Judul: Floristic Composition and Vegetation Structure of Ex-burnt Mountain Forest, in Mount Ciremai National Park, West Java
Abstrak: Kebakaran di areal Taman Nasional Gunung Ciremai terjadi pada kurun waktu tahun 2011-2022. Kebakaran mengakibatkan suksesi sekunder yang mengubah komposisi dan struktur tegakan. Tujuan penelitian ini menganalisis komposisi dan struktur tegakan di hutan pegunungan pascakebakaran. Pengambilan data dengan analisis vegetasi pada dua tipe tutupan lahan, yaitu hutan alam utuh dan pascakebakaran 2019. Penelitian menunjukkan perbedaan jumlah jenis pohon yang ditemukan pada hutan pascakebakaran. Pada tumbuhan bawah didominasi jenis kumis kucing (Orthosiphon aristatus), pada tingkat semai dan pancang didominasi jenis kina (Cinchona calisaya), pada tingkat tiang didominasi jenis nangsi (Oreocnide rubescens), dan tingkat pohon didominasi jenis huru ki leueur (Macaranga tanarius). Keanekaragaman hayati hutan alam utuh relatif lebih tinggi dan komposisi tingkat pohon hutan alam utuh dengan hutan pascakebakaran 2019 mendekati serupa. Struktur horizontal tegakan membentuk kurva J terbalik dan struktur vertikal didominasi pohon lapisan tajuk C.
Keyword: hutan pegunungan, kebakaran, komposisi jenis, struktur tegakan, fire, mountain forest, floristic composition, vegetation structure

Judul: Health Condition of Ex-burnt Forest Area in Gunung Ciremai National Park, Kuningan, West Java
Abstrak: Kebakaran hutan dan lahan akan menimbulkan dampak buruk bagi ekosistem hutan. Oleh sebab itu, perlu dilakukan upaya pengendalian kebakaran hutan guna meminimalisir dampak buruk dari kebakaran hutan yang terjadi, yang salah satu diantaranya berupa penanganan pasca kebakaran dengan memantau areal pasca kebakaran dan melakukan analisis vegetasi bekas terbakar. Tujuan dari penelitian ini adalah menganalisis faktor penyebab kebakaran di Taman Nasional Gunung Ciremai (TNGC), menganalisis kondisi kesehatan areal pasca kebakaran di TNGC dengan frekuensi terbakar yang berbeda, serta menganalisis kondisi kesehatan areal terbakar dan areal tidak terbakar di TNGC. Penilaian kondisi kesehatan hutan di TNGC pada penelitian ini dilakukan dengan metode Forest Health Monitoring (FHM) menggunakan empat indikator, yaitu produktivitas, biodiversitas, vitalitas (kondisi tajuk dan kondisi kerusakan pohon), dan kualitas tapak. Penilaian dilakukan pada dua lokasi dengan karakteristik yang berbeda, yaitu di Blok Pajaten dan Blok Gibug. Penentuan lokasi pembangunan klaster plot dilakukan secara purposive sampling dengan beberapa kriteria antara lain memiliki frekuensi terbakar yang berbeda (0, 1, 3, dan lebih dari 5 kali terbakar) serta memiliki jenis vegetasi penutup lahan yang sama. Jumlah klaster plot yang dibangun yaitu empat klaster plot (16 plot) pada areal terbakar dan satu klaster plot (empat plot) di areal tidak terbakar. Hasil penelitian ini menunjukkan bahwa terdapat dua faktor yang menyebabkan kebakaran di TNGC, yaitu faktor alam dan faktor manusia. Faktor alam yang menjadi penyebab kebakaran di TNGC adalah letusan gunung berapi sedangkan faktor manusia yang menyebabkan kebakaran di TNGC yaitu pembersihan lahan dengan cara membakar dan pemanenan madu hutan. Kondisi kesehatan areal lebih dari 5 kali terbakar di Blok Pajaten lebih rendah dibandingkan dengan kondisi kesehatan areal 1 kali terbakar, kecuali pada indikator vitalitas (kondisi kerusakan pohon) dan kualitas tapak yang menunjukkan bahwa areal lebih dari 5 kali terbakar memiliki kondisi yang lebih baik dibandingkan dengan areal 1 kali terbakar. Areal tidak terbakar di Blok Gibug menunjukkan kondisi kesehatan yang lebih baik dibandingkan areal bekas terbakar, kecuali pada indikator kualitas tapak yang menunjukkan bahwa kondisi tanah di areal terbakar lebih netral (pH lebih tinggi) dengan Kapasitas Tukar Kation (KTK) yang tergolong sedang., Forest and land fires will have a negative impact on forest ecosystems. Therefore, it is necessary to control forest fires to minimize the adverse effects of that occurred forest fires, one of which is in the form of post-fire handling by monitoring the post-fire area and vegetation analysis after burning. This study aims to analyze factors that cause fires in Gunung Ciremai National Park (TNGC), analyze the health condition of the post-fire areas in TNGC with different burning frequencies, and analyze the health conditions of burned and unburned areas in TNGC. Assessment of forest health conditions in TNGC in the research was carried out using the Forest Health Monitoring (FHM) method by using four indicators, namely productivity, biodiversity, vitality (canopy conditions and tree damage conditions), and site quality. The assessment was carried out at two locations with different characteristics, namely the Pajaten Block and the Gibug Block. Determination of the location of the cluster plot establishment was carried out by purposive sampling with several criteria, including having different burning frequencies (0, 1, 3, and
Keyword: Forest Fire, Forest Health Monitoring, Gunung Ciremai National Park, Forest Health Indicators, Pemantauan Kesehatan Hutan, Taman Nasional Gunung Ciremai, Kebakaran Hutan, Area bekas terbakar, Burnt areas

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