Judul: Prevalence of Sanitary Indicator and Pathogenic Bacteria Contamination in Chicken Meat and Chicken Meat Products in Indonesia: A Systematic Review and Meta-Analysis
Abstrak: Daging ayam menjadi salah satu pangan hewani yang digemari masyarakat di Indonesia. Daging ayam memiliki kandungan nutrisi yang tinggi sehingga memiliki risiko tinggi terhadap cemaran mikrobiologis, sehingga perlu diperhatikan keamanannya. Penelitian tentang cemaran pada daging ayam dan produk olahannya telah banyak dilakukan di berbagai wilayah di Indonesia dengan hasil yang bervariasi. Melalui meta-analisis menggunakan berbagai data yang telah dilaporkan, dapat diperoleh ukuran sampel penelitian yang lebih besar agar hasil lebih umum dan komprehensip. Prosedur penelitian dilakukan dengan dua tahapan, tahap pertama tinjauan sistematis yang mencakup pencarian literatur pada basis data (Google Scholar¸ Cross Ref, Pro Quest, PubMed dan Science Direct), seleksi literatur, ekstraksi data dan tahap kedua meta-analisis. Pencarian literatur pada basis data Google scholar dan Cross Ref menggunakan bantuan perangkat lunak Publish or Perish 8 dengan kata kunci/frasa dan pencarian literatur pada basis data PubMed, Science Direct dan Pro Quest menggunakan kata kunci/frasa dan operator Boolean OR dan AND untuk mempermudah pencarian literatur. Meta-analisis dilakukan dengan menggunakan perangkat lunak meta-analisis Open Meta [Analyst]. Pengukuran effect size yang merupakan proporsi/prevalensi cemaran bakteri ditentukan dengan model random-effects dengan metode DerSimonian–Laird. Hasil meta-analisis disajikan dalam tabel dan forest plot. Hasil seleksi dengan PRISMA menghasilkan 44 studi yang berasal dari 25 literatur yang terkait dengan cemaran bakteri pada daging dan olahannya di Indonesia. Berdasarkan hasil meta-analisis diperoleh prevalensi sampel tercemar bakteri di Indonesia sebesar 32,8% (95% CI: 21,6-44,1%; p<0,001). Nilai heterogenitas yang tinggi (I2 =99,49%) menungkinkan dilakukan meta-analisis sub-group berdasarkan jenis sampel, jenis bakteri pencemar dan lokasi pengambilan sampel. Hasil meta-analisis sub-group berdasarkan jenis sampel menunjukkan prevalensi tertinggi diperoleh dari olahan daging ayam (47,8%; 95% CI: 13,9-81,8%; p<0,001), sedangkan jenis bakterinya adalah E. coli (45,3%; 95% CI:15,4-75,3%; p<0,001), S. aureus (44,3%; 95% CI: -5,2-93,8%, p<0,001), Salmonella sp. (30,6%; 95% CI: 18,8-42,5%; p<0,001), Campylobacter sp. (20%; 95% CI: 10,9-29,0%; p<0,001), dan L. monocytogenes (15,7%; 95% CI:10,5-21,0%; p=0,840). Daging ayam dan olahannya dari pasar tradisional mempunyai tingkat prevalensi cemaran bakteri yang lebih tinggi (31,2%; 95% CI: 20,9-41,4%; p<0,001) dibandingkan dengan pasar modern (18,2%; 95% CI: 8,9-27,5%; p<0,001). Tingginya cemaran bakteri pada produk olahan daging ayam (47,8%; 95% CI: 13,9-81,8%; p<0,001) mengharuskan produsen selalu menerapkan praktik keamanan pangan mulai dari pemilihan bahan baku hingga pangan siap dikonsumsi. Prevalensi cemaran pada meta-analisis sub-group memiliki potensi bias karena jumlah studi dan informasi yang terbatas., Chicken meat is one of the most popular animal-based foods in Indonesia. Chicken meat has a high nutritional content that has a high risk of microbiological contamination, which necessitates attention to its safety. Research on contamination in chicken meat and its processed products has been conducted in various regions in Indonesia with varying results. A larger sample size can be obtained through meta-analysis using reported data to generate more generalizable and comprehensive findings. The research procedure consisted of two stages: a systematic review phase that included a literature search in databases (Google Scholar, Cross Ref, Pro Quest, PubMed, and Science Direct), literature selection, data extraction, and a meta-analysis phase. A literature search in Google Scholar and Cross Ref databases was conducted using the Publish or Perish 8 software with specific keywords/phrases, while searches in PubMed, Science
