Judul: Kajian Kenyamanan Termal di Jawa Barat.
Abstrak: Pertumbuhan penduduk yang pesat menyebabkan terjadinya perubahan tingkat kenyamanan termal yang dapat mengganggu aktivitas manusia. Identifikasi sebaran indeks kenyamanan dilakukan pada musim hujan dan kemarau selama tahun 2007 hingga 2016. Indeks kenyamanan di Jawa Barat dianalisis dengan menggunakan Temperature Humidity Index (THI), Wet Bulb Globe Temperature (WBGT), dan Heat Stress Index (HSI). WBGT membutuhkan nilai suhu bola hitam (Tbg) yang diduga dari unsur-unsur cuaca di stasiun Klimatologi Pendidikan Baranangsiang. Penyebaran ketiga indeks di wilayah Jawa Barat memiliki pola yang serupa yaitu wilayah dengan nilai indeks yang tinggi terdapat di wilayah utara dan semakin menurun ke arah tengah dan selatan. Pengamatan di dua sekolah dengan ketinggian < 500 mdpl menunjukkan tingkat tidak nyaman dari segi termal, sedangkan satu sekolah dengan ketinggan > 500 mdpl menunjukkan tingkat kenyamanan termal yang nyaman berdasarkan indeks THI dan HSI, tetapi persepsi para siswa yang tinggal dan beradaptasi dengan lingkungan di sekitar ketiga sekolah menyatakan kondisi termal berada pada tingkat agak nyaman meskipun berada pada ketinggian serta WBGT, THI, dan HSI yang berbeda-beda. Berdasarkan persepsi siswa di ketiga sekolah diperoleh bahwa kondisi nyaman berada pada THI (dalam oC) < 25.5, WBGT (dalam oC) < 28.6, HSI (dalam oF) < 85 dan kondisi tidak nyaman pada THI (dalam oC) > 25.5, WBGT (dalam oC) > 28.6, dan HSI (dalam oF) > 85.
Keyword: indeks kenyamanan, Jawa Barat, Heat Stress Index, Temperature Humidity Indeks, Wet Bulb Globe Temperature,

Judul: Spatial and Temporal Variation of Thermal Comfort in Banten Province
Abstrak: Kenyamanan termal dapat mempengaruhi kinerja seseorang atau masyarakat. Kenyamanan termal di luar ruang dapat dipengaruhi salah satunya oleh kondisi vegetasi di wilayah tersebut. Penelitian ini bertujuan untuk mengidentifikasi variasi spasial dan temporal kenyamanan termal di Provinsi Banten dan hubungannya dengan kondisi vegetasi. Identifikasi kenyamanan termal dilakukan menggunakan data periode tahun 2011-2020. Indeks kenyamanan yang digunakan adalah Heat Stress Index (HSI) dan Effective Temperature (ET). Hasil penelitian ini menunjukkan bahwa kenyamanan termal di Provinsi Banten secara spasial dan temporal memiliki variasi yang berbeda. Di wilayah utara memiliki indeks kenyamanan yang lebih tinggi daripada wilayah selatan. Kenyamanan termal dengan metode HSI memiliki kondisi nyaman serta hati-hati pada musim DJF dan JJA, sedangkan musim MAM dan SON keduanya memiliki kondisi nyaman, hati-hati, serta sangat hati-hati. Kenyamanan termal dengan metode ET pada musim DJF memiliki kondisi nyaman, agak nyaman, serta tidak nyaman, sedangkan pada musim MAM, JJA, dan SON ketiganya memiliki kondisi nyaman, agak nyaman, tidak nyaman, serta sangat tidak nyaman. Pada pagi hari wilayah dengan kondisi nyaman cukup luas, meliputi 92% wilayah menurut HSI dan 80% wilayah menurut ET, sedangkan pada sore hari hanya mencakup 7% wilayah menurut HSI dan 2% wilayah menurut ET. Pada siang hari tidak terdapat wilayah nyaman termal menurut HSI dan ET. Kenyamanan termal dengan metode HSI memiliki nilai indeks yang lebih tinggi di musim hujan pada pagi dan siang hari, sedangkan dengan metode ET lebih tinggi di musim kemarau pada siang dan sore hari. Wilayah dengan indeks vegetasi rendah menyebabkan indeks kenyamanan termal yang lebih tinggi atau lebih tidak nyaman dibandingkan wilayah dengan indeks vegetasi tinggi., Thermal comfort can affect the performance of a person or society. Outdoor thermal comfort can be affected by vegetation conditions in the area. This study aims to identify spatial dan temporal variations of thermal comfort in Banten Province, and its relationship to the greenness of the vegetation. Identification was carried out using data for the period 2011-2020. Comfort index used is Heat Stress Index (HSI) and Effective Temperature (ET). Thermal comfort in Banten Province spatially and temporally have different variations. In the northern region, it has higher comfort index than the southern region. The thermal comfort with HSI method has a condition in comfort and caution in DJF and JJA seasons, therefore in MAM and SON seasons has a comfortable, caution, and extreme caution condition. The thermal comfort with ET method has condition in comfortable, partially comfortable, and uncomfortable in DJF seasons, therefore in MAM, JJA, and SON has a comfortable, partially comfortable, uncomfortable, and very uncomfortable condition. In the morning the comfortable area is quite wide, covering 92% areas with HSI and 80% areas with ET, while in the evening only cover 7% areas with HSI and 2% areas with ET. In the afternoon not having area comfortable according HSI and ET. The value of comfort index with HSI method is higher in wet season in the morning and afternoon, meanwhile with ET method is higher in dry season in the afternoon and evening. Areas with the low index vegetation have a higher thermal comfort index or tend to be uncomfortable than areas
Keyword: Banten Province, Effective Temperature, Heat Stress Index, Index Vegetation, Thermal Comfort Classification

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