Judul: Pengaruh Curah Hujan, Angin, dan Kebakaran Hutan terhadap Sebaran dan Konsentrasi Karbon Monoksida
Abstrak: Karbon monoksida (CO) merupakan salah satu senyawa yang diemisikan dari kebakaran hutan dan dapat mengganggu kesehatan bahkan menyebabkan kematian. Penelitian ini bertujuan untuk menggambarkan sebaran spasial dan temporal CO serta kaitannya dengan curah hujan, angin, dan kebakaran hutan. Studi kasus dilakukan di wilayah Kota Palangka Raya tahun 2019. Pengolahan data dilakukan dengan metode penginderaan jauh. Data titik panas diperoleh dari situs FIRMS digunakan pada selang kepercayaan sebesar ≥ 80% untuk mengasumsikan kebakaran hutan, data konsentrasi CO dan angin diperoleh dari situs Giovanni NASA, dan data curah hujan diperoleh dari BMKG Tjilik Riwut Palangka Raya. Hasil penelitian menunjukkan bahwa konsentrasi CO tertinggi terjadi pada September (525 μg/m3) dan terendah pada bulan Juni (104 μg/m3). Konsentrasi CO tertinggi berada di wilayah timur Kecamatan Bukit Batu dan Jekan Raya, dan terendah di Kecamatan Rakumpit. Konsentrasi CO pada musim kemarau (210 μg/m3) lebih tinggi dari musim hujan (116 μg/m3). Jumlah titik panas tertinggi (450 titik) dan kecepatan angin tertinggi (0,73 m/s) terjadi pada bulan September, bertepatan dengan konsentrasi CO tertinggi. Korelasi CO dengan curah hujan bernilai -0,33, dengan jumlah titik panas bernilai 0,94, dan dengan kecepatan angin bernilai 0,73.
Keyword: Angin, Curah hujan, Karbon monoksida, Kebakaran hutan, Kota Palangka Raya

Judul: Analysis of Carbon Monoxide (CO) Distribution in Pekanbaru City and Its Surroundings Due to Forest Fires
Abstrak: Salah satu polutan yang dihasilkan dari asap kebakaran yakni karbon monoksida (CO). Tahun 2015 terjadi kebakaran hutan terbesar sedangkan tahun 2017 luas kebakaran kecil. Indikator yang dapat dilihat sebagai penentu tinggi atau rendahnya intensitas kebakaran hutan yakni hotspot. Polutan CO yang dihasilkan dapat mengganggu kesehatan manusia bahkan sampai di tingkat kematian. Penelitian dilakukan untuk membandingkan sebaran polutan CO tahun 2015 dan tahun 2017, serta melihat korelasi antara jumlah titik panas dengan konsentrasi CO. Data yang digunakan bersumber website giovanni dan firms modaps nasa. Hasil penelitian menunjukkan bahwa terdapat perbedaan signifikan persebaran polutan CO di tahun 2015 dengan 2017. Tahun 2015 diselimuti dengan polutan CO lebih tinggi dibandingkan dengan tahun 2017. Nilai tertinggi konsentrasi CO tahun 2015 sebesar 226,949 µg/m3 sedangkan nilai polutan tertinggi CO tahun 2017 sebesar 109,966 µg/m3. Hubungan antara jumlah titik panas dengan nilai konsentrasi CO tahun 2015 dan 2017 di Kota Pekanbaru dan sekitarnya masing-masing sebesar -0,585 dan -0,602. Konsentrasi CO di Pekanbaru lebih tinggi disebabkan pergerakan angin dan jumlah kendaraan., One of the pollutants produced from fire smoke is carbon monoxide (CO). In 2015 there was the largest forest fire, while in 2017 the fire area was small.. An indicator that can be seen as a determinant of high or low forest fires is hotspots. The resulting CO pollutants can interfere with human health and even lead to death. The study was conducted to compare the amount of CO pollutants in 2015 and 2017, and to see the correlation between hotspots and CO concentrations. The data used is sourced from the giovanni website and firms modaps nasa. The results showed that there was a significant difference in the distribution of CO pollutants in 2015 and 2017. 2015 was covered with higher CO pollutants than 2017. The highest value of CO concentration in 2015 was 226.949 g/ m3 while the highest pollutant value of CO in 2017 was 109.966 g/m3 . The relationship between the number of hotspots and the value of CO concentration in 2015 and 2017 in Pekanbaru City and its surroundings are -0.585 and -0.602, respectively.The higher CO concentration in Pekanbaru is caused by wind movement and number of vehicles.
Keyword: CO concentration, forest fires, hotspots, Riau Province

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