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The occurrence of earthquakes is increasing almost every year in Indonesia. From January 2014 to December 2017, there was around 16,645 earthquakes with magnitude ≥4 Richter Scale occurred. This study is the first part of earthquake risk modeling that we conducted. This study aims to analyze the relationship of frequency and magnitude of the earthquakes by using Poisson Regression and Generalized Poisson Regression. The data used in this study is frequency and magnitude data of earthquakes occurred in Indonesia. The data were selected by selecting earthquakes with magnitude ≥4 Richter Scale in the period January 2014 to December 2017 (4 years). The dependent variable is frequency, meanwhile the magnitude is independent. The frequency of earthquakes is the rounded value of natural log (Ln) transformation of cumulative frequency of earthquakes occurred in time period, and tested that it follows poisson distribution. The Poisson Regression analysis was done for the first, then the analysis continued by using Generalized Poisson Regression to observe whether there is equidispersion effect. The result of two models was compared then continued by selecting the best model based on the smallest of Akaike Information Criterion (AIC). According to the result of Poisson Regression as well as Generalized Poisson Regression, the magnitude is significantly affect the frequency. Based on AIC, the best model of frequency-magnitude relationship is presented by Poisson Regression model, μ = exp (4.048 – 0.3935x). [ABSTRACT FROM AUTHOR]

Investment in commodities has become an alternative investment that is increasingly in demand by the public in the last fifteen years. In commodity investment, there are two things that investors consider, namely return and risk. One way to calculate risk is to use Value at Risk (VaR) and Expected Shortfall (ES). The main reason of this research is to determine the value of Value at Risk (VaR) and Expected Shortfall (ES) of selected agriculture commodities which are Wheat, Cocoa and Cotton using the time series model approach. The data used in this research is the daily closing price of selected commodities from January 3, 2017 to December 31, 2020. In the time series modeling process, the models used for predicting commodities price movements are Autoregressive Integrated Moving Average (ARIMA) for the mean model, and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) for the volatility model. The values of mean and variance acquired from the model are then used to calculate the Value at Risk (VaR) and Expected Shortfall (ES) of each selected commodity. Based on the analysis, obtained that from the selected commodities, the estimated risk for selected commodities varies, where based on Value at Risk, Cotton has the lowest risk with a Value at Risk of 0.02189155, and Cocoa has the highest risk with a Value at Risk 0.02435271. However, Expected Shortfall gives a different conclusion, where Cocoa has the lowest risk with an Expected Shortfall value of 0.02435271 and Cotton has the highest risk with an Expected Shortfall value of 0.03114681. [ABSTRACT FROM AUTHOR]

This paper describes data science history and behavioral trends on the largest platform for learning and competition in analyzing and modeling data; Kaggle. We analyze the history of methods commonly used in linear predictor to predict, classify, cluster, and explore data sets. In addition, we also examine the use of the most widely used tools and frameworks to help make data modeling easier. The analysis was carried out on the forum discussion data for the last ten years based on the data available on meta-Kaggle. To see the future trend of data science and linear predictor models, we analyzed the abstracts on the articles available on the Elsevier search page. We extracted information from them using a machine learning method. [ABSTRACT FROM AUTHOR]

Social life is developing rapidly, as well as the transportation industry is also facing unprecedented challenges. Surabaya City is one of the regions that experienced this impact, which is the challenge of the rapid growth of private vehicles. Therefore, an adequate public transportation system is needed by providing a route that fits the society of Surabaya City. Observations of vehicle travel paths that pass in the city of Surabaya can be used as a preference for the route traversed by the community. These preferences can be used as a reference for the Surabaya City Government, especially the Transportation Service, as the basis for opening public transportation routes in Surabaya. By utilizing big data, the vehicle path data can be processed so that the results of the vehicle paths that are most frequently travelled by the society of Surabaya City. Big data is applied to the Hadoop ecosystem using HDFS and MapReduce as the parallel computation model. A similar computation model is used because the processed information is extensive, which will be more efficient than the sequential computing model. Due to the vast area of Surabaya City and the infrastructure to acquire the data needed, the researchers took data on a sample of 5 CCTVs points; those are Kayoon street, Panglima Sudirman, Urip Sumoharjo street, Keputran, and Raya Ngagel street. Based on the results of data processed at the 5 CCTVs points, it was found that the most frequently travelled route was the travel route from Panglima Sudirman street headed for Urip Sumoharjo street. [ABSTRACT FROM AUTHOR]

