Judul: Assessing Rainfall and Sea Surface Temperature Anomalies Influences on the Number of Hotspots in Fakfak Regency, West Papua
Abstrak: Kabupaten Fakfak merupakan salah satu kabupaten di Provinsi Papua Barat yang termasuk kedalam wilayah yang memiliki tingkat kerawanan terjadinya kebakaran hutan dan lahan yang tinggi. Beberapa penelitian sebelumnya menunjukkan keeratan hunungan antara curah hujan, anomali Sea Surface Temperature (SST) dan jumlah titik hotspot. Tujuan penelitian ini adalah menganalisis hubungan antara curah hujan, anomali SST dan titik panas (hotspot) di Kabupaten Fakfak. Penelitian ini dilakukan pada bulan November 2023 - Maret 2024 di Laboratorium Kebakaran Hutan, Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, Institut pertanian Bogor. Data yang digunakan adalah data curah hujan harian, data titik panas MODIS dan VIIRS dan data anomali SST 3.4. Hasil penelitian menunjukkan bahwa curah hujan berbanding terbalik dengan titik panas dengan korelasi -0,186. Anomali SST berbanding terbalik dengan curah hujan dengan nilai korelasi negatif sebesar -0,054. Sedangkan anomali SST berbanding lurus dengan titik panas dengan nilai korelasi positif sebesar 0,288. Anomali SST tidak berpengaruh nyata terhadap curah hujan di Papua Barat sehingga dengan demikian anomali SST tidak terlalu berpengaruh terhadap kejadian kebakaran di Kabupaten Fakfak., Fakfak Regency is one of the regencies in West Papua Province that is prone to forest and land fires. Previous researches indicates a strong relation between precipitaton, Sea Surface Temperature (SST) Anomaly and number of hotspots. The aim of this study is to analyze the relationship between rainfall, Sea Surface Temperature (SST) anomalies, and hotspots in Fakfak Regency. This research was conducted from November 2023 to March 2024 in the Forest Fire Laboratory, Department of Silviculture, Faculty of Forestry and Environment, Bogor Agricultural University. The data used include daily rainfall data, MODIS and VIIRS hotspot data, and SST 3.4 anomaly data. The results show that precipitation is inversely related to hotspots with a correlation of -0.186. SST anomalies are inversely related to precipitation with a negative correlation of -0.054, while SST anomalies are directly related to hotspots with a positive correlation of 0.288. SST anomalies do not significantly affect precipitation in Fakfak Regency, and therefore, the SST anomalies do not influence the occurrences of fires in Fakfak Regency.
Keyword: curah hujan, Anomali SST, titik panas

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