, their contribution to increasing carbon emissions, and their impact on biodiversity. Forest and land fires are influenced by two main factors, namely climate conditions, and human activity (anthropogenic) factors. The objective of this research was to analyze the influence of natural and anthropogenic factors on the area of forest and land fires in Kalimantan. The anthropogenic effects on the area of burn scars can be analyzed by using the output of the CMIP5 model with statistical techniques, Random Forests. The data used are climate data and index data on forest and land fires in Kalimantan. Climate data consist of the variables: surface relative humidity, surface air temperature, and rainfall which were obtained from the output of the MRI-CGCM3 CMIP5 model. Indices of Forest and land fires in Kalimantan were obtained from Global Fire Emissions Database (GFED). The results of the analysis showed that extensive forest and land fires during the period of 1997 to 2005 in Kalimantan, occurred in 1997 and 2002. Historically extensive seasonal variations of Forest and land fires in Kalimantan increased in June, reaching the peak in September and decreased in November. Between June and July, anthropogenic factors positively influenced (causing less burned area), while from August to October had a negative effect (causing larger) burned areas.
Prediksi Luas Kebakaran Hutan dan Lahan pada Tahun 1997-2005 Akibat Faktor Antropogenik Menggunakan Data CMIP5 Arnida Lailatul Latifah; Lesi Mareta; Rahmat Hidayat; Rini Hidayati
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 11 No. 2 (2021): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.11.2.324-333

Abstract

Kebakaran hutan dan lahan (Karhutla) merupakan sebuah bencana lokal dan nasional tahunan yang ada di Indonesia. Kebakaran hutan dan lahan secara garis besar dipengaruhi oleh dua faktor, yaitu terjadi karena alami (Natural forcing) dan/atau aktivitas manusia (Anthropogenic forcing). Aktivitas manusia tersebut melepaskan sejumlah besar karbon dioksida (CO2), karbon monoksida (CO), metana (CH4), oksidanitrat, nitrogen dioksida (NOx) dan partikulat yang bertindak sebagai sumber pemanasan rumah kaca yang telah dipantau oleh satelit beberapa tahun terakhir. Penelitian ini mengkaji luas karhutla dalam beberapa dekade terakhir akibat pengaruh faktor antropogenik di Kalimantan menggunakan dua jenis kelompok data yang akan dianalisa yaitu data tanpa dan dengan komponen antropogenik. Analisa dilakukan dengan memanfaatkan data luaran CMIP5. Studi ini menggunakan pendekatan statistik teknik Random Forests (RF) untuk mengevaluasi kontribusi faktor iklim dan antropogenik terhadap luas karhutla di daerah Kalimantan. Kondisi umum luas karhutla berdasarkan data observasi yang diperoleh dari data GFED. Dua luas tertinggi yang terjadi di Kalimantan selama periode 1997 hingga 2005 terjadi pada tahun 1997 dan 2002 Menurut ketiga model pada tahun 1997 dan 2002 terlihat bahwa faktor antropogenik memberikan pengaruh lebih dominan terhadap luas karhutla di Kalimantan. Pada tahun 1997 dan 2002 luas karhutla akibat pengaruh antropogenik bernilai positif (menyebabkan luas karhutla meningkat).
Parallel Algorithms for Spatial Rainfall Distribution Arnida Lailatul Latifah; Adi Nurhadiyatna
INKOM Journal Vol 8, No 1 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.inkom.383

Abstract

This paper proposes parallel algorithms for precipitation of flood modelling, especially applied in spatial rainfall distribution. As an important input in flood modelling, spatial distribution of rainfall is always needed as a pre-conditioned model. In this paper two interpolation methods, Inverse distance weighting (IDW) and Ordinary kriging (OK) are discussed. Both are developed in parallel algorithms in order to reduce the computational time. To measure the computation efficiency, the performance of the parallel algorithms are compared to the serial algorithms for both methods. Findings indicate that: (1) the computation time of OK algorithm is up to 23% longer than IDW; (2) the computation time of OK and IDW algorithms is linearly increasing with the number of cells/ points; (3) the computation time of the parallel algorithms for both methods is exponentially decaying with the number of processors. The parallel algorithm of IDW gives a decay factor of 0.52, while OK gives 0.53; (4) The parallel algorithms perform near ideal speed-up.
Epidemic Data Analysis of Three Variants of COVID-19 Spread in Indonesia Inna Syafarina; Taufiq Wirahman; Syam Budi Iryanto; Arnida Lailatul Latifah
Jurnal Ilmu Komputer dan Informasi Vol 15, No 1 (2022): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v15i1.1055

Abstract

Three variants of COVID-19 had been found in Indonesia. A control strategy may rely on the transmission rate of the variant. This study aims to investigate how the variants spread in Indonesia by computing a basic and effective reproduction number on the national and province scale. The basic reproduction number shows the indicator of initial transmission rate of alpha variant computed by an exponential growth rate model. The effective reproduction number describes the dynamic of the transmission rate estimated based on a Bayesian approach. This study revealed that each variant shows different characteristics. The alpha variant of COVID-19 in Indonesia was mainly initiated from big cities, then it spread to all provinces quickly because the control strategies were not established well at the beginning. A rapid increase of the effective reproduction number about July 2021 showed a novel delta variant, but it could be managed quite well by a large number of testing and stronger restrictions. Before the end of 2021, a novel variant omicron was also shown by the steeper change of the effective reproduction number. Thus, the variant spread rate can be estimated by how steep the effective reproduction number change is.
Dissipation of Solitary Wave Due To Mangrove Forest: A Numerical Study by Using Non-Dispersive Wave Model Didit Adytia; Semeidi Husrin; Arnida Lailatul Latifah
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 24, No 1 (2019): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (734.02 KB) | DOI: 10.14710/ik.ijms.24.1.41-50

