Judul: Fluctuation of Concentration PM2.5, PM2.5-10 and PM2.5/PM10 Ratio and its Relationship with Rainfall Intensity (Case Study: Baranangsiang and Cibeureum)
Abstrak: Fluktuasi konsentrasi Particulate Matter (PM) dapat berdampak pada kualitas udara dan kesehatan manusia. Partikel PM sangat kecil sehingga dapat menyebabkan gangguan pernapasan, penyakit jantung, dan efek negatif lainnya. Penelitian ini bertujuan untuk menganalisis hubungan fluktuasi konsentrasi PM2.5, PM2.5-10, dan rasio PM2.5/PM10 dengan intensitas curah hujan, serta kelembaban sebagai faktor pendukung. Konsentrasi PM2.5 , PM2.5-10 dan rasio PM2.5/PM10 tertinggi terjadi selama musim kemarau. Intensitas curah hujan harian dominan memengaruhi penurunan konsentrasi polutan pada penelitian ini adalah hari dengan hujan lebat (Heavy rain). Intensitas curah hujan dapat mempengaruhi PM2.5-10 secara langsung namun tidak langsung terhadap PM2.5. Kondisi dengan curah hujan rendah tetapi kelembaban tinggi dapat mempengaruhi peningkatan konsentrasi partikulat. Rasio PM2.5/PM10 wilayah Baranangsiang memiliki nilai yang sama pada musim hujan dan kemarau saat hari hujan dan tanpa hujan, sebesar 0,4. Rasio PM2.5/PM10 wilayah Cibeureum pada musim hujan saat hari tanpa hujan sebesar 0,4 dan saat hari hujan sebesar 0,3, sedangkan pada musim kemarau saat hari tanpa hujan dan hari hujan sebesar 0,5., Fluctuations in Particulate Matter (PM) concentrations can impact air quality and human health. PM particles are so small that they can cause respiratory problems, heart disease, and other negative effects. This study aims to analyze the relationship of fluctuations in PM2.5, PM2.5-10 and PM2.5/PM10 concentrations with rainfall intensity and humidity as a supporting factor. The highest concentrations of PM2.5 , PM2.5-10 and the ratio of PM2.5/PM10 occurred during the dry season. The dominant daily rainfall intensity affecting the decrease in pollutant concentrations in this study is days with heavy rain. Rainfall intensity can affect PM2.5-10 directly but indirectly on PM2.5. Conditions with low rainfall but high humidity may result in increased particulate concentrations. The PM2.5/PM10 ratio of the Baranangsiang area has the same value in the wet and dry seasons on rainy and non-rainy days, at 0.4. The PM2.5/PM10 ratio of the Cibeureum area in the rainy season during non-rainy days is 0.4 and during rainy days is 0.3, while in the dry season during non-rainy days and rainy days is 0.5.
Keyword: Particulate, PM2.5/PM10 Ratio, Rainfall Intensity, Sub-Urban, Rural

Judul: The Effect of Turbulence Intensity on Fluctuation of SO2, CO, PM2.5, PM10 Pollutant Concentrations (Case Study: Baranangsiang Bogor)
Abstrak: Turbulensi merupakan salah satu faktor meteorologi yang dapat membantu penyebaran polutan di atmosfer. Pengaruh turbulensi terhadap penyebaran polutan dapat diketahui dengan menghitung intensitas turbulensi. Penelitian ini bertujuan untuk menganalisis fluktuasi konsentrasi polutan SO2, CO, PM2.5 dan PM10 serta menganalisis pengaruh intensitas turbulensi terhadap konsentrasi SO2, CO, PM2.5 dan PM10 khususnya di sekitar Kampus IPB Baranangsiang, Bogor. Data yang digunakan dalam penelitian ini adalah data sekunder yang diperoleh dari CCROM IPB Baranangsiang bulan Januari-November 2017, meliputi data SO2, CO, PM2.5, PM10, dan kecepatan angin horizontal serta data sekunder European Centre for Medium-Range Weather Forecast (ECMWF) bulan Januari-November 2017, meliputi data kecepatan angin horizontal dan vertikal. Metode yang digunakan adalah perhitungan intensitas turbulensi menggunakan data kecepatan angin horizontal. Sebagai pembanding, dilakukan perhitungan intensitas turbulensi menggunakan metode Turbulence Kinetic Energy (TKE) dengan data kecepatan angin horizontal dan vertikal dari ECMWF. Nilai intensitas turbulensi rata-rata periode Januari – November 2017 di sekitar Baranangsiang sebesar 0,0724. Nilai intensitas turbulensi pada siang hari lebih tinggi dibandingkan malam hari. Hal ini sesuai dengan nilai TKE. Hasil analisis konsentrasi polutan menunjukkan konsentrasi SO2 memiliki nilai maksimum pada siang hari sekitar pukul 12.00 WIB, konsentrasi CO memiliki nilai maksimum pada pagi dan sore hari sekitar pukul 07.00 WIB dan 18.00 WIB, konsentrasi PM2.5 memiliki nilai maksimum pada sore hari, serta konsentrasi PM10 memiliki nilai maksimum pada malam hari. Pola mingguan keempat polutan menunjukkan pola yang berkaitan dengan aktivitas kendaraan bermotor di sekitar Baranangsiang setiap hari. Pola bulanan keempat polutan menunjukkan konsentrasi polutan dipengaruhi oleh musim. Hasilnya menunjukkan nilai konsentrasi polutan tinggi pada musim kemarau dan rendah pada musim hujan. Hasil analisis koefisien korelasi hubungan antara intensitas turbulensi dengan konsentrasi polutan per periode waktu tertentu memiliki hubungan dan nilai yang berbeda-beda. Konsentrasi SO2 memiliki hubungan berbanding lurus dengan intensitas turbulensi sekitar pukul 07.00 – 00.00 WIB dan berbanding terbalik pada pukul 01.00 – 06.00 WIB. Konsentrasi CO memiliki hubungan berbanding terbalik pada pukul 01.00 – 18.00 WIB dan berbanding lurus pada pukul 19.00 – 00.00. Hubungan konsentrasi PM2.5 dan PM10 dengan intensitas turbulensi berbanding terbalik pada siang hari, dan berbanding lurus pada malam hari. Nilai intensitas turbulensi di Baranangsiang periode Januari – November 2017 cukup rendah untuk mendispersi polutan, sehingga belum bisa menunjukkan secara sempurna pengaruh intensitas turbulensi terhadap konsentrasi polutan., Turbulence is one of the meteorological factors that can disperse pollutants in the atmosphere. In analyzing the effects of turbulence on pollutant dispersion, it can be identified by calculating turbulence intensity. This study aims to analyze the fluctuation of pollutant concentrations of SO2, CO, PM2.5 and PM10 and to analyze the effect of turbulence intensity on the concentrations of SO2, CO, PM2.5 and PM10, especially around the IPB Baranangsiang Campus, Bogor. The data used in this study are secondary data obtained from CCROM of IPB Baranangsiang in January-November 2017, including SO2, CO, PM2.5, PM10, and horizontal wind speed and secondary data from the European Center for Medium-Range Weather Forecast (ECMWF) January-November 2017, including horizontal and vertical wind speed data. The method used is the calculation of turbulence intensity using horizontal wind speed data. As a comparison, the calculation of turbulence intensity
Keyword: correlation, dispersion, pollutant, TKE, turbulence intensity

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