Oviliani Yenty Yuliana

Fuzzy Bayesian Belief Network for Analyzing Medical Track Record

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, the BBN concept is extended with meaningful fuzzy labels for mining fuzzy association rules. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels secondary disease and complication disease are defined for disease classification. We extend the concept of Mutual Information dealing with fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining association among nodes. A brief algorithm is introduced to develop the proposed concept. Experimental results of the algorithm show processing time in the relation to the number of records and the number of nodes. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.

Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules

Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each domain data. For example, fuzzy labels poor disease, moderate disease, and severe disease are defined to describe a condition/type of disease. We extend and propose a concept of fuzzy information gain to employ the highest information gain for splitting a node. In the process of generating fuzzy association rules, we propose some fuzzy measures to calculate their support, confidence and correlation. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.

A Conceptual Schema Based XML Schema with Integrity Constraints Checking

The more popular XML for exchanging and representing information on Web, the more important flat XML (XML) and intelligent editors become. For data exchanging, an XML data with an XML Schema and integrity constraints are preferred. We employ an object-role modeling (ORM) for enriching the XML Schema constraints and providing better validation the XML Data. An XML conceptual schema is presented using the ORM conceptual model. Editor Meta Tables are generated from the conceptual schema diagram and are populated. A User XML Schema base on the information in the editor meta tables is generated. However, W3C XML Schema language does not support all of the ORM constraints. Therefore, we propose an Editor XML Schema and an Editor XML Data to cover unsupported the ORM constraints. We propose the algorithms for defining constraint in the User XML Schema and extending validity constraint checking. Finally, XQuery is used for extending validity checking.

IMPLEMENTASI REFERENTIAL INTEGRITY CONSTRAINT PADA MICROSOFT ACCESS DALAM UPAYA MEMELIHARA KONSISTENSI DATA

Three main processes in Natural Language Processing are syntax analysis or parsing, semantic interpretation and contextual interpretation. This paper discuss about the first and the second of these processes. Parsing is the recognition of the sentence structure based on a grammar and a lexicon. Parsing can be done in either top-down or bottom-up methods, each has its own advantages and disadvantages. Top-down parsers can not handle grammar with left-recursion, where bottomup parsers can not handle grammar with empty production. The best parsers combine these two

Pemetaan Penderita Pneumonia di Surabaya dengan Menggunakan Geostatistik

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.

Deriving Conceptual Schema from XML Databases

In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML Databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an Object-Role Model conceptual schema diagram.

Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.