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: Fluctuations PM2,5 Concentration  and its Relationship with Wind Characteristics (Case study: Central Jakarta), Fluctuations PM2,5 Concentration  and its Relationship with Wind Characteristics (Case study: Central Jakarta)
Abstrak: Masalah pencemaran udara merupakan salah satu masalah yang dihadapi oleh beberapa kota besar di dunia, salah satunya adalah Kota Jakarta. Peningkatan jumlah penduduk di Jakarta mengakibatkan peningkatan kegiatan konsumsi energi baik dari sektor transportasi, maupun industri, yang dapat mengakibatkan peningkatan emisi pencemar udara, salah satunya adalah partikulat halus PM2,5. Fluktuasi konsentrasi pencemar, selain dipengaruhi sumber emisi, juga dipengaruhi faktor meteorologi, salah satunya adalah angin. Penelitian ini bertujuan untuk menganalisis fluktuasi konsentrasi PM2,5 dan hubunganya dengan karakteristik angin dengan lokasi penelitian Jakarta Pusat. Data konsentrasi PM2,5 tahun 2019 dan 2020 didapatkan dari hasil pengukuran US Embassy (Jakarta Pusat), data angin menggunakan data ECMWF. Fluktuasi konsentrasi PM2.5 dan hubungannya dengan kecepatan angin dianalisis menggunakan statistik deskriptif, uji-t berpasangan sederhana dan package open air menggunakan metode windrose dan polar plot serta analisis regresi dan korelasi. Hasil penelitian menunjukkan konsentrasi PM2.5 memiliki puncak konsentrasi pada pagi hingga siang hari, menurun saat menuju sore hari dan kembali meningkat pada saat menuju malam hari. Fluktuasi PM2,5 bulanan secara keseluruhan menunjukkan peningkatan dari bulan Januari hingga mencapai puncaknya di bulan Juni, lalu mengalami penurunan hingga bulan Desember. Hubungan kecepatan angin dengan konsentrasi PM2,5 berkorelasi negatif -0,30 pada 2019 dan -0,31 pada 2020. Hubungan komponen zonal (Barat-Timur) dengan PM2,5 korelasi negatif dengan r sebesar -0,26 pada 2019 dan -0.31 pada 2020 dibandingkan dengan komponen meridional (Utara–Selatan) berkorelasi positif dengan r yang sebesar 0.21 dan 0.12. Berdasarkan hasil R2 komponen zonal memiliki pengaruh yang lebih besar terhadap konsentrasi PM2,5¬ dengan R2 sebesar 13,90 % (2019) dan 15,03% pada 2020 dibadingkan dengan hasil R2 komponen meridional yang hanya sebesar 8,62% dan 9,52%, The problem of air pollution is one of the problems faced by several big cities in the world, one of which is the city of Jakarta. An increase in the population in Jakarta has resulted in an increase in energy consumption activities from both the transportation and industrial sectors, which can result in an increase in air pollutant emissions, one of which is PM2.5 fine particulate matter. Fluctuations in pollutant concentrations, apart from being influenced by emission sources, are also influenced by meteorological factors, one of which is wind. This study aims to analyze fluctuations in PM2.5 concentrations and its relationship with the characteristics of the wind with the research location in Central Jakarta. PM2.5 concentration data In 2019 and 2020 obtained from the US Embassy (Central Jakarta) measurement results, wind data uses ECMWF data. PM2.5 concentration fluctuations and its relationship with wind speed were analyzed using descriptive statistics, simple paired t-test and open package air using the windrose method and polar plots as well as regression and correlation analysis. The results showed the concentration of PM2.5 has a peak concentration in the morning until noon, decreases towards the afternoon and increases again towards the evening. PM2.5 fluctuation The monthly as a whole showed an increase from January to reach a peak in June, then decreased until December. Relationship between wind speed and PM2.5 concentration negatively correlated -0.30 in 2019 and -0.31 in 2020. Relationship between the zonal (West-East) component and PM2.5 has negative correlation with r of -0.26 in
Keyword: Fluktuasi konsentrasi, Partikulat (PM2,5), Kecepatan Angin, Angin Zonal, Angin Meridional

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