The brain tumor is a dangerous disease because it attacks vital organs and can affect anyone. Therefore, magnetic resonance imaging (MRI) is usually used for the early diagnosis of the disease. This paper discusses the application of image processing for the detection of brain tumors based on MRI images. The initial stage is preprocessing, which includes gray scaling techniques, median filters, intensity settings, and histogram equalization. The next stage is segmentation to obtain objects from brain tumors in the image using the active contour method. The segmentation results are then trained using the Convolution Neural Network (CNN) algorithm to classify images with brain tumors and images without brain tumors. In the testing process, the accuracy value is 0.8456, and the reliability value is 1.1467. This accuracy and reliability value shows that the combination of active contour segmentation techniques and CNN classification techniques can detect brain tumors well on MRI images. [ABSTRACT FROM AUTHOR]

The stocks are one of a variety of securities that are traded in general through the stock exchange. One sector that is quite large in the Indonesian capital market is banking stocks. Banking stock prices are often used by economic analysts as a reflection of the Indonesian economy, this is because banking stock prices are one of the largest sectors in the Indonesian capital market. However, since the discovery of the Covid-19 outbreak in Indonesia in March 2020, banking stock prices have fallen drastically. Since then, the movement of banking stock prices has continued to fluctuate and be uncertainty. This study will forecast banking stock prices using BBCA, BMRI, and BBRI stock price data by adding an intervention variable, namely the time the Covid-19 outbreak was discovered in Indonesia. In this study, we will compare hybrid model of the univariate time series and Artificial Neural Network known as ARIMAX-NN with hybrid model of the multivariate time series and artificial neural network as VARX-NN. The results of this study show that hybrid VARX-NN model produces a smaller RMSE value than ARIMAX-NN model in BBCA, BMRI and BBRI. [ABSTRACT FROM AUTHOR]

Corona Virus Disease 2019 (COVID-19) is a new virus that can be contagious and its worst effects can lead to death. COVID-19 first appeared in Wuhan, China until it finally spread throughout the country, one of which is Indonesia. The spread of COVID-19 cases in Indonesia itself is quite rapid until finally the World Health Organization (WHO) designates COVID-19 cases as pandemics. Based on current conditions, this paper discuss about predict positive case data of COVID-19 at five locations in East Java (Malang City, Batu City, Pasuruan Regency, Malang Regency, Pasuruan City) using a space-time model namely Generalized Space-Time Autoregressive (GSTAR). Considering that COVID-19 is very easy to spread not only depending on the time but also the proximity between locations, the GSTAR method is good enough to be used to predict the assumption of parameters between heterogeneous locations. The estimation used is OLS with the location weight of cross-correlation normalization. The results of this study obtained the GSTAR(21)-OLS model is the best model to predict the number of positive cases of COVID-19 in.five locations in East Java by weighting the normalization of cross-correlation based on the smallest RMSE value in data out sample. Forecast results for the next 10 days of positive cases of COVID-19 in.all five locations show not very significant changes. [ABSTRACT FROM AUTHOR]

This study aims to model and predict the number of passengers in the Makassar Port using the Feed Forward Neural Network (FFNN) and Seasonal ARIMAX (SARIMAX) models, called FFNN-SARIMAX. The SARIMAX model includes seasonal exogenous variables during Eid al-Fitr and calendar variation variables one month after Eid al-Fitr. The data is collected monthly from 2006 to 2019 and divided into 92% as in-sample and 8% as out-sample data. The ARIMAX modeling is conducted by modeling the dummy variable regression residuals using seasonal ARIMA. Meanwhile, these dummy variables and significant lags of the ACF/PACF plot from the regression model's residuals will be used as input in the FFNN model. We use 1–10 hidden neurons in the FFNN model. The accuracy of the forecast is calculated using Mean Absolute Percentage Error (MAPE). The forecast results show that the number of passengers in Makassar Port is best predicted using the ARIMAX (3,1,0)(1,0,1)12 followed by the FFNN-SARIMAX model with one hidden neuron and ARIMA (1,0,1)(1,0,0)12. The seasonal ARIMAX model can capture the big surge in ship passengers during and after the Eid Al-Fitr date. [ABSTRACT FROM AUTHOR]

