Rahmad Mahendra

Implementation of weighted parallel hybrid recommender systems for e-commerce in Indonesia

This paper focus on building recommender system with weighted parallel hybrid method for e-commerce in Indonesia. The dataset was derived from one of the largest ecommerce company in Indonesia. The experiments used three sampling techniques, namely bootstrapping validation, timing series and systematic sampling. The best result of these experiments yields F1-measure of 9.99% which is achieved by the combination of user-based collaborative filtering approach and content-based filtering approach. Moreover, the value of evaluation metrics in this research is not much different from the previous research of recommender system. This indicates that recommender systems can be applied to e-commerce companies in Indonesia.

Knowledge representation system for copula sentence in Bahasa Indonesia based on Web Ontology Language (OWL)

Now the knowledge source in natural language text are available in large quantities. There is an increasing need of knowledge representation, then it would require the knowledge processing on text automatically. The previous research has built on knowledge representation system of natural language text that is OWLizr. However, this study has not been able to handle the knowledge representation in concepts that describe a particular object. This paper developed a knowledge representation system in copula sentences containing concepts in Bahasa Indonesia. If the concept can be handled then the relationship between concepts with components of existing ontology can defined. This supports ontology engineering process that build domain ontology by enrich knowledge of the existing knowledge base. This system combine NLP (Natural Language Processing) techniques and OWL (Web Ontology Language) to model the knowledge contained in the copula sentence. The testing in this system is by unit testing and testing on collection of sentences which adapted from Wikipedia. The results of this research are system can represent knowledge in copula sentences containing concepts in Bahasa Indonesia. The system can generate output in OWL knowledge base that can share and reuse for other systems.