This study aims to analyze and understand about analyzing the knowledge sharing strategy carried out by SMA Nurul Jadid, Paiton, Probolinggo in improving the quality of its human resources. This research uses a qualitative approach to the type of case study. Data collection techniques are carried out through interviews, observation, and documentation. Data analysis was carried out through display data, data reduction and conclusion drawing. The results showed that the strategy to improve the quality of human resources through knowledge sharing at SMA Nurul Jadid was carried out through; utilize materials, talk space, knowledge sharing culture, benchmarking best practices.

The purpose of this study was to determine the leadership of women in creating child-friendly schools in RA Nurus Salam, Sambirampak Kidul, Kotaanyar, Probolinggo. The type of research used is quantitative research with a case study observation approach. The results showed, first, the leadership in RA Nurus Salam was a woman who wanted to create a child-friendly school. Second, the implementation of childfriendly schools has been implemented in RA Nurus Salam with the following stages; first, Word Analysis. Second, the Branding School. Third, Implementation of Sra Development. Fourth, Continuous Improvement. With the implementation of child-friendly schools in RA Nurus, the learning greetings are more conducive and the trust of parents to teachers is getting higher.

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Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN.  The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

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The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. In this paper several methods of supervised learning (decision tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain the best method that can be used for gene analysis. In this study there is no optimization and tuning of the algorithm to assess the fitness of algorithms. There are 1881 features of microRNA gene expresion, 22 cancer classes based on tissue location. A simple feature selection method is used to test the comparison of the algorithm. Expreriments were conducted with various scenarios to asses the accuracy of the classification. Keywords: Cancer, MicroRNA, classification, Decision Tree, Naïve Bayes, Neural Network, Deep Learning AbstrakData ekpresi gen sel kanker secara umum memiliki feature yang sangat banyak dan memerlukan analisa untuk mengetahui gen apa yang sangat berpengaruh terhadap spesifik penyakit untuk diagnosis dan juga penemuan obat. Pada tulisan ini beberapa metode supervised learning (decisien tree, naïve bayes, neural network, dan deep learning) digunakan untuk mengklasifikasi sel kanker berdasarkan ekpresi gen microRNA untuk mendapatkan metode terbaik yang dapat digunakan untuk analsisa gen. Dalam studi ini tidak ada optimasi dan tuning dari algoritma untuk menguji kemampuan algortima secara umum. Terdapat 1881 feature epresi gen microRNA pada 25 kelas kanker berdarkan lokasi tissue. Metode sederhana feature selection digunakan juga untuk menguji perbandingan algoritma tersebut. Exprerimen dilakukan dengan berbagai sekenario untuk menguji akurasi dari klasifikai.

User reviews are important in the new approach to fintech services. To learn this information, a simple sentiment analysis can make the right observations to support the ... View more Metadata

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