Judul: Utilization of The Macroalgae Padina australis as a Heterogeneous Catalyst in Making Biodiesel from used Cooking Oil
Abstrak: Energi migas di Indonesia menjadi andalan utama untuk perekonomian, baik sebagai penghasil devisa ataupun pemasok kebutuhan energi dalam negeri. Peningkatan yang terus menerus terjadi mengharuskan Indonesia segera menemukan cadangan migas baru. Oleh karena itu, dilakukan penggunaan bahan bakar alternatif yang ramah lingkungan seperti biodiesel. Pembuatan biodiesel dilakukan dengan memanfaatkan senyawa bioaktif yang terkandung di dalam makroalga Padina australis yang dapat diolah menjadi katalis heterogen. Katalis heterogen dari Padina australis dibuat melalui proses kalsinasi dengan suhu kalsinasi 500 ?, 600 ?, dan 700 ? selama 4 jam. Penggunaan daya microwave 100 dan 400 watt untuk memanaskan minyak serta variasi waktu 5, 10, 15, 20, dan 25 menit dalam pembuatan biodiesel. Hasil analisis katalis Padina australis seperti Scanning Electron Microscope (SEM), X-ray Diffraction (XRD), Fourier Tranzform Infrared Spectroscopy (FTIR), dan Brunauer-Emmett-Teller (BET) serta Kromatografi Gas (GC) untuk mengetahui kandungan metil ester dalam biodiesel. Hasil terbaik yang diperoleh dari pembuatan biodiesel menggunakan katalis Padina australis adalah dengan variasi suhu kalsinasi 700 oC dengan daya 100 watt, dan waktu 10 menit. Penelitian ini menggunakan rancangan acak lengkap dua faktor dan dianalisis menggunakan ANOVA (a = 0,05) dan dilakukan uji lanjut Duncan. Penelitian ini diperoleh hasil biodiesel dengan nilai rendemen sebesar 70-85%, nilai densitas sebesar 0,863-0,888 g/ml, nilai angka asam sebesar 4,85-7,80 mg KOH/g, nilai kadar metil ester sebesar 10,99-48,91% pada penggunaan daya microwave 100 watt dan nilai rendemen sebesar 71-89%, nilai densitas sebesar 0,858-0,893 g/ml, nilai angka asam sebesar 4,67-6,48 mg KOH/g, nilai kadar metil ester sebesar 7,53-62,54% pada penggunaan daya microwave 400 watt.
Keyword: biodiesel, katalis heterogen, Padina australis, kalsinasi

Judul: Pemanfaatan Kulit Buah Kola Sebagai Katalis Heterogen Untuk Produksi Biodiesel Dari Minyak Goreng Curah
Abstrak: Energi merupakan kebutuhan yang tidak dapat dipisahkan dari kehidupan manusia. Sumber energi yang sering digunakan seperti minyak bumi, batu bara, dan gas memiliki cadangan yang terus berkurang berbanding terbalik dengan penggunaanya. Salah satu sumber energi terbarukan adalah biodiesel. Dalam prosesnya diperlukan katalis untuk mempercepat reaksi pembentukan biodiesel. Jenis katalis yang dapat digunakan adalah katalis heterogen. Katalis ini dapat berasal dari limbah pertanian seperti kulit buah kola. Katalis yang berasal dari kulit buah kola dibuat melalui proses kalsinasi pada berbagai suhu yaitu 550 °C, 625 °C, dan 700 °C selama 4 jam. Konsentrasi katalis yang digunakan pada pembuatan biodiesel yaitu 2%, 3%, dan 4%. Proses pembuatan biodiesel menggunakan suhu reaksi sebesar 60 °C dengan waktu reaksi transesterifikasi selama 2 jam. Hasil dari analisis katalis seperti Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), Atomic Absorption Spectroscopy (AAS), dan penggunaan ulang katalis. Biodiesel yang telah diproduksi juga dianalisis seperti rendemen, densitas, angka asam, bilangan penyabunan, gliserol total, dan kadar metil ester. Hasil terbaik diperoleh pada suhu kalsinasi terbaik yaitu 550 °C dengan konsentrasi katalis sebesar 3%. Rancangan percobaan yang digunakan pada penelitian ini yaitu rancangan acak lengkap faktorial dua faktor dan dianalisis dengan ANOVA (α = 0,05) serta uji lanjut Duncan. Penelitian ini dapat disimpulkan bahwa suhu kalsinasi dan konsentrasi biodiesel mempengaruhi hasil biodiesel., Energy is a necessity that cannot be separated from human life. Energy sources, such as petroleum, coal, and gas, have reserves that continuously decrease inversely with their usage. One of the renewable energy sources is biodiesel. In its production process, a catalyst is needed to speed up the biodiesel formation reaction. The type of catalyst that can be used is a heterogeneous catalyst. This catalyst can be derived from agricultural waste, such as kola nut pod husk. Catalysts derived from kola nut pod husk are produced through calcination processes at various temperatures, namely 550 °C, 625 °C, and 700 °C for 4 hours. The catalyst concentration in biodiesel production is 2%, 3%, and 4%. The biodiesel production process uses a reaction temperature of 60 °C with a transesterification reaction time of 2 hours. The results of catalyst analysis include Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), Atomic Absorption Spectroscopy (AAS), and catalyst reusability. The biodiesel is also analyzed for yield, density, acid number, saponification number, total glycerol, and methyl ester content. The best results are obtained at the optimum calcination temperature of 550 °C with a catalyst concentration of 3%. The experimental design used in this research is a two-factor full factorial design, and it is analyzed using ANOVA (α = 0.05) and the Duncan post hoc test. This study can be concluded that calcination temperature and biodiesel concentration affect biodiesel yield.
Keyword: biodiesel, calcination, kola pod husk, concentration, heterogeneous catalyst

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