Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Genetic Algorithm for Variable Selection and Parameter Optimization in SVM and Fuzzy SVM for Colon Cancer Microarray Classification

Colon cancer is the second leading cause of cancer-related deaths in the world, hence research on that topic needs to be undertaken with improvement. Recent advanced in microarray technology allows the monitoring of the expression level of a large set of genes simultaneously. Microarray data is a type of high-dimensional data with hundreds or even thousands number of genes (features), while usually the number of patients observed (observations) is much smaller than the number of features. This study uses a colon cancer microarray dataset contains two class of genes, normal and tumor. The aims of this study is to develop a classification model using fuzzy support vector machines (FSVM) hybridized with genetic algorithm (GA) for classifying individuals based on gene expression. Fuzzy memberships was used in SVM in order to deal with the case of imbalanced microarray data. Meanwhile, the role of genetic algorithm is, firstly, to select the relevant genes as the features and, secondly, to optimize the parameter of FSVM as GA is able to handle the problem of nonlinear optimization that has a high dimension, adaptable, and easily combined with other methods. The classification using FCBF selection has a higher accuracy value than the ones without the selection. The results also show that FSVM that has been optimized using GA has the highest accuracy value compared to other classification methods used in this study.

A Study of Count Regression Models for Mortality Rate

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

The Confidence Interval of the Estimator of the Periodic Intensity Function in the Presence of Power Function Trend on the Nonhomogeneous Poisson Process

The nonhomogeneous Poisson process is one of the most widely applied stochastic processes. In this article, we provide a confidence interval of the intensity estimator in the presence of a periodic multiplied by trend power function. This estimator's confidence interval is an application of the formulation of the estimator asymptotic distribution that has been given in previous studies. In addition, constructive proof of the convergent in probability has been provided for all power functions.

On Rainbow Vertex Antimagic Coloring of Graphs: A New Notion

All graph in this paper are simple, finite, and connected. Let be a labeling of a graph . The function is called antimagic rainbow edge labeling if for any two vertices and , all internal vertices in path have different weight, where the weight of vertex is the sum of its incident edges label. The vertex weight denoted by for every . If G has a antimagic rainbow edge labeling, then is a antimagic rainbow vertex connection, where the every vertex is assigned with the color . The antimagic rainbow vertex connection number of , denoted by , is the minimum colors taken over all rainbow vertex connection induced by antimagic rainbow edge labeling of . In this paper, we determined the exact value of the antimagic rainbow vertex connection number of path ( ), wheel ( ), friendship ( ), and fan ( ).

Modeling Length of Hospital Stay for Patients With COVID-19 in West Sumatra Using Quantile Regression Approach

This study aims to construct the model for the length of hospital stay for patients with COVID-19 using quantile regression and Bayesian quantile approaches. The quantile regression models the relationship at any point of the conditional distribution of the dependent variable on several independent variables. The Bayesian quantile regression combines the concept of quantile analysis into the Bayesian approach. In the Bayesian approach, the Asymmetric Laplace Distribution (ALD) distribution is used to form the likelihood function as the basis for formulating the posterior distribution. All 688 patients with COVID-19 treated in M. Djamil Hospital and Universitas Andalas Hospital in Padang City between March-July 2020 were used in this study. This study found that the Bayesian quantile regression method results in a smaller 95% confidence interval and higher value than the quantile regression method. It is concluded that the Bayesian quantile regression method tends to yield a better model than the quantile method. Based on the Bayesian quantile regression method, it investigates that the length of hospital stay for patients with COVID-19 in West Sumatra is significantly influenced by Age, Diagnoses status, and Discharge status.

The Ring Homomorphisms of Matrix Rings over Skew Generalized Power Series Rings

Let M_n (R_1 [[S_1,≤_1,ω_1]]) and M_n (R_2 [[S_2,≤_2,ω_2]]) be a matrix rings over skew generalized power series rings, where R_1,R_2 are commutative rings with an identity element, (S_1,≤_1 ),(S_2,≤_2 ) are strictly ordered monoids, ω_1:S_1→End(R_1 ),〖 ω〗_2:S_2→End(R_2 ) are monoid homomorphisms. In this research, a mapping τ from M_n (R_1 [[S_1,≤_1,ω_1]]) to M_n (R_2 [[S_2,≤_2,ω_2]]) is defined by using a strictly ordered monoid homomorphism δ:(S_1,≤_1 )→(S_2,≤_2 ), and ring homomorphisms μ:R_1→R_2 and σ:R_1 [[S_1,≤_1,ω_1]]→R_2 [[S_2,≤_2,ω_2]]. Furthermore, it is proved that τ is a ring homomorphism, and also the sufficient conditions for τ to be a monomorphism, epimorphism, and isomorphism are given.

Inclusion Properties of The Homogeneous Herz-Morrey Spaces With Variable Exponent

In this paper, we have discussed about the inclusion properties of the homogeneous Herz-Morrey spaces with variable exponent and the weak homogeneous spaces with variable exponent. We also studied the inclusion relation between those spaces.

Analysis of Landing Airplane Queue Systems at Juanda International Airport Surabaya

Juanda International Airport is currently preparing to realize the construction of terminal 3. This construction project impression that Juanda Airport is experiencing an overload, including in the airplane queue. This study aims to analyze the current queuing system at the Juanda International Airport apron, whether effective, quite effective, or less effective in serving the number of existing flights with two terminals. An analysis of the queuing system was conducted in several scenarios. They are in normal/regular condition, a scenario if there is an increase in flight frequency, and a scenario if there is a reduction in aprons’ number because of certain exceptional situations. To analyze the airplane’s landing queue at Juanda airport apron, the queuing model (M/M/51) : (FCFS/∞/∞) is used. From this model, the results show that in normal conditions, the estimated waiting time for each airplane in the system is 0.18 hours with a queue of 2 up to 3 planes/hour, categorized as effective. In one apron reduction scenario, each airplane’s estimated waiting time in the system is 0.7 hours, with a queue of 6 up to 7 planes categorized as less effective. In the scenario of additional flights, only 9 other flights are allowed every day to keep the service performance still quite effective. By obtaining this results analysis, the decision of PT. Angkasa Pura 1 (Persero) to build terminal 3 is suitable to reduce queuing time and improve Juanda International Airport services to be more effective.

Supplier Selection Analysis Using Minmax Multi Choice Goal Programming Model

Production control, inventory and distribution is an important factor in trading activities. These three factors are discussed in a system called Supply Chain Management (SCM). Procurement of goods from a company or trading business related to suppliers. In some cases, there are several supplier that can be assessed by considering certain factors. In certain cases, the data from several factors that are considered are uncertainty, so the fuzzy approach can be used. The MINMAX Multi Choice Goal Programming model can be used to solve fuzzy supplier selection problems with linear membership function. It can be applied to selecting supplier of Brastagi Oranges. There are four suppliers, namely Jaya, Mako, Baros. Gina. There are three factor to consider, cost, quality and delivery. The decision maker select the best supplier for ordering 17000 kg Brastagi oranges. The results, the best supplier is Gina with an order quantity of 10000 kg and Mako with a total order of 7000 kg