Judul: Influence of Meteorological Factors on PM2.5 and PM10 Concentration Fluctuation (Case Study: Jakarta)
Abstrak: Jakarta sebagai ibukota negara Indonesia merupakan salah satu kota dengan aktivitas penduduk, transportasi dan industri yang tinggi. Hal ini mendorong semakin tingginya pencemaran udara di Jakarta. Terdapat berbagai faktor yang mempengaruhi pencemaran udara, salah satunya faktor meteorologi. Faktor meteorologi dapat mempengaruhi fluktuasi konsentrasi polutan di udara, seperti suhu udara, kelembaban, curah hujan, radiasi, kecepatan dan arah angin. Penelitian ini bertujuan untuk mengetahui fluktuasi konsentrasi PM2.5 dan PM10 di Jakarta serta interaksi antara faktor meteorologi dengan konsentrasi partikulat. Interaksi antara faktor meteorologi dan konsentrasi partikulat ditunjukkan melalui korelasi pearson dan menggunakan package openair di RStudio. Hasil penelitian menunjukkan konsentrasi PM2.5 dan PM10 di Jakarta memiliki puncak pada siang hari, menurun saat menuju sore hari dan kembali meningkat pada saat menuju malam hari. Interaksi antara faktor meteorologi dengan konsentrasi PM2.5 dan PM10 di Jakarta berkorelasi positif dengan suhu dan radiasi, serta berkorelasi negatif dengan kelembaban, curah hujan dan kecepatan angin. Secara bulanan konsentrasi PM di bulan kering lebih tinggi daripada di bulan basah., Jakarta as the capital city of Indonesia is one of the cities with high population, transportation, and industrial activity. This has led to higher air pollution in Jakarta. There are various factors that influence air pollution, one of which is meteorological factors. Meteorological factors will affect the fluctuation of pollutant concentrations in the air, such as air temperature, humidity, rainfall, radiation, wind speed and wind direction. This study aims to see the fluctuation of PM2.5 and PM10 concentrations in Jakarta and the interaction between meteorological factors and PM concentrations. The interaction between meteorological factors and PM concentrations was shown through Pearson correlation and using the openair package in RStudio. The results showed that the concentrations of PM2.5 and PM10 in Jakarta had a peak during the day, decreases towards the afternoon and increases towards the evening. The interaction between meteorological factors and concentrations of PM2.5 and PM10 in Jakarta has a positive correlation with temperature and radiation, and negatively correlates with humidity, rainfall and wind speed. On a monthly basis, PM concentration will increase in dry months and decrease in wet months.
Keyword: concentration, correlation, meteorology, PM2.5, PM10

Judul: Pengaruh Faktor Meteorologi terhadap Fluktuasi Konsentrasi PM10 dan O3 di DKI Jakarta
Abstrak: Pencemaran udara adalah masalah bagi suatu wilayah perkotaan, salah satunya DKI Jakarta. Faktor meteorologi seperti radiasi matahari, suhu udara, kelembaban udara, kecepatan dan arah angin, serta curah hujan mempengaruhi fluktuasi konsentrasi polutan di suatu wilayah. Penelitian ini dilakukan untuk mengetahui besarnya pengaruh faktor meteorologi pada konsentrasi PM10 dan O3. Hasil penelitian menunjukkan adanya korelasi negatif antara radiasi matahari, suhu udara, dan PM10 karena kenaikan radiasi matahari maksimum pada pukul 11.00 WIB akan menurunkan konsentrasi PM10. Pada malam hari, PM10 akan stabil berada di permukaan. Ozon mengalami kenaikan konsentrasi seiring dengan penambahan intensitas radiasi matahari dan suhu udara serta penurunan kelembaban udara. Curah hujan bulanan mempengaruhi penurunan konsentrasi PM10 tetapi tidak banyak mempengaruhi konsentrasi ozon. Kecepatan angin berkorelasi negatif dengan PM10 namun berkorelasi positif dengan ozon.
Keyword: angin, meteorologi, pencemaran udara, perkotaan, polutan

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