Judul: Identification Hotspot and Burned Area in Merauke District, South Papua Province.
Abstrak: Provinsi Papua Selatan merupakan salah satu provinsi yang tergolong rawan terhadap kebakaran hutan dan lahan. Salah satu kabupaten yang sering mengalami kebakaran adalah Merauke. Tujuan penelitian ini adalah mengidentifikasi sebaran hotspot dan area bekas kebakaran hutan dan lahan di Kabupaten Merauke Provinsi Papua Selatan pada tahun 2015 dan 2019. Identifikasi hotspot dilakukan dengan menggunakan satelit Terra/Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) yang dapat mengidentifikasi area di permukaan bumi yang memiliki suhu lebih tinggi dibandingkan dengan area sekitarnya. Analisis data dilakukan dengan mengidentifikasi sebaran hotspot pada setiap jenis tutupan lahan, serta identifikasi area bekas terbakar dengan buffering hotspot dengan jarak 1 km x 1 km. Total jumlah hotspot tahun 2015 adalah 2671 dengan 2611 hotspot pada lahan mineral dan 60 hotspot pada lahan gambut. Total hotspot tahun 2019 adalah 534 dengan 524 hotspot pada lahan mineral dan 10 hotspot pada lahan gambut. Luas area bekas terbakar pada tahun 2015 di lahan mineral sebesar 98% dan pada lahan gambut sebear 2% dari seluruh luas area bekas terbakar di Kabupaten Merauke. Luas area bekas terbakar pada tahun 2019 juga 98% nya terjadi di lahan mineral dan pada lahan gambut sebesar 2%. Kebakaran hutan dan lahan pada tahun 2015 dan 2019 terbesar terjadi pada jenis tutupan lahan semak belukar rawa, South Papua Province is one of fire prone province. The districts that frequently experiences forest and land fires is Merauke. The objective of this study were to identify the distribution of hotspots and burned area in Merauke District, South Papua Province in 2015 and 2019. Hotspot identification was obtained from Terra/Aqua-MODIS (Moderate Resolution Imaging Spectroradiometer) satellite which can identify areas on the earth's surface that are has a higher temperature than the surrounding area. Data analysis was carried out by identifying the distribution of hotspots in each type of land cover, as well as identifying burnt areas by buffering hotspots with a distance of 1 km x 1 km. The total number of hotspots in 2015 was 2671 with 2611 hotspots on mineral soil and 60 hotspots on peatlands. The total hotspots in 2019 was 534 with 524 hotspots on mineral soil and 10 hotspots on peatlands. The area burned in 2015 was 98% on mineral soil and 2% on peatland of the total burned area in Merauke District. The area burned in 2019 was 98% on mineral soil and 2% on peatland. The largest forest and land fires in 2015 and 2019 were found in swamp.
Keyword: burned area, hotspot, forest fire, Merauke

Judul: Hotspot Accuracy Between MODIS and VIIRS in Detecting Forest and Land Fires in Merauke District South Papua Province
Abstrak: Kabupaten Merauke merupakan salah satu wilayah di Provinsi Papua Selatan yang memiliki jumlah hotspot tertinggi dalam satu dekade terakhir. Penelitian bertujuan menganalisis akurasi hotspot antara MODIS dan VIIRS dalam mendeteksi kebakaran hutan dan lahan di Kabupaten Merauke. Sumber yang digunakan dalam penelitian adalah citra Sentinel-2 dan data hotspot dari sensor MODIS Terra/Aqua dan NPP-VIIRS. Hasil penelitian menunjukkan adanya penurunan jumlah hotspot pada rentang tahun 2019-2021 dan puncak hotspot terjadi pada tahun 2019. Jumlah hotspot pada rentang tahun tersebut adalah 15.922 titik dari sensor VIIRS dan 3067 titik dari sensor MODIS. Akurasi deteksi kemunculan titik api tertinggi oleh VIIRS sebesar 6508 hotspot (27,72%). Jangkauan buffer paling akurat dalam mendeteksi kebakaran hutan dan lahan yaitu 1 km, dengan selang kepercayaan hotspot pada kategori sedang atau 30%-80%.
Keyword: buffer, hotspot, Merauke District, forest and land fires, confidence level

Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm