Decision mining is combination of process mining and machine learning technique to retrieve information about how an attribute in a business process affects a case's route choice. It identifies decision point by looking for XOR-splits in petri-net workflow model and analyzing rules for each choice based on available attributes using decision tree. Problem emerges when decision mining technique is used on a workflow that does not use either XOR or AND splits, for example OR-split gateway logic. OR-split does not have explicit representation in petri nets and it makes decision mining algorithm cannot find its decision point. Workflow pattern that uses OR-split as its splitting logic is multi choice. Multi choice does not have its own explicit representation in form of petri net and it is problematic to apply decision mining to this workflow pattern. To make multi choice can be analyzed by decision miner some modification needs to be applied to the petri net representation of this pattern. This paper proposes modification of OR-split gateway representation in petri net. The new representation of OR-split uses combination the existing XOR-split and AND-split to make the model easier to be analyzed using decision miner. The proposed modification do not affect the conformance of event log and process model, but will allow each choice branch to be checked by decision miner.

One type of investments in securities is stock as a sign of ownership for individual or corporation within a company. For making investment strategy and portfolio, investors should pay more attention to the co-movement of the stock price. Several methods can be used to see moving of the stock price, one of them is data mining. In this paper, association rule mining algorithm is used to find the stock movement relationship of inter-company in Indonesia. Fuzzy approach is used as tools to categorize data based on the time and transactions sequence. Experimental results show that association rule method can solve the problems. By using some values of minimum support, some strong association rules among companies can be obtained

Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.

Metaheuristic algorithms are generic optimization tools to solve complex problems with extensive search spaces. This algorithm minimizes the size of the search space by using effective search strategies. Research on metaheuristic algorithms continues to grow and is widely applied to solve big data problems. This study aims to provide an analysis of the performance of metaheuristic research and to map a description of the themes of the metaheuristic research method. Using bibliometric analysis, we examined the performance of scientific articles and described the available opportunities for metaheuristic research methods. This study presents the performance analysis and bibliometric review of metaheuristic research documents indexed in the Scopus database between the period of 2016-2021. The overall number of papers published at the global level was 3846. At global optimization, heuristic methods, scheduling, genetic algorithms, evolutionary algorithms, and benchmarking dominate metaheuristic research. Meanwhile, the discussion on adaptive neuro-fuzzy inference, forecasting, feature selection, biomimetics, exploration, and exploitation, are growing hot issues for research in this field. The current research reveals a unique overview of metaheuristic research at the global level from 2016-2021, and this could be valuable for conducting future research.