Keyword: bacteria, chicken meat, Indonesia, meta-analysis, prevalence

Judul: Salmonella spp. Contamination on Chicken Carcasses and Risk Profile in the Distribution Chain in Jakarta
Abstrak: Provinsi Jakarta adalah salah satu provinsi di Indonesia dengan tingkat konsumsi protein tinggi, terutama dari karkas ayam. Salmonella spp. adalah bakteri patogen yang sering ditemukan pada karkas ayam dan dapat menyebabkan penyakit terbawa makanan. Tujuan dari penelitian ini adalah untuk menentukan prevalensi Salmonella spp. pada karkas ayam dan titik kendali kritis berdasarkan profil risiko dalam rantai distribusi di Provinsi Jakarta. Penelitian ini dilakukan dengan mengumpulkan data surveilans dari lembaga pemerintah, analisis kuantitatif Salmonella spp. terduga, survei implementasi good practices, pemeringkatan risiko, dan analisis sensitivitas. Prevalensi tingkat kontaminasi ditentukan dengan metode hitung cawan menggunakan media selektif XLT4 agar. Profil risiko dilakukan dengan pemeringkatan risiko dan analisis sensitivitas. Simulasi Monte Carlo sebanyak 100.000 iterasi diterapkan dengan menggunakan Oracle Crystal Ball®. Prevalensi tertinggi kontaminasi Salmonella spp. di Jakarta selama 2019-2023 ditemukan pada karkas ayam yang didistribusikan melalui pasar tradisional, dengan nilai prevalensi 41,86% dan konsentrasi Salmonella terduga 1,09±0,12 log CFU/g. Diikuti oleh karkas ayam yang didistribusikan dari TPHU (Tempat Pemotongan Hewan Unggas), prevalensi nilainya 17,65% dengan konsentrasi Salmonella terduga 1,92 ± 1,11 log CFU/g. Prevalensi kontaminasi Salmonella spp. untuk produk karkas ayam yang didistribusikan melalui pasar ritel modern adalah 0%. Analisis sensitivitas menunjukkan aspek paling penting adalah suhu kendaraan transportasi yang berkontribusi sebesar 12,3% terhadap risiko kontaminan untuk TPHU dan metode pembersihan peralatan yang berkontribusi 38% terhadap risiko kontaminan untuk pasar tradisional. Profil risiko menunjukkan bahwa aspek yang memiliki kontribusi tinggi terhadap tingkat risiko kontaminan dan bisa menjadi titik paling kritis yang perlu dikendalikan., Jakarta is one of province in Indonesia with high level of protein consumption especially for chicken carcasses. Salmonella spp. is pathogenic bacteria which frequently found in chicken carcasses and could cause food borne disease. The aim of this study were to determined Salmonella spp. prevalence in chicken carcasses and critical control point based on risk profile in Jakarta distribution chain. The study was conduct by compiled surveillance data from government institution, quantitative analysis of Salmonella spp. presumtive, good practice implementation survey, constructed risk ranking and analysis sensitivity. Prevalence of contaminant level determined by plate count method with selective media XLT4 agar. Risk profile was involved risk-ranking and sensitivity analysis. Monte Carlo simulation of 100,000 iterations was applied by using Oracle Crystal Ball®. The highest prevalence of Salmonella spp. contamination in Jakarta during 2019-2023 found in chicken carcasses that distributed through traditional markets, prevalence value was 41.86% with concentrations presumptive Salmonella spp is 1.09 ± 0.12 log CFU/g. Followed by chicken carcasses distributed from traditional TPHU, prevalence value was 17.65% concentration of 1.92± 1.11 log CFU/g. Prevalence of Salmonella spp. contamination for chicken carcass products that were distributed through modern retail markets is 0%. Sensitivity analysis indicated the most important aspect were temperature of transportation vehicle that contribute 12.3% to risk contaminant for TPHU and equipment cleaning method that contribute 38% to risk contaminant for traditional market. Highest rank correlation shown that those aspects had high contribution to risk level of contaminant and could be the most critical point to be controlled.
Keyword: Chicken Carcass, Salmonella spp., Critical point, Risk profile, Sensitivity analysis

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