Judul: Metabolomic Analysis of Selaginella spp. (Selaginellaceae) and its Potential as Medicinal Plants
Abstrak: Selaginella merupakan salah satu marga tumbuhan paku dari suku Selaginellaceae yang memiliki keragaman spesies yang tinggi di daerah tropis dan subtropis. Selaginella memiliki potensi dan manfaat yang cukup besar terutama dalam bidang kesehatan, khususnya dapat dimanfaatkan sebagai tumbuhan obat. Namun, informasi metabolit pada Selaginella spp. masih terbatas. Penelitian ini bertujuan menganalisis metabolit pada Selaginella spp. yang dapat dimanfaatkan sebagai obat dan dapat digunakan sebagai pembeda antar spesies. Metode penelitian yang dilakukan meliputi preparasi sampel, ekstraksi sampel, analisis kandungan metabolit dengan LC-MS/MS, dan analisis data. Analisis metabolomik meliputi identifikasi metabolit menggunakan perangkat lunak MZmine 3.1.0 beta, analisis profil metabolit, klaster heatmap, dan analisis gugus menggunakan MetaboAnalyst 5.0. Hasil analisis menunjukkan sebanyak 113 metabolit terdeteksi pada tiga spesies Selaginella yang diamati. Analisis gugus berdasarkan ciri metabolit mengelompokan seluruh individu menjadi 3 kelompok sesuai dengan spesiesnya. Metabolit Selaginella willdenowii lebih mirip Selaginella ornata dibandingkan dengan Selaginella plana. Beberapa senyawa yang terdeteksi memiliki manfaat penting dalam bidang pangan, kesehatan, dan kecantikan. Senyawa aloeresin a, naringin, rhoifolin, isoschaftoside, isovitexin 2''-oarabinoside, 4-vinylphenol, dan isofraxidin merupakan kandidat penanda kimia pada Selaginella plana. Senyawa velutin, aspalathin, 2-hydroxybenzaldehyde, gardenin a, catechol, dan n-(1,1-dimethyl-3-oxobutyl)acrylamide merupakan kandidat penanda kimia pada Selaginella ornata. Senyawa kapakahine b, dihydrojasmonic acid, linoleic acid, alantoic acid, menaquinone, dan nandrolone merupakan kandidat penanda kimia pada Selaginella willdenowii., Selaginella is a genus of Pteridophyta from the Selaginellaceae which has high diversity in tropical and subtropical areas. Selaginella has considerable potential and benefits, particularly it can be used as a medicinal plant. However, information on metabolites in Selaginella spp. is still limited. This study aimed to analyze the metabolite compounds in Selaginella spp. which can be used as medicine and as a biomarker to differentiate among species. The research methods included sample preparation, sample extraction, analysis of metabolite content by LC-MS/MS, and data analysis. The metabolomic analysis included the identification of metabolites using MZmine 3.1.0 beta software, analysis of metabolite profiles, and cluster analysis using MetaboAnalyst 5.0. The results of the analysis showed that as many as 113 different metabolites were detected in the three Selaginella species. Metabolite based cluster analysis grouped all individuals into 3 groups according to the species. Selaginella willdenowii metabolites are more similar to Selaginella ornata than Selaginella plana. Some of the metabolites detected have important benefits for food, health, and beauty. Aloeresin a, naringin, rhoifolin, isoschaftoside, isovitexin 2''-o-arabinoside, 4- vinylphenol, and isofraxidin compound are candidate chemical markers in Selaginella plana. Velutin, aspalathin, 2-hydroxybenzaldehyde, gardenin a, catechol and n-(1,1-dimethyl-3-oxobutyl)acrylamide compound are candidate chemical markers in Selaginella ornata. Kapakahine b, dihydrojasmonic acid, linoleic acid, alantoic acid, menaquinone, and nandrolone compound are candidate chemical markers in Selaginella willdenowii.
Keyword: cluster analysis, heatmap, LC-MS/MS, metabolite

Judul: Biodiversity, Ethnobotany, and Antioxidant Ability of Selaginella spp. from Halimun-Salak Mountain National Park (TNGHS).
Abstrak: Biodiversity and ethnobotany of Selaginella were studied in Halimun-Salak Mountain National Park (TNGHS) to describe the diversity and the use of Selaginella as medicinal plant. The diversity study of Selaginella was done by exploring Selaginella at 6 sampling points. The ethnobotany study of Selaginella was done by interviewing respondents at the villagers around Bunder Mountain, Citalahab Sentral, and Kasepuhan Adat Banten Kidul. There were eight species Selaginella found in TNGHS which were S. willdenovii, S. ornata, S. plana, S. intermedia, S. involvens, S. alutacia, S. subalpina and one species that has not been identified yet. The first two species were found dominantly in this location. The community in the location of study called Selaginella as paku rane. At the villages around Bunder mountain and Citalahab Sentral, Selaginella is also called as rande. The plant was traditionally used as fresh vegetables, postnatal and wound treatment, and body fitness maintenance. Antioxidant ability of Selaginella was tested by performing hydroxyl radical scavenging activity based on Fenton reaction. Three species of Selaginella were tested their hydroxyl radical scavenging activity. S. plana was the most potential species as an antioxidant followed by S. ornata and S. willdenovii. The crude extract of S. plana showed the best antioxidant activity at concentration of 75 μg/ml. Key words: Selaginella, biodiversity, ethnobotany, antioxidant, Halimun-Salak Mountain National Park (TNGHS)
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Judul: Construction of Strongly Optimal Binary Linear Code with Minimum Distance 9 and 11
Abstrak: A binary linear code of length n over q F is a subspace of n q F . A code has three parameters that attached to it, namely length, dimension, and minimum distance. A code with length n, dimension k and minimum distance d is often called [n, k, d ]-code. Usually, when two parameters are given, then we want to find a code that has the best value for the last parameter. Based on Gilbert- Varshamov bound, if a [n, k, d ]-code exists and can not be expanded, we call it a strongly optimal code. In this paper, we construct strongly optimal code with minimum distance 9 and 11. In constructing the code, we created a theorem and algorithm based on Gilbert-Varshamov bound before we implement the algorithm to MAPLE programming language. Because of computational limitations, the program can only construct up to k = 10 for d = 9 and k = 12 for d = 11.
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