Judul: Analysis of Humidity, Air Temperature and Rainfall on Peat Fire Case in Jambi
Abstrak: Kabupaten Tanjung Jabung Timur Jambi memiliki permasalahan serius pada kebakaran lahan gambut. Pengaruh kelembapan, suhu udara dan curah hujan seringkali dikaitkan dengan pengaruh iklim yang memberikan kondisi ideal untuk terjadinya kebakaran. Informasi hotspot yang didapatkan dari penginderaan jauh melalui satelit Terra dan Aqua MODIS dapat digunakan untuk mengetahui jumlah hotspot dan peluang terjadinya kebakaran. Tujuan penelitian ini adalah untuk menganalisis hubungan antara kelembapan, suhu udara dan curah hujan terhadap kejadian kebakaran hutan dan gambut di Kabupaten Tanjung Jabung Timur Jambi pada tahun 2017-2019. Hasil penelitian menunjukkan bahwa analisis korelasi kelembapan, suhu udara, curah hujan terhadap hotspot di Kabupaten Tanjung Jabung Timur dalam kurun waktu 2017-2019 memperlihatkan bahwa ketiganya memberikan sumbangan pengaruh terhadap jumlah hotspot. Kelembapan merupakan variabel dengan nilai korelasi tertinggi, sebesar -0,569. Penurunan kelembapan udara yang diikuti oleh penurunan curah hujan disertai terjadinya peningkatan suhu udara mulai bulan Juni sampai bulan September 2019 menyebabkan terjadinya peningkatan hotspot tertinggi pada bulan September 2019 yang menyebabkan terjadi peningkatan kejadian kebakaran di Tanjung Jabung Timur, Jambi., Tanjung Jabung Timur Jambi has a serious problem with peatlands fires. The effects of humidity, air temperature and rainfall are often associated with climatic influences that provide ideal conditions for fire to occur. Hotspot information obtained from remote sensing through the Terra and Aqua MODIS satellites can be used to determine the number of hotspot and the chance of fire occurrences. The research objective was to analyze the relationship between air humidity, air temperature and rainfall to the forest fires in Tanjung Jabung Timur Regency, Jambi during 2017-2019. The results showed that analysis of correlation between air humidity, air temperature, and rainfall on hotspot occurrences at Tanjung Jabung Timur shows that all three variables contributed to the number of hotspot. Humidity was the variable with highest correlation of -0,569. The decrease of humidity that followed by a decrease in rainfall and an increase in air temperature from June to September 2019 led to the highest hotspot in September 2019, which resulted to an increase of forest fire occurrences in Tanjung Jabung Timur, Jambi.
Keyword: Fire, Humidity, Peatlands, Rainfall, Temperature

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