Consider an ordered couple V and E, which V symbolized set of vertex in graph G and E symbolized set of edge in graph G, respectively, i.e G = (V, E). Furthermore, for simplicity we call G. Assume graph G has the properties: connected, undirected, finite. We have a set of vertices, symbolized by Rm and Rm ⊂ V (G). The set Rm is known as a mixed resolving set, if every vertex or every edge in G are able to be determined by one or more vertices of Rm. The mixed metric dimension, symbolized by dimm(G), i.e. the smallest amount of elements of a mixed resolving set Rm in G. In this research, we consider the mixed metric dimension of star graph Sn and it's comb operation. Assume K and L are any two graphs. The comb operation between them, symbolized by K ⊳ L, is a new one that formed by grafting the j-th imitate of L to the j-th vertex in K. Moreover, we precisely get value of mixed metric dimension of star graph Sn and it's comb operation, those are Pm ⊳ Sn and Sm⊳ Pn. [ABSTRACT FROM AUTHOR]

Artificial Neural network are commonly used for time series forecasting, especially in financial forecasting, and when external information is useful. The aim of this study is to forecast direct economic losses of Earthquake in Indonesia using NNAR and ARIMA models and selects a model that produces a forecast with a minimum root mean square error (RMSE). The accurate prediction of the direct economic losses of earthquake is critical for allowing policy makers to take a decision to anticipate losses in the future. For the purpose of forecasting accurately, an additional method is used, namely GARCH method for resolving the white noise problem in residual, such as heteroscedasticity, autocorrelation and normality. In this paper, two interpolations are applied to expand the original small sample with virtual points. The data of direct economics losses in Indonesia during the period from 1989 to 2021 were collected from EM-DAT (The International Disaster Database). The data partitioned into training and validation periods, such that the last 5 years are the validation period. The software used for ARIMA-GARCH is EViews 10 and NNAR accomplished by forecast package in R software. Based on the analysis, obtained that the result of this study is the neural network autoregression in the training period has an RMSE of 2.412, compared to ARIMA-GARCH 9,197. The neural network autoregression is significantly worse in the validation period. Its RMSE is 24,66 versus ARIMA-GARCH 8,4. According to the RMSE value, we can notice that the ARIMA-GARCH method outperform the NNAR (4,1,20) [4] model for validation period and to forecast the direct economic losses of Earthquake in Indonesia. [ABSTRACT FROM AUTHOR]

The forecasting of ferry ticket sales aims to prepare in case of a surge in passengers at the Kayangan port, East Lombok in the following years, especially before Eid al-Fitr. The purpose of this study was to determine the calendar variation model that represents the sales volume pattern of class IVA vehicles so that the model can be used to predict the number of class IVA vehicles in ticket sales and to get the forecasting results for January 2019 – December 2019. Calendar variation is a recurring pattern with varying lengths of periods due to the influence of different calendars each year. Eid al-Fitr is one of the examples of the calendar variations that occur in Indonesia because Eid al-Fitr always has a shift in the Gregorian calendar. This shift is caused by the difference between one year in the Hijri calendar dan one year in the Gregorian calendar. These periodic changes give a calendar variation. Affected data by Eid al-Fitr such as the sales volume of the ticket at the ferry port will also have a calendar variation effect. Thus, to analyze the sales volume of tickets used ARIMA method with calendar variation effect or well known as calendar variation method. Based on the analysis, it is known that the significant dummy variable is D1 which is the variable of the month of Eid Al-Fitr. Then conducted ARIMA modeling of the dummy regression residuals. The best model then obtained by analysis, which is the calendar variation model that containing ARIMA (0,1,1)(1,0,0)12, all the parameters are significant and the residual assumption is fulfilled, which are normality test and white noise test. The Mean Absolute Percentage Error (MAPE) of the calendar variation model that containing ARIMA (0,1,1)(1,0,0)12 is 12,6% which means that the forecasting results can be trusted by 87,4%. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease that has spread to countries in the world, including Indonesia. A stochastic SIR model was constructed to represent the spreading of COVID-19. One of the popular methods in a heuristic search and optimization algorithm, a genetic algorithm method, is implemented to estimate the parameters of the SIR stochastic model. As a result, using the parameters obtained, the stochastic SIR model can be in line with the actual data, and we get accurate predictions within four weeks later. [ABSTRACT FROM AUTHOR]

