Judul: Sebaran Spasial Makrozoobentos dan Penggunaannya sebagai Bioindikator di Perairan Pesisir Tangerang, Banten
Abstrak: Pesisir merupakan wilayah yang banyak mengalami perubahan fisik maupun fungsinya akibat kegiatan antropogenik yang mengeluarkan bahan pencemar. Oleh sebab itu, Perlu adanya pendekatan untuk mengetahui tingkat pencemaran wilayah pesisir. Salah satu pendekatannya adalah metode fisika-kimia air dan metode biologi (makrozoobentos) sebagai bioindikator. Tujuan penelitian ini untuk menentukan struktur komunitas makrozoobentos secara spasial dan memprediksi status perairan perairan Pesisir Tangerang, Banten. Pengambilan sampel dilakukan di perairan Pesisir Tangerang, Banten pada Bulan April-Agustus 2013. Sample diambil dari 52 stasiun yang terbagi ke dalam 3 lokasi yaitu Kronjo (15 stasiun), Cituis (15 stasiun) dan Tanjung Pasir (22 stasiun). Sample diidentifikasi dengan menggunakan buku panduan standar identifikasi makrozoobentos dan World Register of Marine Species (WoRMS: http://www.marinespecies.org/index.php). Sample makrozoobentos teridentifikasi dianalisis sebarannya dengan analisis similaritas Bray-curtis dan Canonical Correspondence Analysis (CCA), sedangkan status perairan dianalisis dengan metode fisika-kimia air (indeks STORET) dan metode biologi dengan makrozoobentos sebagai bioindikator dengan AZTI Marine Biotic Index (AMBI). Fauna makrozoobentos ditemukan sebanyak 5458 spesimen, tersebar ke dalam 74 species dan 15 kelas. Makrozoobentos polychaeta (25 spesies) ditemukan dengan jumlah spesies terbanyak yang didomiasi polychaeta deposit feeder (20 spesies), diikuti kelas bivalvia dan kelas lainnya. Kepadatan makrozoobentos tertinggi ditemukan di lokasi Tanjung Pasir (634 ind/m2), diikuti lokasi Kronjo (595 ind/m2) dan terendah ditemukan di lokasi Cituis (177 ind/m2). Kesimpulan penelitian ini adalah berdasarkan analisis sebaran spasial makroozoobentos Bray-Curtis lokasi Kronjo membentuk 4 kelompok, Cituis 3 kelompok dan Tanjung Pasir 5 kelompok, dan CCA menunjukan jenis Ophryotrocha puerilis, Clitellio arenarius dan Xantho pilipes yang bersifat kosmopolitan ditemukan di setiap zonasi pada lokasi berbeda. Adapun analisis status perairan lokasi Kronjo menunjukan nilai AMBI dan STORET terrendah dengan status tercemar sedang diikuti oleh lokasi Tanjung Pasir dengan status buruk dan lokasi Cituis ditemukan tingkat pencemaran tertinggi dengan status sangat buruk atau terganggu berat.
