Data mining dengan peran asosiasi sudah banyak digunakan oleh dunia usaha, salah satu algoritma yang sering digunakan untuk aturan asosiasi adalah apriori. Namun apriori memiliki kelemahan dalam hal performa, karena pada setiap penentuan frequent k-itemset harus melakukan scan database. Hal ini akan menjadi masalah apabila kandidat k-itemset memiliki dimensi yang banyak. proses scan database yang besar akan memakan waktu yang lama dan berpengaruh pada penggunaan memori dan prosesor. Apriori sudah sering dikembangkan, salah satu yang populer adalah Frequent Pattern (fp-growth), apriori dan fp-growth sama-sama merupakan algoritma untuk aturan asosiasi, hanya saja fp-growth menggunakan pendekatan yang berbeda dengan apriori yakni menggunakan pendekatan Frequent Pattern Tree (fp-tree). Meski fp-growth memiiki performa yang bagus ketika scan database namun rules yang di hasilkan oleh fp-growth tidak sebaik yang di hasilkan oleh apriori. Alternatif lain yang bisa digunakan adalah metode hashing, hal ini bisa menjadi solusi untuk mengatasi masalah dalam proses pencarian dan penentuan frequent k-itemset, sehingga proses scan database bisa lebih cepat. Tujuan penelitian adalah memperbaiki kinerja apriori dalam proses pencarian frekuensi itemset sehingga waktu scan database bisa lebih cepat

Patient data is one of the datasets managed by the hospital. Patient data in the form of examination results and other data is important data that is private and confidential. Therefore, patient data needs to be secured so that there is no misuse of data by parties who are not responsible for things that can harm the data owner. One of the several methods that can be used to secure data is cryptography. Cryptography itself has several algorithms, one of which is blowfish. This study applies the blowfish algorithm to secure patient data in the database to reduce the possibility of data misuse by irresponsible parties. This study succeeded in implementing the blowfish algorithm for securing patient data. The data stored in the database is the result of encryption using the blowfish algorithm, the results of which are difficult to understand because there is a combination of symbols and text. The application of the algorithm affects the data storage time in the database, which originally took 0.12 seconds to save data and now takes 0.28 seconds to store data.

West Nusa Tenggara is one of the tourist attractions in Indonesia which has a certain attraction for tourists. With the increase in tourism in NTB, it is necessary to make adequate efforts to maintain tourist objects and attractions. In an effort to maintain a tourist attraction, the NTB provincial tourism office needs to analyze and predict the arrival of local and international tourists. The current analysis and prediction process is still being carried out by collecting data from each tourist attraction entrance. The processed data produces predictions of tourist arrivals, both local and international, where the data processing process takes a long time and requires high human resources. To overcome these problems, it is done by applying computational predictions. Computational predictions can minimize the prediction time and human resources required. The method used is a neural network algorithm with optimized parameters using a genetic algorithm. The optimized parameters are the hidden layer, the number of neurons in the input layer, momentum and others. The data used is time series data from 1997 to 2018. From the neural network experiment, the parameters of the number of neurons in the input layer xt-7 are determined, the number of neurons in the hidden layer 10, the training cycle value is 400, the learning rate value is 0.3 and the momentum value is 0.2. From the experiment, the RMSE value of 0.050 was obtained. While the RMSE value for the neural network algorithm parameters optimized using the genetic algorithm is 0.044. Because of this, it can be stated genetic algorithm with neural network can be used to determine the hidden layer and the number of hidden nodes, the right features, momentum, initialize, and optimize the weight of the neural network. So that the application of the genetic algorithm to optimize the parameter values of the neural network algorithm is better than the application of the neural network algorithm without optimization.