Judul: Identifikasi Pengaruh Hujan dan Kebijakan Pembatasan Kendaraan terhadap Konsentrasi Karbon Monoksida (CO)
Abstrak: Kualitas udara yang memburuk semakin menjadi perhatian masyarakat luas. Emisi gas buang kendaraan menjadi salah satu kontributor utama polusi udara, salah satunya adalah gas Karbon Monoksida (CO). Hal ini diperparah dengan curah hujan yang rendah pada periode tersebut yang membuat proses pencucian udara oleh hujan menjadi rendah. Permasalahan tersebut, memerlukan kebijakan pengendalian kualitas udara, salah satunya adalah kebijakan pembatasan kendaraan untuk mengurangi emisi gas CO. Penelitian bertujuan mengidentifikasi pengaruh hujan dan pembatasan kendaraan terhadap konsentrasi CO dengan menggunakan analisis komparatif, klasterisasi dan asosiasi. Penelitian ini menggunakan data hasil rekaman sensor kualitas udara National Institute for Environmental Studies (NIES) yang di pasang pada Pos Polusi Udara Cibeureum, Badan meteorologi Klimatologi dan Geofisika (BMKG). Data curah hujan harian diperoleh dari hasil penakaran curah hujan Pos Polusi Udara Cibereum dan data Cuaca diperoleh dari hasil pengamatan cuaca sinoptik pada Stasiun Meteorologi Citeko. Rentang data yang digunakan dalam penelitian ini mulai dari 1 Januari 2018 – 31 Desember 2021. Dalam rentang waktu tersebut terdapat periode saat diberlakukan kebijakan pembatasan akibat pandemi COVID19 (31 Maret 2020 – 31 Desember 2021). Periode tersebut merepresentasikan kondisi lalu-lintas kendaraan yang lebih sepi. Hasil yang diperoleh menunjukkan bahwa terdapat hubungan antara kebijakan pembatasan kendaraan dengan konsentrasi CO. Hal ini ditunjukkan dengan adanya perbedaan yang signifikan pada konsentrasi CO sebelum dan saat kebijakan pembatasan kendaraan diterapkan. Perbandingan curah hujan bulanan dengan rata-rata konsentrasi CO bulanan juga menunjukkan korelasi negatif. Hasil pengelompokan menunjukkan bahwa klaster dengan curah hujan yang tinggi memiliki sebaran nilai konsentrasi CO yang berada di antara rendah dan sedang. Klaster dengan hari hujan yang lebih banyak juga umumnya memiliki sebaran nilai konsentrasi CO yang lebih rendah dibandingkan klaster dengan hari hujan yang lebih sedikit. Selain itu, sebagian besar hari pada saat kebijakan pembatasan diterapkan berada pada klaster dengan distribusi nilai konsentrasi CO terendah. Hasil pengelompokan tersebut semakin memperkuat asumsi bahwa hujan dan kebijakan pembatasan memiliki hubungan terbalik dengan konsentrasi CO. Hasil analisis asosiasi juga memvalidasi adanya hubungan hujan maupun kebijakan pembatasan dengan konsentrasi CO. Hasil analisis regresi dummy menunjukkan adanya pengaruh yang signifikan antara hujan dan kebijakan pembatasan kendaraan terhadap konsentrasi CO dengan nilai koefisien determinasi (R2 ) sebesar 24,2%.
Keyword: Polusi Udara, Karbon Monoksida, Pembatasan Kendaraan, Hujan, carbon monoxide, air pollution, lockdown, rainfall

Judul: The Analysis if Rainfall Effect on Carbon Monoxide (CO) and
Nitrogen Oxide (NOx) Concentrations in the Puncak Bogor Area
Abstrak: Karbon monoksida (CO) dan Nitrogen Oksida (NOx) merupakan polutan yang sangat berbahaya bagi kesehatan manusia. Fluktuasi konsentrasi CO dan NOx tidak hanya dipengaruhi oleh aktivitas antropogenik tetapi faktor meteorologi seperti curah hujan. Penelitian ini bertujuan menganalisis pengaruh curah hujan terhadap konsentrasi CO dan NOx, dengan studi kasus Puncak Bogor tahun 2019. Data yang digunakan berupa data sekunder curah hujan dan konsentrasi CO dan NOx diperoleh dari BMKG Citeko bekerjasama dengan NIES Jepang. Analisis yang dilakukan adalah analisis data bulanan, harian, dan per menit. Hasil analisis data bulanan, curah hujan berpengaruh signifikan terhadap penurunan konsentrasi CO (r = -0,725; p-value, Carbon monoxide (CO) and Nitrogen Oxide (NOx) were pollutants that were very dangerous for human health. Fluctuations in CO and NOx concentrations are not only influenced by anthropogenic activities but meteorological factors such as rainfall. This research aims to analyze the effect of rainfall on CO and NOx concentrations, with a case study in Puncak Bogor in 2019. The data used in this study were rainfall, and CO and NOx concentrations secondary data obtained from BMKG Citeko in collaboration with NIES Japan. The data were analyzed in different time interval such as monthly, daily and minute. The results of monthly data analysis showed that rainfall has a significant effect on reducing CO concentrations (r = -0.725; p-value < 0.05), in contrast there was no significant effect on NOx concentrations (r = -0.531; p-value > 0.05). The results of daily data analysis showed that rainfall has an effect on reducing CO and NOx concentrations with a low level of correlation (r CO = -0.16 and r NOx = -0.17). The results of analysis of data per minute in certain periods of the wet month and dry season showed that rainfall has a significant effect on reducing CO and NOx concentrations, especially in the wet season. On the other hand, in the dry season, rainfall had a less significant effect on reducing CO and NOx concentrations. Based on analysis over the same time period, the average concentrations of CO and NOx were smaller when rain occurred than there were no rain occured. This showed that rainfall has an effect on reducing the concentration of CO and NOx pollutants in the Puncak Bogor area.
Keyword: curah hujan, Kualitas Udara, karbon monoksida, nitrogen oksida

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