Judul: Kontraksi pola graf klausa Bahasa Indonesia menggunakan metode knowledge graph
Abstrak: Knowledge graph is a new method of knowledge representation. In principle, knowledge graph consists of concept and relationship. There are already rules of constructing wordgraphs of noun, verb, adverb, adjective, preposition, and phrase. However to be able to represent the meaning of Indonesian text we still need other rules to represent clause and sentence. This research is mainly focused on constructing a subordinative clause graph and building rules of subordinative clause graph formation. In this research, subordinative clause is classified by analyzing the subordinative relationship (time, condition, if-relation, purpose, consessive, resemblace, because-relation, result, way-relation, tool-relation, complementation, attributive, equative, and comparative) and the structure of clause (the combination of subject, predicate, object, complement, and description). After the result of constructing subordinative clause graph is analyzed and formulated, the subordinative clauses are classified based on the same conjunction graph pattern and clause structure graph pattern, and we get 13 conjunction graph patterns and 48 clause structure graph patterns for subordinative clause in Indonesian.
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Judul: Analysis of Contruction Indonesian Sentence Graph Using Knowledge Graph
Abstrak: The syntactic analysis of a language has an advantage as compared to the semantic analysis in terms of the time used to process the language, that is faster than semantic analysis. But, if the syntactic analysis is not followed by its semantic analysis it tends to cause ambiguity. For example is an Indonesian sentence kucing memakan tikus mati. In Indonesian, this sentence has an ambiguity whether the cat died after eating the rat or the rat has died eaten by the cat. Therefore, semantic analysis is needed to explain the meaning of this sentence. In such a case the method of knowledge graph can be used to explain the meaning of the sentence. This method is a new approach for natural language understanding. There are 9 binary relationships and 4 frame relationships as components of the knowledge graph. Using this method, a word is represented by a word graph, and a sentence is represented by a sentence graph. Analyzing the sentence using knowledge graph needs a chunking rule. This rule has already existed in the structure of English and Chinese, but it has not existed in Indonesian yet. The objective of this research is to constructs a chunking rule in Indonesian sentences and a sentence graph in Indonesian with the knowledge graph method. The benefits of this research is to provide the rules of constructing Indonesian sentence graph and to create an Indonesian sentence graph. The result of this research is Indonesian chunking rule with 8 indicators, they are comma and period sign, pronoun, adverb, preposition, jumps, logic words, interlude of breath, and conjunction. The other result is Indonesian sentences graph that describes semantics aspect of the sentence.
Keyword: knowledge graph, sentence graph, chunk graph, chunk indicators

Judul: Ketahanan Benih Udang Vaname Litopenaeus vannamei SPF (Specific Pathogen Free) Terhadap Infeksi Buatan TSV (Taura Syndrome Virus)
Abstrak: White shrimp culture had suffered from losses due to the outbreak of TSV in which mortality could achieved 95%. Therefore SPF shrimp is required as a preventive effort. This research was conducted to evaluate the resistance of white shrimp seed obtained from several SPF broodstock to TSV. Infection of TSV was carried out by feeding flesh of TSV-infected shrimp to PL 30 for 2 days at a feeding rate of 50%. After that, shrimp was fed commercial diet at 10% feeding rate till the end of experiment (11 days of culture). Parameters observed included survival rate, clinical symptoms, and weigth gain. After 11 days of culture, all the tested shrimps were infected, showing clinical symptom namely black spot on the body and the tail became red. The highest survival rate was obtained at S6 code (77,33±3,51%), while the lowest was observed at S4 code but statistically was different significantly (P<0,05) compared to other.
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