Virtual Reality (VR) research has been widely applied in many fields. VR promises to deliver the experience that is beyond the user's imagination. One of the advantages of VR is the feeling it gives of being there. VR can provide experiences impossible in the real world, such as flying, diving in deep water, exploring outer space, or living with dinosaurs. Despite the improvements in the software and hardware, the problem of motion sickness remains. We implement a deep learning model to train and predict motion sickness. A questionnaire is a well-known method to measure motion sickness. The weakness of the questionnaire is the measurement carried out after the user experiences motion sickness symptoms. By using the deep learning and EEG, the system will learn and classify motion sickness. The system learns the user's EEG pattern when they begin to feel the sickness symptoms. The system will be trained using deep learning to identify the sickness patterns in the future. By the EEG patterns, the system can predict the sickness symptoms before it occurs. Our model outperforms traditional models in loss values, accuracy, and F-measure metrics in Roller Coaster. With other datasets, our model also performs well. Our model can achieve 82.83% accuracy from the dataset. We also found that the time steps to predict motion sickness during 5 minute periods is a suitable configuration.

PT PAL Indonesia is one of the state-owned enterprises engaged in the shipbuilding industry which has business advantages in shipbuilding and shipbuilding capabilities. Being a fairly large company, PT PAL gets opinions from the public regarding the performance and services provided. Therefore, a sentiment analysis was carried out on public opinion on Twitter social media using data that had been collected into a dataset and processed using Rapidminer tools. This study uses the Naïve Bayes, K-NN and Decision Tree methods to make comparisons by looking at the level of accuracy of the three methods used. The results of the study show that the Naïve Bayes method has an accuracy rate of 84.08% with class precision for pred. positive is 83.65%, pred. Neutral is 97.06%, pred. negative 100%, K-NN method is 83.38% with class precision for pred. positive is 83.05%, pred. Neutral is 96.43%, pred. negative 0.0% and the Decision Tree method is 81.09% with class precision for pred. positive is 81.09%, pred. Neutral is 0.0%, pred. negative 0.0%. The results of this study can show that the Naïve Bayes method has a higher accuracy rate than other methods used with an accuracy rate of 84.08%

Nurses as one of health workers in the hospital having an important role in achieving health development goals. The success of health services depends on the participation of nurses to provide high quality care nursing for patients. This research is quantitative and using cross sectional design. The purpose of this research is to see what factors that can influence over the performance of nurses of emergency room in Bunda Thamrin Hospital Medan 2018. This research use total sampling or exhaustic sampling and The source data of this research is using questionnaire and then followed by using univariate analysis, bivariate analysis, and multivariate analysis with SPSS program. The results of this research is that work load (p=0,000),strees of work (p=0,000),competention (p=0,000), insentive (p=0,000),length of working (p=0,000) have influences for performance of nurse. After multivariate analysis the most influence variable is work load (p=0,000. The result showed that there was correlation between work load, strees of work, competention, insentive, length of working has influences against work performances of nurse. Therefore, needs toimprovenursing service that can be made to improving the performance of nurses with increased knowledge through education nursing sustainable and improving skills nursing is absolutely necessary. The arrangement of a work conducive environment so important, so that nurses can work effectively and efficiently .Creating a work that could lead to a nurse to do the best.

The object of this research was conducted at PT. Salim Jaya Medan, a company that is active as a distributor of frozen food, especially for raw materials for seafood and local and imported meat. In this company, the number of customers is decreasing from time to time. This is thought to be due to sales promotion factors, product quality and customer satisfaction. The research method used by researchers is quantitative, the type of research is descriptive. Primary and secondary data were used. The data collection technique is done by interview, questionnaire and documentation study. The data analysis used was multiple linear regressions, the coefficient of determination of simultaneous testing (F-test), and partial testing (t-test). The study population was 109 customers of PT. Salim Jaya Medan where 30 people were used as validity testing while the research sample was 86 people. The partial test results show that sales promotion, product quality and customer satisfaction have a positive effect on customer loyalty, while simultaneously it shows that sales promotion and customer satisfaction have a positive and significant effect on customer loyalty.

Coronavirus atau Covid-19 adalah virus yang ditemukan di Wuhan, China pada Desember 2019. Virus Covid-19 memiliki kemampuan penyebaran yang cukup cepat diseluruh dunia termasuk di Indonesia melalui interaksi antar manusia dan menginfeksi saluran pernapasan yang dapat menyebabkan kematian. Kasus Covid-19 yang terus meningkat membuat perlu dilakukan pemetaan tingkat kerawanan penyebaran Covid-19 khususnya di Pulau Jawa. Algoritma K-Means adalah salah satu metode clustering yang dapat membagi data ke dalam beberapa kelompok. Davies Bouldin Index (DBI) digunakan untuk menghitung kemiripan setiap cluster. Hasil pengujian menunjukkan cluster terbaik pada ukuran cluster 3 dengan nilai DBI 0.609. Terdapat 3 tingkat kerawanan, yaitu kerawanan rendah terdapat pada cluster 0 yang memiliki 105 kabupaten/kota, kerawanan sedang terdapat pada cluster 2 yang memiliki 7 kabupaten/kota, dan kerawanan tinggi terdapat pada cluster 1 yang memiliki 7 kabupaten/kota. Hasil pemetaan menunjukkan kabupaten/kota yang berada pada tingkat kerawanan tinggi berada di Kota Jakarta Utara, Kota Jakarta Barat, Kota Jakarta Pusat, Kota Jakarta Selatan, Kota Jakarta Timur, Kota Surabaya, dan Kota Semarang. Hasil dari penelitian diharapkan dapat digunakan sebagai acuan oleh masyarakat maupun pemerintah.

This study uses a classification system in managing its data. In classification there are several methods provided, one of which is the decision tree method with the C4.5 algorithm this method means a decision tree where the structure is the same as a flowchart where each node signifies an attribute test, each branch presents the test results and the leaf node represents the class or class distribution. The data used is the data of Lake Poso Tourism visitors from 2009 to 2020, then the method used in this study is divided into several stages, namely the data being studied, analyzing the data, transforming data and designing a decision tree with the C4.5 algorithm. The results achieved from this study are that the number of visitors more than 28,984 has a description of "Much" which is dominated by local tourists, while the value with the name "Less" is in foreign tourists. This is one of the important points in determining the right strategy for developing tourism in Lake Poso.

The birth rate is one of the factors increasing the rate of population growth. Birth or fertility can affect the population, getting more lower of birth rate in an area, the higher the life expectancy in that area. The number of births in Randau Jekak Village is increasing every year. The Naïve Bayes algorithm can be used to predict the future births rate because this algorithm is a simple algorithm and uses a lot of data as information in collecting data groups, and with data mining techniques it can see the predictive pattern of each variable and test. The testing data and the training data are expected to help the Village Institution or Office in Randau Jekak to suppressing the rate of population growth which increases every year. The results of this study can be concluded that the Naïve Bayes Algorithm is suitable for predicting the birth rate of babies in Randau Jekak Village with the classification technique, the birth rate in Randau Jekak Village in 2021 is High. The results of this data will be very useful for the Randau Jekak Village office in suppressing the rate of population growth in the coming year.

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