International Journal of Advances in Intelligent Informatics
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Association Rule Algorithm Sequential Pattern Discovery using Equivalent Classes (SPADE) to Analyze the Genesis Pattern of Landslides in Indonesia
Full text linkLandslide is one of movement of soil, rock, soil creep, and rock debris that occurred the move of the slopes. It is caused by steep slopes, high rainfall, deforestation, mining activities, and erosion. The impacts of the landslide are loss of property, damage to facilities such as homes and buildings, casualties, psychological trauma, disrupted economic and environmental damage. Based on the impacts of landslide, mitigation required to take early precautions are to know how the pattern of association between the sequence of events landslides and to know how the associative relationship pattern of earthquakes. Based on the impacts, the results of this research is associative relationship pattern is obtained from data flood that occurs in Indonesia, namely in case of heavy rain will occur labile soil structure to support the value of 0.37, confidence level of 41% and the power of formed ruled is 1.02
Automatic differentiation based for particle swarm optimization Steepest descent direction
Full text linkParticle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution
RETRACTED: RCE-Kmeans Method for Data Clustering
Full text linkRETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “RCE-Kmeans Method for Data Clustering†Vol 1, No 2, pp. 107-114, July 2015, DOI: http://dx.doi.org/10.26555/ijain.v1i2.38.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in Indonesian Journal of Computing and Cybernetics Systems, Vol. 9, No. 2 (July 2015), pp. 157-166, DOI: http://doi.org/10.22146/ijccs.7544, entitled "Metode RCE-Kmeans untuk Clustering Data".The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article
Adding synonyms to concepts in ontology to solve the problem of semantic heterogeneity
Full text linkNowadays many department (community) are thinking how to get more knowledges and metadata by linking more systems from other community. There are great challenges to make all systems organizing knowledge and sharing metadata – to make it easy searched, indexed and used in different context. In this paper we will focus on metadata in specific domain - ‘Poverty’2. Regardless of the various definitions of poverty, in this paper we will focus on managing metadata in “Poverty†with many different terms therein. Ontology Mapping is the process of relating similar concepts or relations from different sources through some equivalence relation. Mapping allows finding correspondences between the concepts of two ontologies. If two concepts correspond, then they mean the same thing or closely related things. Currently, the mapping process is regarded as a promise to solve the problem between ontologies since it attempts to find correspondences between semantically related entities that belong to different ontologies. It takes as input two ontologies, each consisting of a set of components (classes, instances, properties, rules and axioms). Based on the presented reasons, we believe that ontologies with common terms and common concepts are very important in a metadata sharing process
Target threat assessment using fuzzy sets theory
Full text linkThe threat evaluation is significant component in target classification process and is significant in military and non military applications. Small errors or mistakes in threat evaluation and target classification especial in military applications can result in huge damage of life and property. Threat evaluation helps in case of weapon assignment, and intelligence sensor support system. It is very important factor to analyze the behavior of enemy tactics as well as our surveillance. This paper represented a precise description of the threat evaluation process using fuzzy sets theory. A review has been carried out regarding which parameters that have been suggested for threat value calculation. For the first time in this paper, eleven parameters are introduced for threat evaluation so that this parameters increase the accuracy in designed system. The implemented threat evaluation system has been applied to a synthetic air defense scenario and four real time dynamic air defense scenarios. The simulation results show the correctness, accuracy, reliability and minimum errors in designing of threat evaluation syste