The determination of optimal incentives aims to improve takaful operator performance. The incentives depend on premiums paid by participants. The premium remains the property of participants, and the operators in the insurance company, manage the fund collected. In this study, the mathematical model of the takaful is formed based on a pure wakalah business model. The model is obtained using expected utility theory, where the participants are risk-neutral, and the operators are risk-averse. The optimal incentives for takaful operators are obtained based on the model with three affecting factors: additional participants, underwriting efforts, and proportion of investment fund. This study shows that this increasing number of takaful pools in the pure wakalah business model gives more incentives to the operators until they reach the optimal number of additional participants. However, increasing the operators' underwriting process effort or investment in the takaful funds will not give additional incentives to them. [ABSTRACT FROM AUTHOR]

Salinity is the level of dissolved salt in water, which is one of the factors that affect salt production, the higher the salinity dissolved in seawater, the better the resulting salt production. The factors that affect seawater salinity include air humidity, wind speed and seawater temperature. In this study, the Spline Truncated Regression method was applied to predict seawater salinity based on the variables that influence it. Data collection was obtained using satellite images obtained from Landsat 8. The data was taken within 1 year from January to December 2019. From the analysis results, it was found that the linear spline model with 1 knot point is the best model with a minimum GCV value.0.3648021178, with an R-Sq of 0.580443194, the MSE value is 0.136237833, and the MAPE is 0.98349512. Based on the MAPE value, the prediction model is said to be very accurate forecasting. [ABSTRACT FROM AUTHOR]

The economic crisis caused by the Covid-19 pandemic has made it difficult to achieve sustainable economic growth, which is one of the sustainable development goals. The great pressure on the economic sector caused most companies, including the banking sector, to experience cash flow problems. The ability to maintain a balance of cash flows during the pandemic is key for the banking sector to survive. It is very important to do a study that is able to create a model to forecast the value of cash flows in the banking sector. By having a model that is able to accurately predict the value of cash flows, the problem of cash flow difficulties can be avoided early on. In this case, the banking cash flows consist of the value of cash in to the office, the value of cash out of the office, the value of cash inflows from the e-channel, and the value of cash out of the e-channel at a bank in Indonesia. In addition, modeling is carried out by taking into account the effects of daily and holiday effects as exogenous variables. The results showed that two variables had a significant effect on the value of office cash flows. Meanwhile, the value of e-channel cash flow is only influenced by daily effects. By comparing the accuracy of the Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX) method and the Vector Autoregressive with Exogenous Variable (VARX) models, it is found that the value of cash in office, cash out of office, and cash out of e-channel provide predictive value which is more accurate with the ARIMAX model. On the other hand, the VARX model is more suitable for predicting the value of e-channel cash inflows. Thus, predictions of the value of bank cash flows can be made so that existing policies can be adjusted to support the banking sector in maintaining cash flow balance during the Covid-19 pandemic. [ABSTRACT FROM AUTHOR]

This study aims to predict the price of stock options in the future period when the underlying asset follows a Garch process and as a consideration for stock traders to make decisions to sell or buy options for a stock. GARCH Model can forecast volatility which is needed in financial applications. We select the best GARCH Model based on AIC and BIC value for the underlying stock and embedding it into the Monte Carlo scheme to derive price Asian option. We use data on ISSP to illustrate fat tail and volatility data. The result shows that the best GARCH Model for the historical data ISSP stock is ARMA-GARCH. [ABSTRACT FROM AUTHOR]

