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: Hotspot and Rainfall Analysis Period in Mappi District, South Papua Province
Abstrak: Kabupaten Mappi merupakan salah satu wilayah di Provinsi Papua Selatan yang memiliki jumlah hotspot tertinggi setelah Merauke dalam satu dekade terakhir. Penelitian bertujuan menganalisis pola sebaran hotspot dan keterkaitannya dengan curah hujan pada periode 2012-2021 di Kabupaten Mappi, Provinsi Papua Selatan. Sumber yang digunakan dalam penelitian adalah data hotspot dari citra satelit MODIS Terra/Aqua dan data curah hujan dari NASA LaRC Power yang dianalisis menggunakan analisis deskriptif dan analisis regresi. Hasil penelitian menunjukkan adanya fluktuatif jumlah hotspot pada rentang tahun 2012-2021 dan puncak hotspot terjadi pada tahun 2015. Jumlah hotspot pada rentang tahun tersebut adalah 3879 titik. Sebanyak 1415 titik (36,48%) tersebar pada lahan gambut dan 2464 titik (63,52%) pada lahan mineral. Hasil analisis regresi menunjukkan bahwa hubungan antara hotspot dengan curah hujan memiliki nilai p-value 0,006, artinya p-value < 0,05 menunjukkan korelasi keduanya berpengaruh secara signifikan., Mappi Regency is one of the areas in South Papua Province which has the highest number of hotspots after Merauke in the last decade. The research aims to analyze the pattern of hotspot distribution and its relationship to rainfall in the 2012-2021 period in Mappi District, South Papua Province. The sources used in this study were hotspot data from the MODIS Terra/Aqua satellite imagery and rainfall data from NASA LaRC Power which were analyzed using descriptive analysis and regression analysis. The results showed that there was a fluctuation in the number of hotspots in the 2012-2021 range and the peak of hotspots occurred in 2015. The number of hotspots in that year range was 3879 points. A total of 1415 points (36,48%) are spread on peat land and 2464 points (63,52%) on mineral land. The results of the regression analysis show that the relationship between hotspots and rainfall has a p-value of 0,006, meaning that the p-value is < 0,05 which indicates that the correlation between the two has a significant effect.
Keyword: forest and land fires, hotspot, Mappi District, rainfall

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