Judul: The construction of a rule to combine sentence graphs
Abstrak: Nowadays, people prefer to read an electronic article or text and it requires a short time to understand the content or substance of the text. This is due to time constraints and other activities are more important than reading. However, reading remains an important thing to do. This can be solved by making a summary of the text. Summary text is done by using the Knowledge Graph and carried through two stages, namely representation and simplification or simplification results representation. Representation of a text is carried out in stages because the text has several elements, ie words, phrases, clauses, sentences, and paragraphs. Representation of text elements that have been researched are words, phrases, and clauses. The representation can be used to generate a representation of the sentence. Representation of sentences requires a rule so that everyone produces the same representation. Therefore, the authors conducts a study on a representation of the meaning of the sentence. This research aims are to create a rule to construct a sentence graph and a rule to combine sentence graphs. The benefit of this research is to provide the rules for constructing a sentence graph thus eliminating the ambiguity of the sentence. Knowledge Graph method is a way to describe or represent of human language that is more centered on the semantic aspects of the syntactic aspect. The Knowledge Graph consists of two components, namely concept and relationship. Concept consists of three elements, namely token, type, and name. Token is a concept that is understood by a person according to their perspectives; this affects token to be subjective. Type is a concept that has been agreed and it is considered as general information so that affect type to be objective. Name is an individualized concept and unique. Relationship consists of several types of relations, namely ALI, CAU, SUB, DIS, ORD, PAR, EQU, and SKO. ALI relation is used to connecting the token with the type. CAU relation is used to connecting the two concepts that have a causal relationship. SUB relation expresses a concept as a part of another concept. DIS relation is used to presenting two concepts are not the same. ORD relation is stated that two things have a certain order to each other. PAR relation is stated that a thing is an attributes (properties) of the other things. EQU relation is used to connecting the two equal or similar concept. SKO relation expresses dependency relationship between two concepts. This study uses a sentence that has a type based on the basic structure of the sentence, which is the structure of subject-predicate sentence, the structure of subject-predicate-object sentence, the structure of subject-predicate-adverb sentence, the structure of subject-predicate-complement sentence, the structure of subject-predicate-object-adverb sentence, and the structure of subject-predicate-object-complement sentence. Chronology of the research is as follows, (1) studying literature which is about the types of sentences, the Knowledge Graph methods, the application of the Knowledge graph methods, and the standard procedures for Indonesian language. (2) creating rules to construct a sentence graph, with the following steps, (2a) construct a sentence graph, (2b) test rules to
Keyword: a rule to combine sentence graphs, a rule to construct a sentence graph, Knowledge Graph, Sentence Graph

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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