This study proposes a NARX model with a series-parallel architecture for forecasting time series data. In this study, the determination of the input variables of the NARX model uses an approach based on the length of the seasonal period from time-series data. At the same time, the number of neurons in the hidden layer uses trial and error from one neuron to the number of input variables. The case study was carried out on real data, namely data on the inflation rate in Indonesia with the exogenous variable of the interest rate of Bank Indonesia. In addition, the data were given two treatments, namely raw data (without the first differencing process) and data with the first differencing process. The best model on training data for the NARX model without the first differencing process data obtained MSE of 0.032737 and MAPE of 0.016193. While the NARX model with the first differencing process data obtained MSE of 0.004944 and MAPE of 0.006778. It can be seen that the NARX model with the first differencing process data gives better results on the training data. In addition, the comparison of MSE and MAPE was also carried out on testing data with ARIMA, ARIMAX, and NAR models. The best model was obtained with the same model in the testing data, namely the NARX model with the first differencing process data (MSE of 0.006521 and MAPE of 0.008182). Based on these results, specifically, the proposed NARX model effectively improves forecasting accuracy. Further research on the NARX model can be developed on a parallel architecture. [ABSTRACT FROM AUTHOR]

This research focused on parameter estimation and hypothesis testing of Bivariate Zero-Inflated Poisson Inverse Gaussian Regression (BZIPIGR) type II. BZIPIGR model is a mixed Poisson distribution used to model data with excess-zero and overdispersion. The parameter estimation in this study used the Maximum Likelihood Estimation (MLE) method. The first derivative of the log-likelihood function from the BZIPIGR model had obtained not closed-form. Therefore, to maximize the likelihood function, it had used numerical iteration, namely the Berndt-Hall-Hall-Hausman (BHHH). The determination of the critical area and the significance of the model parameters had used the Maximum Likelihood Ratio Test (MLRT). [ABSTRACT FROM AUTHOR]

Sharia insurance is a type of insurance where the participants make a fixed contribution to a common pool in order to protect themselves against specific financial losses under an Islamic (Sharia) principle. The mudharabah model is one of the insurer's business models under an Islamic law that is frequently used in industry to manage the participant's fund in an exchange for a certain percentage of sharing investment profits. The purpose of this paper is to discuss the mathematical model and management of premium funds in a sharia life insurance based on a modified-mudharabah scheme. In this scheme, we add a fixed proportion of underwriting surplus as an extra compensation to the operator. In this study, we sampled participants aged 25, 45, and 65 years old by using the data from the 2019 Indonesian Mortality Table. Afterwards, we compared the operator's profit with regards to participants' age, gender, and compensation. Based on the numerical simulation, we noticed that as the participants' age increased, the operator's total profit decreased. Following that, we also noted that male participant policies generated lower profit than those for female. The difference in the operator's total profit in each gender group was wider during the last years of policy contract. Furthermore, we observed that there was a constant effect on the operator's profit when the amount of death benefit changed. [ABSTRACT FROM AUTHOR]

Many industries have adopted Automatic Guided Vehicles (AGV) into production lines such as automobile factories, food processing, woodworking, and other factories. Therefore, the problem of tracking the trajectory of the AGV system needs to be solved to meet the needs of the industry. The accuracy of a system model is strongly influenced by the completeness of the state in the dynamic system. So that, an estimator is needed to meet the state requirements that cannot be measured. We derive the mathematical model of Automatic Guided Vehicle (AGV) with some assumptions, so we can obtain the non-linear AGV trajectory model. Then, we discrete the non-linear model at first before estimating it with Ensemble Kalman Filter (EnKF) algorithm. In the simulation only two states out of five are observable, so we use observations on literal velocity and yaw rate of AGV system. We use RMSE to validate the accuracy of the EnKF algorithm. The simulation results show that the non-linear AGV model that has been derived can be estimated well with the EnKF algorithm. [ABSTRACT FROM AUTHOR]

