Judul: Dinamika populasi wereng pucuk teh, empoasca flavescens (F) (Homoptera : Cicadellidae), dan kajian pemangsaan salticidae
Abstrak: Wereng pucuk teh (WPT), Empoasca flavescens (F.) (Homoptera : Cicadellidae), merupakan hama baru di perkebunan teh di Indonesia. Belum banyak diketahui tentang kehidupan dan dinamika populasi hama ini. Penelitian dilakukan di perkebunan teh rakyat, Desa Cirumput, Kecamatan Cugenang, Kabupaten Cianjur, Jawa Barat, mulai dari bulan April hingga September 2004, dengan tujuan untuk memahami perkembangan dan pola tebaran populasi WPT serta preferensi dan kemampuan pemangsaan laba-laba Salticidae.
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Judul: Hama Perusak Pucuk Teh dan Musuh Alaminya pada Perkebunan Teh Rakyat Desa Cirumput, Cugenang, Cianjur dan Perkebunan Teh PTPN VIII Gunung Mas, Bogor.
Abstrak: Teh (Camellia sinensis) di Indonesia telah banyak dimanfaatkan menjadi bahan baku industri minuman dan menjadi penyumbang devisa penting bagi negara melalui ekspor ke luar negeri serta menyediakan lapangan kerja bagi rakyat Indonesia. Perkebunan teh di Indonesia terbagi menjadi tiga berdasarkan status kepemilikannya yaitu perkebunan milik rakyat, perkebunan milik pemerintah (perusahaan negara), dan perkebunan milik perusahaan swasta. Kegiatan budi daya tanaman teh tidak selalu berjalan dengan baik karena berdampingan dengan permasalahan yang disebabkan oleh hama. Penelitian ini bertujuan untuk mengetahui jenis, tingkat serangan dan kelimpahan hama serta musuh alaminya pada perkebunan teh rakyat dan perusahaan negara. Pengamatan dilakukan dari bulan Maret hingga Juni 2018. Lokasi penelitian untuk perkebunan teh milik rakyat terletak di Desa Cirumput dan untuk perkebunan teh negara terletak di perkebunan teh PTPN VIII Gunung Mas blok 9 dan blok 11. Di setiap lokasi dipilih 3 petak yang masing-masing berukuran 300 m2. Pengamatan terhadap tingkat serangan, kelimpahan hama, dan musuh alami dilakukan secara langsung pada 5 titik contoh yang setiap titik berukuran 4 m2. Koleksi musuh alami dilakukan dengan metode sweep net. Pengamatan jenis dan tingkat parasitisasi dilakukan terhadap larva Lepidoptera yang berasal dari lapangan dan dipelihara di laboratorium. Hasil pengamatan menunjukkan bahwa Homona coffearia (Lepidoptera: Tortricidae), Hyposidra talaca (Lepidoptera: Geometridae), Caloptilia theivora (Lepidoptera: Gracillaria), Helopeltis antonii (Hemitera: Miridae), Empoasca sp. (Hemiptera: Ciccadellidae), dan Cydia leucostoma (Lepidoptera: Tortricidae) menjadi hama perusak pucuk teh pada ketiga lokasi. Predator terbanyak yang ditemukan di Desa Cirumput adalah Libellulidae (23.82%) sedangkan di Gunung Mas blok 9 dan blok 11 berturut-turut adalah Syrphidae (19.31%) dan Formicidae (23.70%). Parasitoid terbanyak yang ditemukan pada ketiga lokasi adalah Braconidae dengan proporsi berturut-turut (39.67%), (43.04%), dan (37.58%). Sympiesis sp. (Hymenoptera: Eulophidae), Sub Famili Microgastrinae (Hymenoptera: Braconidae) ditemukan menjadi parasitoid larva C. theivora dan H. coffearia. Macrocentrus sp. (Hymenoptera: Braconidae) ditemukan hanya menjadi parasitoid H. coffearia.
Keyword: hama perusak pucuk teh, predator, parasitoid, perkebunan teh, teh

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