Judul: Status Kualitas Air Hulu Sungai Cihideung Menggunakan Makrozoobentos Sebagai Bioindikator
Abstrak: Aktivitas manusia dapat mempengaruhi kualitas perairan yang berpotensi menimbulkan pencemaran. Penelitian ini bertujuan untuk menentukan status kualitas air ruas hulu Sungai Cihideung berdasarkan makrozoobentos sebagai bioindikator. Penelitian ini dilakukan pada bulan April hingga Mei 2021 di hulu Sungai Cihideung, Kabupaten Bogor, Jawa Barat. Stasiun pengambilan contoh terdiri dari empat stasiun dengan masing-masing stasiun terdiri dari empat sub stasiun. Data yang digunakan dalam penelitian ini adalah kepadatan makrozoobentos, parameter fisika serta parameter kimia. Metode untuk penilaian status kualitas perairan menggunakan FBI (Family Biotic Index) dan SingScore. Hasil parameter fisika-kimia Sungai Cihideung masih sesuai dengan baku mutu PP No. 22 Tahun 2021. Makrozoobentos yang ditemukan terdiri dari 35 famili dan 49 genus. Komposisi seluruh stasiun didominasi oleh Ephemeroptera dari famili Baetidae. Indeks keanekaragaman yang didapat dari Stasiun 1 hingga Stasiun 4 adalah 3,01, 2,20, 1,54, dan 2,14 dengan kategori rendah. Indeks keseragaman dari Stasiun 1 hingga Stasiun 4 berturut turut adalah 0,71, 0,49, 0,34, dan 0,45 dengan kategori rendah hingga tinggi serta tidak ada jenis yang mendominasi (C<0,5). Hasil FBI menunjukkan kualitas perairan Sungai Cihideung tergolong sangat baik dengan rentang nilai 1,99-3,89. Hasil SingScore menunjukkan kualitas perairan rendah hingga sedang dengan rentang nilai 57,27-80,83., Human activities can affect water quality that has potential to cause pollution. This study aims to determine the water quality status of upstream section of Cihideung River based on macrozoobenthos as a bioindicator. This research was conducted from April to May 2021 in the upper reaches of Cihideung River, Bogor Regency, West Java. The sampling station consisted of four stations with each station consisting of four sub-stations. Data needed in this study are density, physical parameters and chemical parameters. Methods for assessing the status of water quality using FBI and SingScore. Results of physico-chemical parameters of Cihideung River still in accordance with PP No. 22/2021. The macrozoobenthos found consisted of 35 families and 49 genera. Composition of all stations is dominated by Ephemeroptera from Baetidae family. Diversity index obtained from Station 1-Station 4 is 3.01, 2.20, 1.54, and 2.14 with low categories. The uniformity index from Station 1-Station 4 was 0.71, 0.49, 0.34, and 0.45, respectively, with low-high categories and no dominant species (C<0.5). The results of FBI show that water quality of Cihideung River is classified as very good with value range of 1.99-3.89. SingScore results show low to moderate water quality with a value range of 57.27-80.83.
Keyword: Bioindicator, Cihideung River, Family Biotic Index, Macrozoobenthos, SingScore

Judul: Determination of Water Quality Status Based on Macrozoobenthos Community in Cihideung River, Bogor, West Java
Abstrak: Aktivitas manusia di sekitar sungai menghasilkan limbah yang dapat mempengaruhi kondisi kualitas air sungai. Penilaian kualitas air sungai dapat dilakukan menggunakan pendekatan biologi, salah satunya adalah makrozoobentos. Penelitian ini bertujuan untuk menentukan status kualitas air Sungai Cihideung berdasarkan struktur komunitas makrozoobentos menggunakan Lincoln Quality Index (LQI) dan Stream Invertebrate Grade Number Average Level 2 (SIGNAL 2). Penelitian dilakukan di Sungai Cihideung, Kabupaten Bogor, Jawa Barat yang terdiri dari empat stasiun pengamatan. Stasiun 1 hingga Stasiun 4 berada di kawasan tengah hingga hilir sungai. Struktur komunitas makrozoobentos dianalisis menggunakan indeks diversitas, yaitu indeks keanekaragaman, keseragaman, dan dominansi. Penentuan kualitas perairan dilakukan menggunakan indeks biologi, yaitu LQI dan SIGNAL 2. Makrozoobentos yang ditemukan di Sungai Cihideung adalah 51 genus, 38 famili, dan 18 ordo yang didominasi oleh Ordo Ephemeroptera dan Trichoptera. Indeks keanekaragaman dan dominansi makrozoobentos di Sungai Cihideung termasuk ke dalam kategori rendah. Berdasarkan indeks LQI dan SIGNAL 2, kualitas air di Sungai Cihideung tergolong ke dalam kategori baik., Human activities around rivers produce waste that can affect river water quality. Assessment of river water quality can be done using a biological indices based on macrozoobenthos. This study aimed to determine the water quality status of the Cihideung River based on the macrozoobenthos community structure using the Lincoln Quality Index (LQI) and Stream Invertebrate Grade Number Average Level 2 (SIGNAL 2). The study was conducted in Cihideung River, Bogor Regency, West Java with four observation sites located in the middle to downstream areas of the river. The macrozoobenthos community structure was analyzed using Shannon diversity, Evenness, and Dominance indices. Water quality was determined by biological indices, namely LQI and SIGNAL 2. The macrozoobenthos found in the Cihideung River were 51 genera, 38 families, and 18 orders, dominated by Ephemeroptera and Trichoptera. The index of diversity and dominance of macrozoobenthos in the Cihideung River is in the low. Based on the LQI and SIGNAL 2 indices, the water quality in the Cihideung River is in the excellent category
Keyword: Cihideung, Macrozoobenthos, LQI, SIGNAL 2, Water Quality

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