The Net Enrollment Rate (NER) is one useful indicator to see the level of success achieved by the government in efforts to improve the quality of education. The NER for SHS (Senior High School)/equivalent in Indonesia in 2015-2019 is the lowest, which is still around 60%, when compared to the NER for Elementary School/equivalent which is around 97% and the NER for JHS (Junior High School)/equivalent which is around 78%, this shows that the higher the level of education, low population participation in continuing education. NTT Province is one of the provinces where the achievement of the NER for SHS/equivalent during the 2015-2019 period is always below the NER for the national SHS/equivalent level and is always in the lowest third and fourth positions. Thus, the researcher wants to know what factors cause the low achievement of the NER for SHS/equivalent in NTT Province for the 2015-2019 period by using the panel data regression method. The use of this method is useful to determine the effect of the independent variable on the dependent variable whose model is a combination of cross section data and time series data. In this study, the best estimation model for analyzing the NER for SHS/equivalent in NTT Province is the Fixed Effect Model (FEM) individual time with variables that have a significant effect, namely the ratio of students to high school/equivalent levels and population density and the resulting goodness of the model (R2) by 87,61%. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model based on the influence of unmonitored asymptomatic individuals, vaccinations and individuals returning home to the spread of COVID 19. The concept used is that individual populations moves in 3 regions with each region having 1 interface or 1 connecting route. Individual movement is expressed by a weight function which in modeling use the Kernel density function in the normal group. The mathematical model obtained is in the form of a System of Integro-Partial Differential Equations consisting of 3 regional sub-models and an entire regional system model. Leipzig constant analysis was carried out in order to obtain model validation that was suitable for the phenomenon that occurred. [ABSTRACT FROM AUTHOR]

In this paper, the spread of the Covid-19 in.Indonesia is described by the SIRD epidemiological mathematical model. The mathematical model used in this paper is Susceptible, Infected, Recovered, Death (SIRD). The modified extended Kalman filter algorithm is applied to predict the spread of Covid-19 in.the future. We modified the algorithm by generating real data based on the previous estimation results. The real data generated from the generation is used at the correction stage to obtain prediction results in a fairly long period. Simulations were carried out with three types of mobility restrictions, namely mobility 100%, mobility 75%, and mobility 50%. Based on the simulation results, it can be concluded that mobility restrictions in Indonesia, which starts on September 4, 2020, can reduce the number of infected and death individuals and can increase the number of individuals who recover from Covid-19. [ABSTRACT FROM AUTHOR]

This paper presents a disturbance observer model predictive control (DO-MPC) for the UAV pitch angle control with the presence of disturbances. The linear disturbance observer is designed to estimate the unknown disturbances in a system. Then, the MPC optimization problem is formulated into quadratic programming (QP) by adding the disturbance estimations into the system prediction subject to the input constraints. Furthermore, the algorithm of DO-MPC scheme for UAV pitch angle control is presented. According to the simulations, the DO method can estimate the actual disturbances in terms of step and sinusoidal disturbances well. The simulation results show that with the same controller parameters, the DO-MPC has the better performance compared to MPC in handling the disturbances. [ABSTRACT FROM AUTHOR]

