Judul: Keterkaitan Curah Hujan, Anomali Sea Surface Temperature, Southern Oscillation Index dan Hotspot di Kota Waingapu.
Abstrak: Kota Waingapu merupakan salah satu daerah Provinsi Nusa Tenggara Timur yang sering dilanda kebakaran hutan dan lahan. Pencegahan kebakaran hutan dan lahan dapat memanfaatkan data titik panas untuk di analisis dengan Sistem Informasi Geografis (SIG). Tujuan penelitian ini adalah menganalisis hubungan antara curah hujan, anomali Sea Surface Temperature, Southern Oscillation Index dan kejadian kebakaran hutan dan lahan di wilayah sabana Kota Waingapu. Penelitian ini dilakukan pada bulan September 2023 - Januari 2024 di Labolatorium Kebakaran Hutan, Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, Institut pertanian Bogor. Data yang digunakan adalah data titik panas MODIS dan VIIRS, data curah hujan harian, dan data anomali SST 3.4. Hasil penelitian menunjukan bahwa curah hujan berhubungan terbalik dengan titik panas dengan menunjukan nilai korelasi negatif. Anomali SST berbanding terbalik dengan curah hujan dengan nilai korelasi negatif. Anomali SST berbanding lurus dengan titik panas dengan nilai korelasi positif. SOI berbanding terbalik dengan curah hujan dengan nilai korelasi negatif. SOI berbanding terbalik dengan titik panas dengan nilai korelasi negatif.
Keyword: Hotspot, curah hujan, Anomali SST, SOI, Sabana

Judul: Precipitation, Sea Surface Temperature Anomaly (SSTA)
Abstrak: Waingapu merupakan salah satu daerah di Provinsi Nusa Tenggara Timur yang sering dilanda kebakaran hutan sabana. Pencegahan kebakaran hutan dan lahan dapat memanfaatkan data titik panas untuk dianalisis dengan Sistem Informasi Geografis (SIG). Iklim merupakan salah satu faktor alam yang memengaruhi terjadinya kebakaran hutan sabana di Waingapu. Tujuan penelitian ini adalah menganalisis hubungan antara curah hujan, anomali SST, dan kejadian kebakaran hutan sabana di Waingapu. Penelitian ini dilakukan pada bulan Februari April 2021 di Laboratorium Kebakaran Hutan dan Lahan, Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, Institut Pertanian Bogor. Data yang digunakan adalah data titik panas MODIS dan VIIRS, data curah hujan harian dan data anomali SST 3.4. Hasil penelitian menunjukan bahwa curah hujan berhubungan terbalik dengan titik panas dengan nilai korelasi negatif. Anomali SST berhubungan terbalik dengan curah hujan dengan nilai korelasi negatif. Sedangkan anomali SST dengan titik panas berbanding lurus dengan nilai korelasi positif., Waingapu is one of the areas in Nusa Tenggara Timur Province which often suffered from savanna fires. Savanna fires prevention can be done by utilizing hotspot data for analysis using Geographic Information System (GIS). The climate is one of factors influence the occurrence of savanna fires in Waingapu. The purpose of this study was to analyze the relationship between precipitation, SST anomalies, and the occurrences of savanna fires in Waingapu. This research was conducted on February April 2021 at the Forest and Land Fires Laboratory, Department of Silviculture, Faculty of Forestry and Environment, IPB University. The data used are MODIS and VIIRS hotspot data, daily precipitation data and SST 3.4 anomaly data. The results showed that precipitation was inversely related to hotspots with a negative correlation value. SST anomaly is inversely related to precipitation a negative correlation value. While the SST anomaly with hotspots is directly proportional with a positive correlation value.
Keyword: climate, hotspot, Geographic Information System (GIS)

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