Detection and Prediction of Peatland Cover Changes Using Support Vector Machine and Markov Chain Model

Khaira, Ulfa; Sitanggang, Imas Sukaesih; Syaufina, Lailan

doi:10.12928/telkomnika.v14i1.2400

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Berbagai macam algoritme klasifikasi telah diterapkan dalam mengklasifikasi citra satelit. Penelitian mengenai klasifikasi tutupan lahan gambut di Kabupaten Rokan Hilir dengan menerapkan algoritme Support Vector Machine (SVM) berhasil mengklasifikasikan tutupan lahan dengan kelas vegetasi rapat, vegetasi jarang, dan nonvegetasi, dengan akurasi rata-rata 98,2% [6]. Pada penelitian lainnya, klasifikasi data citra satelit untuk area gambut yang terbakar di Provinsi Riau menerapkan beberapa algoritme pohon keputusan, antara lain algoritme C4.5, algoritme CART, algoritme C5.0, dan algoritme pohon keputusan berbasis autokorelasi spasial.…”

Section: Pendahuluan Kebakaran Hutan Telah Menjadi Bencana Tahunan DIunclassified

Implementasi Algoritme Support Vector Machines untuk Klasifikasi Area Terbakar di Lahan Gambut

Saputra

Khaira

Abidin

2021

JNTETI

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Kebakaran hutan telah menjadi bencana tahunan di Indonesia, yang berdampak pada degradasi lahan. Kebakaran hutan banyak terjadi di lahan gambut. Pada bulan Agustus 2019 terdeteksi sebanyak 810 hotspot di Provinsi Jambi. Informasi luas area kebakaran diperlukan untuk menentukan kebijakan dalam pengelolaan hutan dan lahan. Informasi mengenai luas area kebakaran sulit didapatkan dari pengukuran lapangan karena area yang luas dan tidak mudah diakses. Data Landsat merupakan salah satu jenis citra dari teknologi remote sensing yang dapat digunakan untuk memetakan luas area kebakaran. Salah satu metode yang sering digunakan untuk mengestimasi luas area kebakaran adalah dengan teknik interpretasi visual on screen. Namun, teknik ini memerlukan tenaga interpreter yang berpengalaman. Untuk itu, pada makalah ini digunakan teknik interpretasi digital menggunakan algoritme Support Vector Machine (SVM) untuk mengklasifikasikan area kebakaran di Kabupaten Muaro Jambi pada ekosistem gambut indikatif fungsi lindung, kemudian menghitung estimasi luasan lahan vegetasi, lahan terbuka, dan area terbakar. Makalah ini menghasilkan klasifikasi dengan akurasi sebesar 99,8%. Estimasi luas gambut berdasarkan klasifikasi SVM pada tanggal 15 Agustus 2019 adalah 1.396,89 ha teridentifikasi sebagai area terbakar, 7.069,5 ha adalah area vegetasi, dan 1.089,54 ha merupakan area lahan terbuka.

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Section: Pendahuluan Kebakaran Hutan Telah Menjadi Bencana Tahunan DIunclassified

Implementasi Algoritme Support Vector Machines untuk Klasifikasi Area Terbakar di Lahan Gambut

Saputra

Khaira

Abidin

2021

JNTETI

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“…Due to the progress of computer computing capacity, the calculation of stochastic model is no longer the bottleneck, we give more and more attention to stochastic model, and Markov [12] Random Field is one of the important branches. MRF introduced structure information through the neighborhood system, so that it can express interaction model between spatial relative random variables, and find the solution of the problem according to the statistical decision and estimation theory optimal criterion.…”

Section: Introductionmentioning

confidence: 99%

Binary LDPC Codes Decoding Algorithm Based on MRF and FPGA Implementation

Wang

2016

TELKOMNIKA

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IntroductionLDPC code, low density parity check code [1][2][3], belongs to the linear block code. Its check matrix is sparse matrix of 0 and 1. Generalized LDPC codes have shown relatively good performance. Scholars Gallage brought forward the related theory basis of LDPC codes in 1962 [4]. Because the LDPC code decoding complexity is not high, and its description is relatively simple, it can be realized by hardware, so it immediately draw attention and became academic research hot [5]: increasing the transmission distance, proposing energy-saving scheme and improving anti interference ability, etc [6]. In fact, the research steps of further enhancing the performance of LDPC codes never stop. LDPC codes have been extensively studied in the literature using code modifying techniques such as information nulling or shortening, extending [7,8], puncturing [9], and combining [10]. We focused on studying the structure of LDPC code and LDPC code decoding algorithm, improved LDPC code decoding algorithm became the most crucial part [11].Due to the progress of computer computing capacity, the calculation of stochastic model is no longer the bottleneck, we give more and more attention to stochastic model, and Markov [12] Random Field is one of the important branches. MRF introduced structure information through the neighborhood system, so that it can express interaction model between spatial relative random variables, and find the solution of the problem according to the statistical decision and estimation theory optimal criterion. No matter hard decision or soft decision LDPC code decoding algorithm, after optimizing ring detection algorithm, we put forward the application of Gaussian Markov Random Field model to realize the source parameter estimation, and realize the LDPC code decoding algorithm based on MRF.The best way of testing whether an algorithm is effective is to apply the algorithm to practice it. So we use FPGA [13] model to realize the algorithm, and to verify the effectiveness, so that it can provide guidance when we use this algorithm in bad communication environment.

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Techno-Socio Approaches in Peatland Fire Control in Indonesia

Syaufina

Sitanggang

2020

IOP Conf. Ser.: Earth Environ. Sci.

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Detection and Prediction of Peatland Cover Changes Using Support Vector Machine and Markov Chain Model

Abstract: Detection and prediction of peatland cover changes needs to be done in the rapid

Cited by 3 publications

References 15 publications

Implementasi Algoritme Support Vector Machines untuk Klasifikasi Area Terbakar di Lahan Gambut

Implementasi Algoritme Support Vector Machines untuk Klasifikasi Area Terbakar di Lahan Gambut

Binary LDPC Codes Decoding Algorithm Based on MRF and FPGA Implementation

Techno-Socio Approaches in Peatland Fire Control in Indonesia

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