Judul: Stem Characteristics of Kenaf (Hibiscus cannabinus L.) Hydrothermal Carbonized
Abstrak: Karbonisasi merupakan suatu proses untuk mendapatkan produk arang dengan suhu tertentu umumnya menggunakan suhu rendah dibawah 400 ℃ untuk menghasilkan arang hidro dan suhu tinggi sekitar 600 ℃ untuk menghasilkan arang aktif. Tujuan penelitian ini adalah menganalisis pengaruh suhu dan waktu dari proses karbonisasi hidrotermal terhadap batang kenaf serta menghilangkan hemiselulosa pada batang kenaf dengan proses hidrotermal. Penelitian ini menggunakan proses karbonisasi hidrotermal pada batang kenaf dengan suhu 130 ℃, 150 ℃, 170 ℃ dengan waktu 3 jam, 4 jam, dan 5 jam. Hasil penelitian ini menunjukkan karbon terikat, daya jerap polutan, dan daya jerap iodin arang hidro batang kenaf meningkat seiring kenaikan suhu dan waktu dibandingkan kontrol batang kenaf. Arang hidro batang kenaf terbaik didapat pada suhu 170 ℃ dalam waktu 3 jam dikarenakan total daya jerap iodin yang paling tinggi. Karbonisasi hidrotermal terbukti mampu mengurangi kandungan hemiselulosa., Carbonization is a process to get charcoal products with a certain temperature generally using low temperatures below 400 ℃ to produce hydro charcoal and high temperatures around 600 ℃ to produce activated charcoal. The objective of this study was to analyze the effect of temperature and time from the hydrothermal carbonization process on kenaf stems and to remove hemicellulose from kenaf stems using a hydrothermal process. This study used a hydrothermal carbonization process on kenaf stems with temperatures of 130 ℃, 150 ℃, 170 ℃ with 3 hours, 4 hours, and 5 hours. The results of this study showed that the bound carbon, pollutant adsorption capacity, and iodine adsorption capacity of hydro charcoal kenaf stems increased with increasing temperature and time compared to control kenaf stems. The best kenaf stems hydro charcoal was obtained at a temperature of 170 ℃ within 3 hours due to the highest total iodine adsorption capacity. Hydrothermal carbonization is proven to be able to reduce the hemicellulose content.
Keyword: Arang hidro, batang kenaf, daya jerap, karbonisasi hidrotermal, Adsorption capacity, hydro charcoal, hydrothermal carbonization, kenaf stems

Judul: Characteristics of Pyrolysis Charcoal from Kenaf (Hibiscus cannabinus L.) Stem with Pyrolysis Carbonization
Abstrak: Karbonisasi pirolisis dipilih dalam penelitian ini karena metode ini memiliki kelebihan, yaitu tidak diperlukannya bahan tambah pada saat proses karbonisasi. Karbonisasi pirolisis menghasilkan produk berupa arang pirolisis. Limbah batang kenaf yang cukup banyak dan belum dimanfaatkan dengan optimal, mendasari penggunaan batang kenaf pada penelitian ini. Penelitian ini bertujuan untuk mengetahui pengaruh karbonisasi terhadap karakteristik arang pirolisis batang kenaf, serta mengetahui suhu dan waktu yang terbaik dalam melakukan karbonisasi pirolisis. Pada penelitian ini karbonisasi dilakukan dengan variasi suhu 175 °C, 200 °C, dan 225 °C dengan waktu karbonisasi 3, 4, dan 5 jam. Karakteristik yang diuji meliputi komponen kimia bahan, analisis proksimat arang hasil karbonisasi, daya jerap polutan, dan daya jerap iodin. Hasil pada penelitian ini menunjukkan bahwa, daya jerap polutan dan daya jerap iodin dari arang batang kenaf menjerap dengan baik dibandingkan dengan daya jerap batang kenaf sebelum dijadikan arang. Berdasarkan hasil pengujian daya jerap polutan dan total bilangan iodin, didapatkan arang terbaik pada suhu 225 °C selama suhu 4 jam., Pyrolysis carbonization was chosen in this study because this method has an advantage that it does not require additional materials during the carbonization process. Pyrolysis carbonization produces a product in the form of pyrolysis charcoal. There is a lot of kenaf stems and it has not been used optimally, underlies the use of kenaf stems in this study. This study aims to determine the effect of carbonization on the characteristics of pyrolysis charcoal from kenaf stems, as well as to determine the best temperature and time to carry out pyrolysis carbonization. In this study, carbonization was carried out with variations in temperature of 175 °C, 200 °C, and 225 °C with carbonization times of 3, 4, and 5 hours. The characteristics tested include the chemical components of the material, proximate analysis of carbonized charcoal, pollutant adsorption capacity, and iodine adsorption capacity. The results of this study indicate that the adsorption capacity of the pollutant and the iodine adsorption capacity of the kenaf stem charcoal is better than the adsorption capacity of the kenaf stem before being made into charcoal. Result showed that pollutant adsorption test and the total number of iodine, the best charcoal was obtained at a temperature of 225 °C for 4 hours.
Keyword: Arang pirolisis, batang kenaf, daya jerap iodin, daya jerap polutan, pirolisis, Charcoal pyrolysis, iodine number, kenaf stem, pollutants adsorbtion, pyrolysis

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