Judul: Mapping Distribution of Nitrogen Dioxide (NO2) Pollutants  Due to Forest and Land Fires in Dumai City
Abstrak: Kota Dumai merupakan salah satu daerah di Provinsi Riau yang memiliki kasus kebakaran hutan dan lahan yang tinggi. Kebakaran hutan dan lahan akan berdampak buruk pada kualitas udara apabila tidak dilakukan penanggulan yang baik. Oleh karena itu perlu dilakukannya peninjauan lebih lanjut terkait pencemaran udara terutama yang disebabkan oleh polutan NO2 dengan menggunakan metode yang terukur. Tujuan penelitian ini adalah mengetahui pemetaan sebaran polutan Nitrogen Dioksida (NO2) akibat kebakaran hutan dan lahan di Kota Dumai yang terjadi pada tahun 2019. Metode yang digunakan adalah penginderaan jauh dengan menggunakan data Ozone Monitoring Instrument (OMI) NO2 dan data curah hujan yang didapatkan dari satelit Tropical Rainfall Measuring Mission (TRMM). Prosedur penelitian dimulai dengan pengumpulan dan pengolahan data. Kemudian dilakukan pembagian periode analisis berdasarkan pola curah hujan bulanan, analisis sebaran polutan NO2 menjadi 4 periode, dan visualisasi menggunakan software ArcGis. Konsentrasi NO2 di Kota Dumai pada tahun 2019 berada pada rentang 28 – 39 µg/m3 dan masih berada dibawah baku mutu sebesar 150 µg/m3. Curah hujan dan konsentrasi NO2 memiliki korelasi yang negatif, dimana konsentrasi NO2 akan tinggi pada saat curah hujan rendah., Dumai City is one of the areas in Riau Province that has high cases of forest and land fires. Forest and land fires will have a negative impact on air quality if good management is not carried out. Therefore, it is necessary to conduct a further review related to air pollution, especially those caused by NO2 pollutants using measured methods. The purpose of this study was to find out the mapping of the distribution of Nitrogen Dioxide (NO2) pollutants due to forest and land fires in Dumai City that occurred in 2019. The method used was remote sensing using Ozone Monitoring Instrument (OMI) NO2 data and rainfall data obtained from Tropical Rainfall Measuring Mission (TRMM) satellite. The research procedure begins with data collection and processing. Then the analysis period is divided based on monthly rainfall patterns, analysis of NO2 pollutant distribution into 4 periods, and visualization using ArcGIS software. The concentration of NO2 in Dumai City in 2019 was in the range of 28 – 39 g/m3 and was still below the quality standard of 150 g/m3. Rainfall and NO2 concentration have a negative correlation, where NO2 concentration will be high when rainfall is low.
Keyword: Kota Dumai, nitrogen dioksida, pencemaran udara, penginderaan jauh

Judul: Estimation of Forest, Plantation, and Land Fires Severity Using Remote Sensing in Dumai, Riau
Abstrak: Kebakaran hutan, kebun, dan lahan merupakan peristiwa terbakarnya hutan dan/atau lahan yang menimbulkan kerugian diberbagai sektor seperti bahaya bencana ekologi, ekonomi, sosial budaya, dan politik. Karhutbunla terjadi akibat dua faktor yaitu faktor alami dan aktivitas manusia. Proses alami terjadinya karhutbunla disebabkan oleh aktivitas pembakaran yang tidak sengaja dan tidak terkendali seperti letusan gunung berapi, sambaran petir pada pohon, serta faktor iklim dan cuaca lainnya. Kota Dumai merupakan salah satu areal terbakar terluas pada tahun 2023. Kota Dumai memiliki lahan gambut seluas 163,141 ha (78%) yang mudah terbakar akibat adanya bahan organik pada tanah. Penelitian ini mengestimasi tingkat keparahan kebakaran hutan, kebun, dan lahan di Kota Dumai pada 2023 menggunakan penginderaan jauh. Penelitian ini menggunakan metode ekstraksi indeks Normalized Burn Ratio yang memetakan area terbakar dan tingkat keparahannya. Hasil menunjukkan area terbesar terjadinya karhutbunla yaitu pada tingkat keparahan “rendah” dengan luas 3.909,67 ha (1,72%). Akurasi menunjukkan tingkat baik (86,67%) untuk identifikasi area terbakar dan tingkat cukup (69%) untuk identifikasi tingkat keparahan kebakaran., Forest, plantation, and land fires are the burning land area that cause losses in sectors such as ecological, economic, social culture, and political dangers and catastrophes. Forest, plantation, and land fires come as a result of two factors: natural factors and human activities. The natural process of the burning is caused by involuntary and uncontrolled burning activities such as volcanic eruptions, lightning strikes in trees, and other climate and weather factors. Dumai city was one of the largest cities with the burning area in 2023. Dumai city had a peat land area of 163,141 ha (78%) that has a tendenct to be flammable because of the accumulation of organic matter in the soil. This study will estimate the severity of forest, plantation, and land fires of Dumai city in 2023 by using remote sensing technology. This research method used is the normalized burn ratio that maps the burn area with its severity. The analysis of the extraction index shows the largest burn areas of forest, plantation, and land area on “low severity” class with an area of 3.909,67 ha (1,72%). Accuracy testing results in a level of good accuracy (86,67%) on burnt area identification and edaquate accuracy (69%) on the burnt severity identification.
Keyword: area terbakar, kebakaran hutan dan lahan, penginderaan jauh

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