Learn More
Frequent pattern mining discovers patterns in transaction databases based only on the relative frequency of occurrence of items without considering their utility. For many real world applications, however, utility of itemsets based on cost, profit or revenue is of importance. The utility mining problem is to find itemsets that have higher utility than a(More)
—The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound(More)
High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold. However , mining high utility itemsets presents a greater challenge than frequent itemset mining, since high utility itemsets lack the anti-monotone property of frequent itemsets. Transaction Weighted(More)
Mining High Utility Itemsets from a transaction database is to find itemsests that have utility above a user-specified threshold. This problem is an extension of Frequent Itemset Mining, which discovers itemsets that occur frequently (i.e. with occurrence count larger than a user given value). The problem of finding High Utility Itemsets is challenging,(More)
Research concerning Twitter mining becomes an interesting research topic recently. It is proven by numerous number of published paper related with this topic. This research is intended to develop a prototype system for classifying Indonesian language tweets. The prototype includes preprocessing step, main information retrieval and classification system.(More)
This paper discusses the development of multi-document summarization for Indonesian documents by using hybrid abstractive-extractive summarization approach. Multi-document summarization is a technology that able to summarize multiple documents and present them in one summary. The method used in this research, hybrid abstractive-extractive summarization(More)
The exponential growth of the data may lead us to the information explosion era, an era where most of the data cannot be managed easily. Text mining study is believed to prevent the world from entering that era. One of the text mining studies that may prevent the explosion era is text classification. It is a way to classify articles into several predefined(More)
Research about text summarization has been quite an interesting topic over the years, proven by numerous number of papers related with discussion of their studies such as approaches, challenges and trends. This paper's goal is to define a measurement for text summarization using Semantic Analysis Approach for Documents in Indonesian language. The applied(More)
Advertisement serving on website is a prosperous business with huge market and millions of dollar prospect. By placing right advertisement at right time and place to right people, advertiser can increase their revenue by huge margin. The question is how advertiser and broker can push the right advertisement to the right user. User profiling can be used to(More)
Recommendation system has been proposed for years as the solution of information era problem. This research strives to develop an intelligent recommendation system based on user click behavior on news websites. We extracted frequent itemsets and association rules from the web server log of a news website, performed a pre-computation of similarity between(More)