Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

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In the classification of traditional algorithms, problems of high features dimension and data sparseness often occur when classifying text. Classifying text with traditional machine learning algorithms has high efficiency and stability characteristics. However, it has certain limitations concerning largescale dataset training. In this case, a multi-label text classification technique is needed to be able to group four labels from the news article dataset. Deep Learning is a proposed method for solving problems in text classification techniques. This experiment was conducted using one of the methods of Deep Learning Recurrent Neural Network with the application of the architecture of Long Short-Term Memory (LSTM). In this study, the model is based on trial and error experiments using LSTM and 300-dimensional word embedding features with Global Vector (GloVe). By tuning the parameters and comparing the eight proposed LSTM models with a largescale dataset, to show that LSTM with features GloVe can achieve good performance in text classification. The results show that text classification using LSTM with GloVe obtain the highest accuracy is in the sixth model with 95.17, the average precision, recall, and F1-score are 95. Besides, LSTM with the GloVe feature gets graphic results that are close to good-fit on average.