Text classification is the process of grouping documents based on similarity in categories. Some of the obstacles in doing text classification are many words appeared in the text, and some words come up with infrequent frequency (sparse words). The way to solve this problem is to conduct the feature selection process. There are several filter-based feature selection methods; some are Chi-Square, Information Gain, Genetic Algorithm, and Particle Swarm Optimization (PSO). Aghdam's research shows that PSO is the best among those methods. This study examined PSO to optimize the k-Nearest Neighbour (k-NN) algorithm's performance in categorizing news articles. k-NN is an algorithm that is simple and easy to implement. If we use the appropriate features, then the k-NN will be a reliable algorithm. PSO algorithm is used to select keywords (term features), and it is continued with classifying the documents using k-NN. The testing process consists of three stages. The stages are tuning the parameter of k-NN, the parameter of PSO, and measuring the testing performance. The parameter tuning process aims to determine the number of neighbours used in k-NN and optimize the PSO particles. Otherwise, the performance testing compares the performance of k-NN with and without using PSO. The optimal number of neighbours is 9, with the number of particles is 50. The testing showed that using the k-NN with PSO and a 50% reduction in terms. The results 20 per cent better accuracy than k-NN without PSO. Although the PSO's process did not always find the optimal conditions, the k-NN method can produce better accuracy. In this way, the k-NN method can work better in grouping news articles, especially in Indonesian language news articles
The amount of potential investment in Padang City, Indonesia since 2017 attracted many investors to contribute to the city. One of the investments is a 12-story hotel that will be constructed in By Pass Street of the city. The hotel is located in a high seismic zone area, so the seismic base isolation has been proposed to be used in the hotel building. The main aim of using a seismic base isolation device is to reduce the inertia forces introduced in the structure due to earthquakes by shifting the fundamental period of the structure out of dangerous resonance range and concentration of the deformation demand at the isolation system. An analytical study on the Reinforced Concrete (RC) hotel building with and without rubber bearing (RB) base isolation is carried out using the response spectrum and time history analysis methods. The results show that internal forces and inter-story drift of the building with high damping rubber bearing (HDRB) are lower than that of the fixed base with a remarkable margin. From this study, it is recommended to use the HDRB base isolation for medium and high rise buildings with soft soil in Padang City, Indonesia.
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