Portfolio is the set of assets either real or financial owned by investor. One asset used by investor is stock because the stock has various price along the period time. Nowadays, stock investments have been done by investors. Stock price can be either profit or loss so that it is required portfolio selection. From the various price of stock, we can compute the return, expected return, and the risk of stock. In this research, we will make the optimal portfolio which can result maximum return and minimum risk of stock with the available investation. Goal programming is the mathematical programming techniques to solve the objectives subject to some constraints where the there are priorities for each objective. An application of goal programming is portfolio selection. Portfolio selection is determining some stocks optimizing maximum return and minimum risk of stock with the available investation. The novelty of this research is Goal Programming as portfolio selection method will be optimized by Particle Swarm Optimization (PSO) so that it is called Particle Swarm Optimization-Goal Programming (PSOGP). In PSO, there is initialization of particle i.e. proportion of investation on each stock as decision variable. Particle must be constructed so that it can satisfy the constraint investation of all stocks. In optimization process, the new particle is also modified so that satisfying the constraints. In multiobjective optimization, sort the fitness on the value of the first priority objective. If first fitness have the same value of the objective, then sort them on the second priority objective, and so forth. Based on simulation, PSOGP can be applied on the portfolio selection and can optimize priority of some objectives. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic had an impact on the world of education and it leads to the cancellation of all educational activities. An online learning system was an educational system or concept that utilizes information technology in the teaching and learning process. The basic principles in the online learning process are clarity of messages, learning strategies, interactivity, growth of motivation and creativity, and the use of media for effective communication. The purpose of this study was to determine what factors are hindering students in online lectures by using Principal Component Analysis. The research was conducted using a survey method, namely by filling in forms for undergraduate students at the University of Mataram. The method used to analyze the data was a quantitative descriptive technique which was expressed in the distribution of scores and percentages. This form contains 15 observed variables, after factor analysis was carried out, and obtained 3 factors that most hamper online lectures. The dominant factor is Factor 1 that can explain 28.957% of the variation. The variables included in Factor 1 are the lack of concentration, material understanding, not direct discussion, unconcern (boredom), and lack of study companion variables. The results obtained can be used as a consideration to maximize online lectures during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The mathematical formulation of heat conduction problem along the rod in steady state leads to differential equation namely Poisson equation. The Dirichlet and Neumann boundary conditions are known. In this paper, we study Ritz method as construction of approximation solution based on its extreme formulation. This method applied to Poisson equation with Dirichlet and Neumann boundary conditions by choosing finite basis functions to find approximation solution. From the numerical experiment we obtain good approximation solution. [ABSTRACT FROM AUTHOR]

Isolation and fumigation play an essential role in the mechanism of the spread of fowl pox disease. This paper establishes a mathematical model to describe the spread of fowl pox in chicken farms by considering two interventions; isolation intervention to reduce the spread of feathers containing the virus and fumigation intervention to kill fowl pox vectors of adult mosquitoes and their larvae. The model was constructed as a ten-dimensional non-linear ordinary differential equation. Furthermore, analytical and numerical studies were carried out on the constructed model to determine the existence and analyze the disease-free equilibrium point, endemic balance point, basic reproduction number (ℛ0), and understand the long-term and shortterm dynamics of the constructed model. Based on the carried out analytical and numerical studies, it was concluded that although fumigation and isolation were proven to minimize fowl pox disease, fumigation was more reliable for maximum results. [ABSTRACT FROM AUTHOR]

Generalized Poisson regression is a common model used for over/underdispersion count data. However, excess zeros in the observed data can create difficulties for this model. The zero-inflation regression model is a model that can be used to handle excess zeros on data. If the observational data is over/underdispersion and excess zero, then the Zero-Inflated Generalized Poisson regression (ZIGPR) is the proper model to use. This study aims to obtain the estimator of the BZIGP regression through the EM algorithm and to model data on stillbirths and maternal deaths in 91 sub-districts in Pekalongan Residency, Central Java. Because there are two response variables, the regression analysis used is Bivariate ZIGPR (BZIGPR). The BZIGPR discussed in this study is BZIGPR type II, where the two BZIGP type II marginal distributions have their parameter of zero inflation to be applied in a broader range. BZIGPR parameter estimation is done using the EM algorithm, while the hypothesis testing of the BZIGPR model is derived using the Maximum Likelihood Ratio Test (MLRT). The model generated in the BZIGPR consists of two parts, the log model and the logit model. The results showed an overdispersion and an underdispersion in maternal deaths and stillbirths data, respectively. Based on empirical studies, all predictor variables affect the two response variables significantly. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused economic, social and political crises in the infected countries. These countries implemented a lockdown policy to anticipate the spread, resulting in decreased financial revenues, including tax revenues. It is not easy to achieve tax revenue during a pandemic because almost all sectors have declined. Spatial interactions influence tax revenue, so a spatial model must be applied in its analysis. One of the spatial models is Spatial Durbin Model (SDM) is the development of the spatial autoregressive model (SAR). In the SDM model, the dependent variable and the independent variable both contain spatial effects. Spillover is the tendency of a changes in one area are following changes in another areas. This study's main objective is to estimate spillover's effect on districts/cities tax revenue and its spread in East Java. Researchers found a positive spatial spillover of GRDP, inflation, number of industries, and total population on tax revenue in East Java. [ABSTRACT FROM AUTHOR]

