Judul: Klasifikasi Khasiat Jamu dan Identifikasi Metabolit yang Potensial
Abstrak: Jamu adalah obat tradisional dari Indonesia yang bersifat herbal dan berasal dari tanaman-tanaman obat yang berkhasiat. Analisis tentang formula jamu dapat diperluas ke tingkat molekuler dengan menambahkan informasi senyawa yang terdapat pada tanaman obat sebagai komposisi formula jamu dan menggunakan model ini untuk memprediksi khasiat jamu berdasarkan senyawa aktifnya. Penelitian ini bertujuan menerapkan metode Random Forest untuk memprediksi khasiat jamu dan mengidentifikasi metabolit yang potensial. Data yang digunakan dalam penelitian ini diperoleh dari penelitian Wijaya et al. (2017) dan basis data KNApSAcK. Single Filtering dan Regularized Random Forest digunakan sebagai pra-proses data untuk reduksi data dan fitur. Akurasi tertinggi dalam memprediksi khasiat jamu dengan Random Forest diperoleh saat menggunakan data hasil praproses yaitu sebesar 95.59%. Jumlah metabolit yang berhasil diidentifikasi sebanyak 180 dari 11 kelas khasiat.
Keyword: Jamu, Klasifikasi, Metabolit, Random Forest

Judul: Klasifikasi Khasiat Jamu dengan Metode Deep Belief Netwroks.
Abstrak: Ramuan jamu telah diracik dengan keahlian yang bersifat warisan dari generasi ke generasi. Bagi masyarakat Indonesia, jamu telah terbukti secara empiris mempunyai khasiat yang positif bagi kesehatan. Pembuktian secara ilmiah untuk khasiat jamu masih dalam tahap penelitian. Penelitian tentang khasiat jamu yang telah dilakukan adalah klasifikasi khasiat formula jamu. Metode klasifikasi yang telah digunakan adalah PLSDA, SVM dan ensemble method. Akurasi dengan terbaik adalah 95.34% dari metode SVM. Penelitian yang dilakukan ini menggunakan metode klasifikasi deep belief networks untuk mengklasifikasikan khasiat formula jamu. Akurasi yang dihasilkan adalah 19.54% dengan beberapa kali perulangan kombinasi parameter.
Keyword: deep belief networks, jamu, khasiat, klasifikasi, prediksi

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