Abstract

In this paper, we study a dissipation of solitary wave due to mangrove forest by using numerical simulation. Here, the solitary wave is chosen to represent tsunami wave form.  To simulate the wave dynamic, we use the non-dispersive Nonlinear Shallow Water Equations (NSWE). The model is implemented numerically by using finite volume method in a momentum conservative staggered grid. By using the proposed numerical scheme, the numerical code is able to simulate solitary wave breaking phenomenon. Wave dissipation due to mangrove forest is modelled as bottom roughness with an approximate value of manning roughness, which is derived from the classical Morisson’s formula. To test the modelled dissipation by mangrove forest, we reconstruct a physical experiment in hydrodynamic laboratory where a solitary wave propagates above a sloping bottom, which has a parameterized mangrove in the shallower part.  Two cases are performed to test the performance of the numerical implementation, i.e. the non-breaking and breaking solitary waves. Results of simulation agree quite well with the measurement data. The results of simulation are also analyzed quantitatively by calculating errors as well as correlation with the measurement data. Moreover, to investigate effects of wave steepness on solitary wave, to the reduction of wave energy, we perform numerical investigation. Various solitary waves with different wave steepness are simulated to see their effects on amplitude and energy reduction due to mangrove forest.
Pengaruh koreksi bias dan metode ensemble pada data curah hujan dari empat model luaran Regional Climate Model (RCM) CORDEX-SEA di Sumatera Irza Arnita Nur; Rahmat Hidayat; Arnida Lailatul Latifah; Misnawati
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol. 11 No. 1 (2021): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Graduate School Bogor Agricultural University (SPs IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.11.1.49-56

Abstract

Drought is a natural disaster that occurs slowly and lasts longer until the wet season occurred. Drought occurred in expected time, so that preparations and preparedness can be made in dealing with drought disasters. Therefore, we need an overview of future drought events (or projections).In this study, Standardized Precipitation Index (SPI) was used as drought index. The occurrence of drought is closely related to weather factors and occurs repeatedly. Time-series weather data is needed to know the time-series weather conditions. Problems with data that often occur can be overcome by using numerical climate modeling which is currently widely used. Regional Climate Model (RCM) is a climate model that can be used to build long-term climate data, both time-series and projection data. The results showed RCM model data required bias correction in order to reduce bias in the CORDEX RCM model data. RCM rainfall models before correction were still biased. Thus, bias correction is needed to reduce bias in models data. Time series obtained from SPI baseline data for 2000-2005 in Lampung and West Sumatra provinces showed SPI value which smaller than the projection SPI value in 2021-2030. While SPI time series with RCP 4.5 and 8.5 scenarios showed different results. SPI with RCP 8.5 scenario have more negative value so that drought occurred more often than RCP 4.5. The negative SPI index that often occured in RCP 8.5 scenario appeared to be in RCM IPSL and MPI models year 2025-2030.
Model Time-Series untuk Prediksi Kebakaran Hutan Klimatologis di Kalimantan Furqon Hensan Muttaqien; Inna Syafarina; Intan Nuni Wahyuni; Arnida Lailatul Latifah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 13 No 1 (2022): Vol. 13, No. 1 April 2022
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2022.v13.i01.p04

Abstract

Areas covered by tropical forests, such as Borneo, are vulnerable to fires. Previous studies have shown that climate data is one of the critical factors affecting forest fire. This study aims to predict the forest fire over Borneo by considering the temporal aspects of the climate data. A time seriesbased model, Long Short-Term Memory (LSTM), is used. Three LSTM models are applied: Basic LSTM, Bidirectional LSTM, and Stacked LSTM. Three different experiments from January 1998 to December 2015 are conducted by examining climate data, Oceanic Nino Index (ONI), and Indian Ocean Dipole (IOD) index. The proposed model is evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and correlation number. As a result, all models can capture the spatial and temporal pattern of the forest fires for all three experiments, in which the best prediction occurs in September with a spatial correlation of more than 0.75. Based on the evaluation metrics, Stacked LSTM in Experiment 1 is slightly superior, with the highest annual pattern correlation (0.89) and lowest error (MAE= 0.71 and RMSE=1.32). This finding reveals that an additional ONI and IOD index as the prediction features would not improve the model performance generally, but it specifically improves the extreme event value.
Comparison of various epidemic models on the COVID-19 outbreak in Indonesia Intan Nuni Wahyuni; Ayu Shabrina; Inna Syafarina; Arnida Lailatul Latifah
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14222

Abstract

This paper compares four mathematical models to describe Indonesia's current coronavirus disease 2019 (COVID-19) pandemic. The daily confirmed case data are used to develop the four models: Logistic, Richards, SIR, and SEIR. A least-square fitting computes each parameter to the available confirmed cases data. We conducted parameterization and sensitivity experiments by varying the length of the data from 60 until 300 days of transmission. All models are susceptible to the epidemic data. Though the correlations between the models and the data are pretty good (>90%), all models still show a poor performance (RMSE>18%). In this study case, Richards model is superior to other models from the highest projection of the positive cases of COVID-19 in Indonesia. At the same time, others underestimate the outbreak and estimate too early decreasing phase. Richards model predicts that the pandemic remains high for a long time, while others project the pandemic will finish much earlier.