Inventory control problem becomes one of the most crucial parts in a company. Basically, the problem is to determine the optimal time and quantity of replenishment. But, there are many factors affected the decision such as demand uncertainty, lifetime of the product, deterioration, unit sales price and discount offered by the supplier. In this paper, we consider a mathematical model for inventory control problem. We consider a multi-item problem with inventory and price-dependent demand and all-units discount offered by the supplier. In dealing with multi-item problem, there are two replenishment policies to consider, individual order and joint order policy. Numerical examples are given with different inventory and price-dependent demand functions for each item and compare the individual and joint order policy. We found that in our numerical example, joint replenishment policy gives lower total inventory cost compared to the individual order policy. [ABSTRACT FROM AUTHOR]

Good and properly organized inventory management is essentially needed for a company in order to ensure that its production process runs smoothly. There are many factors to be considered since they can distract the inventory management such as demand behavior, the life time of goods, uncertain replenishment time, the political and economic situation, and unexpected natural disaster. There are certain risks involved with bulk replenishment such as the higher holding cost and the possibility of damage to the stored goods. On the other hand, if we order too little, the risks lie in the possibility that the production process will stop, resulting in lost sales due to the unavailability of goods. One strategy to overcome this unavailability of goods is backorder, where the customers will wait until the next replenishment to obtain their goods. This paper develops a mathematical model for inventory-dependent demand with the backorder policy applied. The decisions variables in our model are the optimal order quantity and the number of backorders, which minimize the total inventory cost. We also performed a sensitivity analysis of the optimal solution and found that the decreasing rate of the inventory is affected by the autonomous demand (α) and the increasing demand factor (β). Inventory will deplete faster as α and β increase. The number of backorders is affected by the high demand when inventory reaches zero and backorders entail a certain cost. When demand is higher and the backorder cost is relatively cheap, the number of backorders becomes higher. [ABSTRACT FROM AUTHOR]

The subprime mortgage crisis was a multinational financial crisis that occurred in 2008. The subprime mortgage is a program by the United States government to provide housing loans for borrowers with unreliable credit histories. The impact of the crisis highly influences the development of the financial market around the world, including Indonesia. The main focus of the research is to construct a simple mathematical model for mortgage financing, especially housing, which is based on Islamic laws. Rarely did previous studies discussed this matter, especially in Indonesia. The construction of the model depends on the contract (aqad) made. The contracts that will be discussed in this study are the murabahah and musharakah mutanaqisah (MMQ) contracts. In the MMQ contract, the ujroh (rental cost) is going to be determined by depreciation using the straight-line method. Based on simulation using the example of a real contract, the obtained rates of return achieved by the simulation with simple murabahah model is 17.84%, murabahah with an annuity of an installment is 11.4%, Islamic bank housing mortgage simulation application 12.19%, and MMQ is 6.87%. This result shows that the calculation involved in the contract should be observed carefully. [ABSTRACT FROM AUTHOR]

Crane is a tool used to transport heavy payload or hazardous materials from one place to another in an industry. Crane movement is susceptible to excessive swing angle of payload, which can affect positioning accuracy, quality, effectiveness, and safety of operations. Problems in crane systems involve the ability to reduce swing angle of payload and move it to the desired position fastly. In this paper, an LQG control design is implemented on a single payload overhead crane system to control trolley position and swing angle. The simulation results show that LQR produces system response better than LQG for swing angle amplitude. Although the simulation results show the performance of LQR better than LQG but in implementation, LQG is wider and cheaper than LQR because it can be applied to systems with only a few state measurements so that it can reduce the needed for measurement sensors that must be installed on the system, in contrast to LQR which must need full state measurements. To get full state measurement requires many sensors because each state must be installed with a measurement sensor so that the cost will be more expensive and also constrained by the limitations of some real systems that not all states are possible to measure. The system disturbance represents the amount of wind energy that affects the overhead crane system when operates. The selection value of system disturbance 10−3 causes LQG to give satisfactory results, where the selection of value is based on the condition that the overhead crane system generally operates indoors so that the amount of wind energy that affects the system is small. [ABSTRACT FROM AUTHOR]