Keyword: macrozobentos, community structure, coastal Tangerang, AMBI

Judul: Struktur Komunitas Makrozoobentos dan Keterkaitannya dengan Kualitas Air di Perairan Teluk Banten
Abstrak: Tingginya aktivitas antropogenik di kawasan pesisir Teluk Banten berpotensi menurunkan kualitas perairan akibat masuknya bahan-bahan organik dan anorganik. Penurunan kualitas air tersebut akan berdampak terhadap kehidupan biota, salah satunya makrozoobentos. Penelitian ini bertujuan menganalisis struktur komunitas makrozoobentos dan keterkaitannya dengan kualitas air di Teluk Banten. Penelitian dilakukan selama enam bulan, sejak bulan September 2022 hingga Februari 2023 dengan enam titik stasiun pengamatan. Metode yang digunakan terdiri dari analisis kualitas air secara insitu dan eksitu berupa sampel makrozoobentos yang diambil menggunakan Ekman Grab, parameter kualitas air, dan substrat. Analisis data yang dilakukan terdiri dari kepadatan, keanekaragaman, keseragaman, dominansi, tipe substrat, analisis similaritas, dan analisis komponen utama. Makrozoobenthos yang diperoleh terdiri dari enam kelas, yaitu Bivalvia, Gastropoda, Polychaeta, Scapopoda, Malacostraca, dan Echinoidea. Kepadatan makrozoobentos tertinggi pada musim kemarau di bulan September. Indeks keanekaragaman yang diperoleh terkategori tinggi dengan keseragaman dan dominansi yang rendah. Kualitas air yang paling berpengaruh bagi makrozoobentos di Teluk Banten, yaitu DO dan suhu., The high anthropogenic activities in the coastal area of Banten Bay have potential to reduce water quality due to the load of organic and inorganic materials. The decrease in water quality will have an impact on biota life, one of which is macrozoobenthos. This study aims to analyze the structure of the macrozoobentos community and its relationship with water quality in Banten Bay. The study was conducted for six months, from September 2022 to February 2023 with six observation stations. The method used consists of in situ and ex situ water quality analysis in the form of macrozoobenthos samples taken usingEkman Grab, water quality parameters, and substrate. The data analysis carried out consisted of density, diversity, uniformity, dominance, substrate type, similarity analysis, and principal component analysis. The macrozoobenthos obtained consist of six classes, namely Bivalves, Gastropods, Polychaeta, Scapopoda, Malacostraca, and Echinoidea. The density of macrozoobenthos was highest at dry season in September. The diversity index is categorized as high with low uniformity and dominance. The most influential water quality for macrozoobentos in Banten Bay is DO and temperature.
Keyword: makrozoobentos di Teluk Banten, kualitas air, struktur komunitas, Teluk Banten

Judul: Algorithm Construction of HLI Hash Function
Abstrak: Cryptography is a study of mathematical techniques related to aspects of information security regarding on confidentiality, data integrity, entity authentication, and data origin authentication. Data integrity is a service, which is associated with the conversion of data carried out by unauthorized parties. To maintain data integrity, one can use hash functions. An hash function is a computationally efficient function to map an arbitrary length bitstring to a fixed length bitstring called as hash value. The use of hash function in maintaining the information integrity and authentication is in digital signatures, virus protection, and software distribution. Illustration of the hash function in the integrity and authentication of information that is emailed as follow. The sender sends the information and hash value to the recipient. If the hash value of the information is equal to the hash value that is sent by the sender, then information has not been changed. Otherwise the information has been changed. An hash function has two properties, one way and collision resistance. An hash function is called one way if it is computationally infeasible to find any input such that when given any hash value . Meanwhile an hash function is called collision resistant if it is computationally infeasible to find any two inputs where such that . The purpose of this study is to construct a HLI hash function algorithm, to analyze the speed and security of the hash functions as a results of construction. Furthermore, it is to compare it with SWIFFT. Hash function is constructed based on the result of algebraic operations on modular polynomial ringwith . Modular polynomial ring noted as is a set of all polynomials of degree at most with coefficients in . can be represented as a set of integer vector of modulo in dimension . So, it is simple to implement in a computer. In vector operations, is lattice . Furthermore, alattice that is defined from certain polynomial ring is called ideal lattice. HLI hash function is consists of addition and multiplication algorithm in the ring. Addition algorithm consists of sumation operations of modulo and multiplication consists of multiplication operations of modulo . Generally, algorithm of HLI hash function consists of multiplication operations of modulo and addition operations of modulo . However, if the input is binary number then multiplication algorithm only consists of multiplication operation of modulo , so algorithm of HLI hash function consists of multiplication operations of modulo and addtion operations of modulo . An hash function is secured if it has two properties, i.e., one way and collision resistance. HLI hash function hash is one way, it is following the proof of Micciancio. It is has collision resistant because of polynomial is irreducible over and monic. Moreover, it is proved that for every unit vector , the vector is small vector. If the input of hash function is a binary number then HLI hash function only involves addition operations of modulo . So the time that used almost same with SWIFFT. The excellence of HLI hash function is the key size smaller than SWIFFT. This effect on
Keyword: hash function, modular polynomial ring, lattice