Judul: Potential Bioactivity of Trigona sp. Honey against Cancer Cells
Abstrak: Kanker adalah pertumbuhan sel-sel abnormal yang tidak terkendali di dalam tubuh. Sel-sel abnormal ini disebut sel kanker yang dapat menyusup ke tubuh normal. Jenis kanker pada penelitian ini berupa kanker payudara, kanker paru-paru, dan kanker kolon. Pengobatan umum yang dilakukan untuk mengobati kanker tersebut yaitu dengan melakukan tindakan operasi, kemoterapi, dan terapi radiasi. Beberapa pengobatan ini menyebabkan efek samping yang mungkin terjadi, yang dapat mempengaruhi kualitas hidup penderita kanker tersebut. Potensi madu Trigona sp. sebagai antikanker juga telah mendapat banyak pertimbangan karena beberapa senyawa yang mendukung untuk fungsi tersebut. Penelitian ini bertujuan untuk mengetahui aktivitas toksisitas (BSLT), aktivitas penghambatan sel kanker (MTT Assay), mendeteksi dan mengkonfirmasikan identitas senyawa yang terkandung dalam madu Trigona sp. secara selektif (LC-MS/MS), interaksi antara suatu senyawa dengan molekul target, salah satunya reseptor (in silico docking) dari sumber madu Trigona sp. Hasil penelitian toksisitas dengan BSLT dari madu Trigona sp. bahwa di daerah Banten 1, Bangka, dan Batu Ampar dapat berpotensi untuk dikembangkan sebagai antikanker karena memiliki nilai toksisitas < 30 μg/mL. Uji sitotoksisitas dengan MTT Assay ditemukan bahwa madu Trigona sp. dari Banten 2 tidak dapat menghambat sel kanker A549, dan pesentase penghambatan terbaik terhadap sel MCF-7, A549, dan WiDr, yaitu berturut-turut dari madu Banten 1, Bangka, dan Banten 1. Terdapat 38 senyawa berhasil teridentifikasi dengan LC-MS/MS dari madu Trigona sp. asal Banten 1 dan Bangka yang memiliki kandungan aktivitas antikanker yaitu senyawa kynurenic acid, oxyquinoline, dan glaucarubinone. Senyawa antikanker terbaik ialah glaucarubinone yang memiliki interaksi sangat kuat terhadap reseptor EGFR dengan nilai energi afinitas sebesar -8,8 kkal/mol dan konstanta inhibisi sebesar 3,4 × 10-7 μM. senyawa tersebut memiliki potensi sebagai antikanker pankreas dengan menghambat enzim EGFR. Hasil senyawa tersebut perlu dilakukan kajian lebih lanjut mengenai uji in vitro untuk memberikan data yang lebih lengkap sebagai antikanker., Cancer is characterized by uncontrolled growth of abnormal cells in the body. These abnormal cells are called cancer cells, which can infiltrate the normal body. The types of cancer in this study were breast, lung, and colon cancer. Common treatments that are done to treat these cancers are surgery, chemotherapy, and radiation therapy. Some of these treatments may cause side effects, which can affect the quality of life of cancer patients. The potential of Trigona sp. honey as an anticancer agent has also received considerable attention owing to the presence of several compounds that support this function. This study aimed to determine the toxicity activity (BSLT) and cancer cell inhibitory activity (MTT Assay), detection and confirmation of the identity of compounds contained in Trigona sp. honey selectively (LC MS/MS), and the interaction between a compound and target molecules, one of which is a receptor (in silico docking) from the source of Trigona sp. honey. The results of toxicity research with BSLT from Trigona sp. honey in Banten 1, Bangka, and Batu Ampar areas can potentially be developed as an anticancer agent because it has a toxicity value of < 30 μg/mL. The cytotoxicity test with MTT Assay revealed that Trigona sp. honey from Banten 2 could not inhibit A549 cancer cells, and the best inhibition percentage was observed against MCF-7,
Keyword: A549, madu Trigona sp., MCF-7, WiDr

Judul: Cytotoxicity Test and Colon Anticancer Activity of the Monoflora Honey of Bees (Apis Mellifera) with Variations of Nectar Sources
Abstrak: Kanker kolon merupakan kanker dengan jumlah kasus dan jumlah kematian terbanyak di dunia. Madu monoflora dari Apis mellifera merupakan salah satu bahan alam yang berpotensi sebagai antikanker kolon dan memerlukan kajian lebih lanjut karena efektivitasnya dipengaruhi oleh perbedaan jenis sumber nektar. Penelitian ini bertujuan menentukan jenis madu monoflora Apis mellifera yang sumber nektarnya berasal dari tanaman-tanaman di Indonesia sebagai antikanker kolon terbaik berdasarkan nilai LC50 dan persentase inhibisi terhadap sel WiDr. Madu diuji dengan metode BSLT dan MTT assay kemudian data dianalisis secara statistik serta dilakukan PCA dan klasterisasi K-means. Hasil penelitian menunjukkan seluruh madu berpotensi sebagai antikanker kolon. Analisis biplot pada PCA dan klasterisasi K-means menunjukkan madu dengan potensi antikanker kolon terbaik adalah madu akasia, madu mangga, dan madu kapuk randu. Ketiga madu tersebut memiliki nilai LC50 rendah dengan metode BSLT, yaitu masing-masing sebesar 153,816 ppm, 164,207 ppm, dan 170,053 ppm. Ketiga madu tersebut memiliki persentase penghambatan yang besar terhadap sel kanker WiDR, yaitu masing-masing sebesar 72,42%, 74,31%, dan 63,96%., Colon cancer is a cancer with the highest number of cases and deaths in the world. Monoflora honey from Apis mellifera is one of the natural ingredients that has the potential as an anticancer colon and requires further study because its effectiveness is influenced by different types of nectar sources. This study aims to determine the type of Apis mellifera monoflora honey which sources its nectar from plants in Indonesia as the best colon anticancer based on the LC50 value and the percentage of inhibition against WiDr cells. Honey was tested using the BSLT and MTT assay methods, then the data was analyzed statistically and PCA and K-means clustering were carried out. The results showed that all honey has the potential as an anticancer colon. Biplot analysis on PCA and K-means clustering showed that the honey with the best colon anticancer potential was acacia honey, mango honey, and kapok honey. The three honeys had low LC50 values using the BSLT method, namely 153,816 ppm, 164,207 ppm and 170,053 ppm respectively. The three honeys had a large percentage of inhibition against WiDR cancer cells, namely 72,42%, 74,31% and 63,96%, respectively.
Keyword: cytotoxicity, antiproliferation, honey, nectar, clustering

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