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: Analysis of Hotspots, Precipitation, and Sea Surface Temperature Anomalies in East Tanjung Jabung Regency, Jambi
Abstrak: Kebakaran hutan dan lahan merupakan permasalahan serius yang terjadi setiap tahun di Indonesia. Penelitian ini bertujuan mengidentifikasi keterkaitan antara titik panas (hotspot), curah hujan, dan anomali Sea Surface Temperature (SST) sebagai indikator potensi terjadinya kebakaran gambut di Kabupaten Tanjung Jabung Timur. Penelitian ini dilaksanakan pada bulan September-Desember 2023. Data yang digunakan adalah data sekunder. Uji korelasi dilakukan antara data hotspot, curah hujan bulanan, dan SSTA Nino 3.4 pada tahun 2015-2022 untuk mengetahui adanya hubungan antara curah hujan dengan jumlah hotspot yang terdeteksi dan faktor yang memengaruhinya. Hasil dari penelitian ini adalah hubungan hotspot dengan curah hujan berkorelasi negatif dengan nilai r sebesar -0.401 yang termasuk dalam kategori cukup, dan hubungan hotspot dengan anomali SST berkorelasi positif. Hal ini menunjukkan bahwa jumlah hotspot di Kabupaten Tanjung Jabung Timur pada tahun 2015-2022 berkaitan erat dengan curah hujan, namun tidak terlalu erat dengan SST anomalies., Forest and land fires are a serious problem that occurs every year in Indonesia. This research aims to identify the relationship between hotspots, precipitation and Sea Surface Temperature Anomalies (SSTA) as indicators of the potential for peatland fires in East Tanjung Jabung Regency. This research was conducted in September-December 2023. The data used is secondary data. Correlation tests were carried out between hotspot data, monthly precipitation, and SSTA Nino 3.4 in 2015-2022 to determine the relationship between precipitation, the number of hotspots detected, and the factors that influence them. The results of this research are that the relationship between hotspots and precipitation is negatively correlated with an r value of -0.400 which is included in the sufficient category, and the relationship between hotspots and anomaly SST is positively correlated. These findings showed that the number of hotspots in East Tanjung Jabung Regency is closely related to precipitation but not too closely with SST anomalies.
Keyword: curah hujan, titik panas, kebakaran gambut, nino 3.4

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