Judul: Peran Curah Hujan Terhadap Penurunan Hotspot Kebakaran Tahun 2016
Abstrak: Kebakaran hutan dan lahan bukan merupakan fenomena baru bagi beberapa wilayah Indonesia, seperti di Pulau Kalimantan dan Sumatera. Wilayah Riau, Sumatera Selatan, Kalimantan Barat, dan Kalimantan Tengah merupakan beberapa provinsi yang hampir setiap tahun terjadi kebakaran hutan dan lahan. Iklim merupakan salah satu faktor alami yang dapat menyebabkan terjadinya suatu kebakaran hutan di Indonesia. Tujuan penelitian ini adalah untuk menganalisis hubungan antara hotspot dengan curah hujan terhadap terjadinya penurunan hotspot di Indonesia tahun 2016. Hasil penelitian ini menunjukkan bahwa jumlah hotspot tinggi seiring dengan terjadinya musim kemarau dimana curah hujan rendah, yaitu pada bulan Juli sampai bulan Oktober pada tahun 2015 dan bulan Juli sampai Agustus pada tahun 2016. Secara statistik dapat dilihat bahwa antara jumlah curah hujan bulanan dengan jumlah hotspot bulanan memiliki korelasi negatif (-) yang mempunyai hubungan sedang dimana nilai Rsquare sebesar 42.5% dan memiliki nilai P-value yang didapat dari hasil uji korelasi sebesar 0.001 dengan nilai persamaan y = 3182 – 2.50x dimana y adalah jumlah hotspot dan x adalah curah hujan.
Keyword: curah hujan, hotspot, kebakaran

Judul: Pengaruh Curah Hujan terhadap Penurunan Titik Panas (Hotspot) di Indonesia Pada Tahun 2019-2020
Abstrak: Kebakaran hutan dan lahan merupakan fenomena yang sering terjadi di Indonesia setiap tahunnya. Terlebih banyaknya perubahan alih fungsi lahan, seperti di Pulau Sumatera dan Pulau Kalimantan. Hal tersebut dapat dilihat seperti terjadi di provinsi Jambi, Sumatera Selatan, Kalimantan Barat dan Kalimantan Tengah. Faktor iklim dapat mempengaruhi terjadinya kebakaran hutan dan lahan. Tujuan penelitian ini adalah mengetahui hubungan antara titik panas dengan curah hujan terhadap terjadinya penurunan kebakaran hutan dan lahan di Indonesia pada tahun 2019 dan khususnya 2020 di provinsi Jambi, Sumatera Selatan, Kalimantan Barat dan Kalimantan Selatan. Penelitian ini menggunakan data titik panas NASA MODIS, peta administrasi, dan data curah hujan dari BMKG. Hasil penelitian ini menunjukkan bahwa curah hujan sangat berpengaruh terhadap jumlah titik panas, apabila curah hujan rendah maka titik panas tinggi dan sebaliknya. Bulan Juli sampai November menjadi musim kering pada tahun 2019, sedangkan tahun 2020 pada bulan Agustus sampai Oktober. Dari hasil uji korelasi P-Value hanya Provinsi Kalimantan Selatan yang dibawah batas 0,05 yaitu 0,041. Sedangkan dari hasil uji korelasi R-Person Provinsi Jambi memilki notasi psositif (+). Hal ini menandakan curah hujan tidak berpengaruh nyata terhadap jumlah titik panas di kedua provinsi tersebut., Forest and land fires (karhutla) are a phenomenon that often occurs in Indonesia every year. Moreover, there are many changes in land use, such as on the islands of Sumatra and Kalimantan. For example in Jambi, South Sumatra, West Kalimantan and Central Kalimantan. Climatic factors can affect the occurrence of forest and land fires. The purpose of this study was to determine the relationship between hotspots and rainfall on the occurrence of forest and land fires in Indonesia 2019 to 2020. This study used NASA MODIS hotspot data, administrative maps, and rainfall data from the BMKG. The results of this study indicate that rainfall is very influential on the number of hotspots, if the rainfall is low then the hotspots is high and vice versa. July to November is the dry season in 2019, while 2020 is from August to October. From the results of the P-Value correlation test, only South Kalimantan Province is below the 0.05 limit, which is 0.041. Meanwhile, from the results of the R-Person correlation test, Jambi Province has a positive notation (+). Wich means that rainfall has no effect on the number of hotspot in the two provinces.
Keyword: fire, hotspot, precipitation, modis, correlation

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