Judul: Yellowfin Tuna (Thunnus albacares) Skin Collagen as Active Ingredients Candidate in Anti-aging Cream
Abstrak: Penambahan bubuk kolagen kulit ikan tuna sirip kuning (Thunnus albacares) sebagai kandidat bahan aktif dalam pembuatan krim anti-aging dapat berperan sebagai pelembab bagi kulit. Penelitian ini bertujuan menentukan karakteristik krim anti-aging serta konsentrasi kolagen kulit ikan tuna sirip kuning (T. albacares) terbaik sebagai kandidat bahan aktif pada sediaan krim anti-aging. Tahapan penelitian ini yaitu pembuatan sediaan krim anti-aging dan penentuan formula terbaik. Krim anti-aging diformulasikan dengan penambahan kolagen sebanyak 0%, 0,5%, dan 1% (b/b). Formulasi sediaan krim anti-aging terbaik didapati pada krim dengan penambahan kolagen larut pepsin kulit tuna sirip kuning dengan konsentrasi 0,5% (KA). Sediaan krim anti-aging terpilih telah sesuai dengan acuan yang ada. Sediaan krim KA (0,5% kolagen kulit tuna sirip kuning), memiliki rata-rata nilai sensori 7,06, nilai pH sebesar 7,53, daya sebar 7,05 cm, daya lekat 21,81 detik, viskositas 9650 ± 268,51 cP, dan kemampuan melembabkan kulit sebanyak 26,19%., The addition of yellowfin tuna (Thunnus albacares) skin collagen powder as active ingredients candidate in the production of anti-aging cream have a role as moisturizer agent for the skin. This study aimed to determine the anti-aging cream’s characteristics and the best concentration yellowfin tuna (T. albacares) skin collagen as active ingredients candidate for the anti-aging cream. This research phase involved making anti-aging cream preparations and determining the best formulation. Anti-aging cream formulated with 0%, 0,5%, and 1% collagen (w/w). The best anti-aging cream formulation found on cream with pepsin soluble collagen yellowfin tuna skin at concentration of 0,5% (KA). The selected anti-aging cream has followed the existing references. KA cream (0,5% yellowfin tuna skin collagen) had an average sensory value of 7,06, pH value of 7,53, spreadability about 7,05 cm, adhesion of 21,81 seconds, viscosity of 9650 ± 268,51 cP, and ability to moisturize the skin by 26,19%.
Keyword: formula, kolagen larut pepsin, kosmetik, krim, pelembab

Judul: Formulasi dan Uji Efikasi Serum Wajah dengan Bahan Aktif Hidrolisat Kolagen Ikan Kakap (Lutjanus sp.).
Abstrak: Hidrolisat kolagen ikan dapat meningkatkan kelembaban kulit. Tujuan penelitian ini adalah menentukan formula serum wajah dengan bahan aktif hidrolisat kolagen ikan kakap dan karakterisasinya. Formula serum wajah mengandung hidrolisat kolagen ikan kakap dengan konsentrasi 0%, 1%, 2%, dan 3% (b/v). Serum dianalisis dengan metode sensori untuk menentukan formula terpilih. Serum terpilih dikarakterisasi pH, viskositas, uji tempel, uji efikasi, dan uji keamanan mikrobiologis setelah penyimpanan 18 bulan. Serum terpilih yaitu formula yang mengandung 3% hidrolisat kolagen ikan kakap. Karakteristik serum wajah terpilih memiliki pH 5, viskositas 23.9±0.4 cP, dan tidak menimbulkan iritasi serta reaksi alergen. Serum terpilih meningkatkan kadar air dan menurunkan kadar minyak pada responden berusia 24 tahun dan mampu mempertahankan kelembaban kulit responden berusia 40 tahun. Angka lempeng total dan angka kapang serta khamir setelah penyimpanan selama 18 bulan adalah ≤ 1x102 cfu. Hal ini sesuai dengan standar BPOM.
Keyword: hidrolisat kolagen, kelembaban kulit, serum

Judul: Addition of preposition word graph generator module in bogor_delft_construct application
Abstrak: Knowledge graph is a new method used to analyze text and represent in a graph form. Word graph consists of concept and relation which represents the meaning of a word in a graph form. Ontology used in a word graph structure consists of 8 binary relationships, 4 frame relationships, and a focus. Bogor_Delft_Construct is an application that implements knowledge graph method to analyze text written in Bahasa Indonesia, However, this application still has some limitations in analyzing vocabularies such as prepositions. This research aims to add a module to the application that could generate word graph of preposition based on preposition patterns prosposed by Wulan (2009). The addition of preposition word graph generator module that was done in Bogor_Delft_Construct could recognize 23 prepositions which were followed by its auxiliary words. The preposition’s auxiliary words were divided into 12 documents; each has different nominative meaning.
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