Judul: Fluctuation Ratio PM2.5/PM10 in the Wet and Dry Season (Case Study: Bogor)
Abstrak: Partikulat pencemar udara yang berbahaya bagi kesehatan dan sering dianalisis adalah partikel berdiameter kurang dari 10µm (PM10) dan kurang dari 2.5µm (PM2.5). Rasio PM2.5/PM10 dapat menjadi salah satu indikator yang memberi gambaran karakteristik dan dominasi sumber emisi partikulat pencemaran udara. Penelitian ini bertujuan untuk menganalisis besaran rasio PM2,5/PM10 di Baranangsiang (Kota Bogor) dan Cibeureum (Puncak, Kabupaten Bogor), serta mengetahui fluktuasi rasio PM2.5/PM10 pada musim hujan dan musim kemarau. Data yang digunakan adalah data konsentrasi PM2.5, PM10, serta faktor meteorologi dari kedua lokasi pada tahun 2020. Penelitian menggunakan analisis korelasi dan regresi antara nilai rasio PM2.5/PM10 dengan faktor meteorologi menggunakan package openair di RStudio dan Microsoft Excel. Nilai Rasio PM2.5/PM10 per 3 jam selama tahun 2020 berkisar 0.2-0.8 di Baranangsiang dan 0.03-0.7 di Cibeureum. Persentase nilai rasio PM2.5/PM10 ≥ 0.5 sebesar 32% di Baranangsiang dan 23% di Cibeureum. Ini menunjukkan proporsi konsentrasi PM2.5 di Baranangsiang lebih tinggi daripada di Cibeureum. Pada musim hujan yang diwakili bulan basah (Februari 2020) dan musim kemarau pada bulan kering (Agustus), persentase nilai rasio PM2.5/PM10 ≥ 0.5 lebih besar pada bulan kering, dan lebih besar di Baranangsiang (42%) daripada di Cibeureum (37%). Ini menunjukkan jenis sumber emisi partikulat di Baranangsiang berbeda dengan di Cibeureum. Pengaruh faktor meteorologi terhadap fluktuasi rasio PM2.5/PM10 berdasar analisis regresi berganda lebih jelas pada musim kemarau, dan lebih besar di Baranangsiang (R2=56.1%) dibandingkan dengan Cibeureum (R2=43.4%). Penelitian selanjutnya diharapkan dapat mengkaji mengenai fluktuasi rasio PM2.5/PM10 dan pengaruh unsur-unsur meteorologi dengan menggunakan rentang waktu data yang lebih panjang untuk melihat kestabilan dan konsistensinya, Air pollutant particulates that are hazardous to health and are often analyzed are particles less than 10µm in diameter (PM10) and less than 2.5µm (PM2.5). The PM2.5/PM10 ratio can be an indicator that describes the characteristics and dominance of air pollution particulate emission sources. This study aims to analyze the PM2.5/PM10 ratio in Baranangsiang (Bogor City) and Cibeureum (Puncak, Bogor Regency), as well as to determine the fluctuations in the PM2.5/PM10 ratio in the wet and dry season. The data used are PM2.5, PM10 concentration data, and meteorological factors from both locations in 2020. The study uses correlation and regression analysis between the PM2.5/PM10 ratio value and meteorological factors using the openair package at RStudio and Microsoft Excel. The value of the PM2.5/PM10 ratio per 3 hours during 2020 ranges from 0.2- 0.8 in Baranangsiang and 0.03-0.7 in Cibeureum. The percentage of PM2.5/PM10 ratio > 0.5 is 32% in Baranangsiang and 23% in Cibeureum. This shows the proportion of PM2.5 concentration in Baranangsiang is higher than in Cibeureum. In the wet season represented by the wet month (February 2020) and the dry season in the dry month (August), the percentage of the PM2.5/PM10 ratio>0.5 was greater in the dry month, and was greater in Baranangsiang (42%) than in Cibeureum (37%). This shows that the types of particulate emission sources in Baranangsiang are different from those in Cibeureum. The influence of meteorological factors on fluctuations in the PM2.5/PM10 ratio based on multiple regression analysis was more pronounced in the dry season, and was greater in Baranangsiang (R2=56.1%) compared to Cibeureum (R2=43.4%). Future research
Keyword: Faktor Meteorologi, Musim Hujan, Musim Kemarau, Partikulat, Rasio  PM2.5/PM10

Judul: Contribution of Long-Range Transport Towards PM 2.5 Concentration (Study Case: Baranangsiang, Bogor)
Abstrak: Fenomena long-range transport (LRT), merupakan pengangkutan polutan dari daerah lain dengan jarak lebih dari 100 km. Perlu dilakukan identifikasi potensi sumber PM2.5 dan melihat fluktuasi konsentrasi PM2.5 di Kota Bogor. LRT dapat divisualisasikan menggunakan lintasan mundur HYSPLIT dan klustering. Selain itu, metode Potential source contribution function (PSCF) digunakan untuk menilai probabilitas dan metode Concentration weighted trajectory CWT untuk melihat nilai konsentrasi dominan. Sumber potensial dan tingkat konsentrasi dapat bervariasi berdasarkan musim di wilayah tersebut. Pada bulan kering sumber polutan dominan berasal dari arah timur-tenggara sebesar 57.7% dan pada bulan basah berasal dari arah barat-barat daya sebesar 67.1%. dan 32.9 %. Konsentrasi maksimum rataan bulanan PM2.5 di Kota Bogor terjadi pada bulan Juli dengan nilai sebesar 25 µg/??3 dan minimum pada bulan November sebesar 10 µg/ ??3 . Secara umum faktor kecepatan angin curah hujan, dan kelembaban memiliki hubungan yang negatif dengan PM2.5. Jika kecepatan angin tinggi maka polutan akan rendah, curah hujan tinggi maka konsentrasi menurun, dan kelembaban tinggi konsentrasi akan turun., The phenomenon of long-range transport (LRT) is that pollutants can be transported from other areas. Identify the potential source of PM2.5 in Bogor City and see the fluctuation of concentration of PM2.5in Bogor City. Transport distance with a backward trajectory using the HYSPLIT model, clustering. Use PSCF for knowing probability and knowing concentration using CWT method. It is important to understand potential sources and concentration levels may vary seasonally in the region, potential sources will follow the season of the area. The dry month was the dominant pollutant from east-southeast was 57.7% and in the wet month from the west-southwest 67.7%. Concentration of PM2.5in Bogor City maximum is 25 µg/??3 and the minimum is 10 µg/??3 in July. Because meteorology factors can impact, such as wind, humidity, and rainfall, they have a negative correlation. If humidity and wind increased the concentration will be decreased, rainfall increases the concentration will be decreased.
Keyword: Concentration weighted trajectory, Long-range transport, PM2.5, Potential Source Contribution Function

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