Probability distributions of COVID-19 tweet posted trends uses a nonhomogeneous Poisson process

The influence of social media in disseminating information, especially during the COVID-19 pandemic, can be observed with time interval, so that the probability of number of tweets discussed by netizens on social media can be observed. The nonhomogeneous Poisson process (NHPP) is a Poisson process with dependent on time parameters and the exponential distribution having unequal parameter values and, independently of each other. The probability of no accurence an event in the initial state is one and the probability of an event in initial state is zero. Using of non-homogeneous Poisson in this paper aims to predict and count the number of tweet posts with the keyword coronavirus, COVID-19 with set time intervals every day. Posting of tweets from one time each day to the next do not affect each other and the number of tweets is not the same. The dataset used in this study is crawling of COVID-19 tweets three times a day with duration of 20 minutes each crawled for 13 days or 39 time intervals. Result of this study obtained predictions and calculated for the probability of the number of tweets for the tendency of netizens to post on the situation of the COVID-19 pandemic

Inventory Control for MSME Products Using the Q Model with Lost Sales Condition Based on Products Sales Forecasting

Micro, Small and Medium Enterprises (MSMEs) have an important role in economic development in order to achieve thq quality of economic growth. Intense competition among MSMEs requires MSMEs to have a good inventory control that can help them minimize costs and maximize profits. One of the MSMEs that often experiences problems in inventory control is Sabun Bening Official. To solve the inventory problems in Sabun Bening Official, Holt-Winter Exponential Additive forecasting method is used as a guide to predict future product demand because product demand graph is seasonal and has trend pattern. After getting the value of product demand forecast, inventory control calucaltions are carried out using the Q Model probabilistic inventory method with lost sales condition. The uncertain and fluctuating demand causing the inventory system in Sabun Bening Official is probabilistic and the company will lose sales if it does not able to fulfill customer demands. Based on the research results, product forecasting for the coming period and inventory control policies which include the optimal number of product order, safety stock, reorder point, and product inventory costs can be obtained

Basic Programming Training in Python for Junior High School Students at Al Fitrah Islamic Boarding School

This research discusses the implementation of basic programming training using the Python programming language for junior high school (SMP) students at Al Fitrah Islamic Boarding School. The aim of this study is to provide an overview of the training experience and its impact on students' understanding of programming and their proficiency in using Python. The research method used is qualitative with a case study approach. The participants of the study consist of students from the seventh and eighth grades at Al Fitrah Islamic Boarding School. Data was collected through observations, interviews, and tests of students' learning outcomes. The results of the research show that the basic programming training with Python effectively improves students' understanding of programming concepts and develops their computational skills. Additionally, the students demonstrate high interest and enthusiasm in programming activities, indicating strong potential for developing technological skills in the future. This research concludes that basic programming training with Python can be well integrated into the junior high school curriculum to enhance digital literacy and prepare students to face the challenges of an increasingly advanced technological world

Optimizing Stock Portfolio with Markowitz Method as a Reference for Investment Community Decisions

An investor who wants to invest by avoiding risk makes investors tend to choose investments with the same expected return and the smallest or lowest possible risk. Therefore, investors expect to be able to maximize profits and minimize risk at the same time in investing. In a stock portfolio, it can be done by investing the funds owned by investors into several stocks so that it can reduce the risk of losses that will occur simultaneously. In choosing the right company to invest in with consideration of expected return and risk, a multi-objective optimization with multivariate objects can be used so that it can meet the expectations of investors. The portfolio concept introduced by Markowitz is a portfolio optimization intended for standard investors because it only refers to one explanation of portfolio returns. The Markowitz method can produce an optimal stock portfolio by considering the expected return and risk simultaneously so that the maximum profit can be obtained without eliminating the existing risk

The Study of Value-At-Risk Calculation and Back-testing Using the ARMA-GARCH Model Based on Stock Returns: An Overview

Stocks are investment instruments that provide returns but tend to be risky. The most important component of investing is volatility, where volatility is identical to the standard conditional deviation of stock price return. The important thing in investing in addition to return is a risk. Value-at-Risk (VaR) is a statistical method of estimating maximum losses. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. To evaluate the quality of VaR estimates, models should always be back-tested with appropriate methods. Back-testing is a statistical procedure in which actual gains and losses are systematically compared to appropriate VaR estimates. The goal of the study was to estimate the Autoregressive Moving Average-Generalized Conditional Heteroscedastic (ARMA-GARCH) model to determine Value-at-Risk and back-testing. ARMA is a combination of AR and MA models, while GARCH is a time series model with symmetrical properties. The method in this study is systematic browsing of libraries. Systematic library tracing is an attempt to identify, evaluate, and interpret all research relevant to a particular phenomenon. 

Measuring the Water Surface Speed of the Cikidang River as a Supporting Facility for the Development of Ecotourism Areas

Pangandaran Regency is famous for its natural beauty, especially the beauty of its beaches. In this district there are many famous beaches. Besides the beach, there are also rivers that offer beautiful views. However, not many tourists have made the rivers in Pangandaran as tourist destinations, especially the Cikidang River. Cikidang River has the potential to be used as ecotourism. In building an ecotourism area on the banks of a river, it is necessary to carry out an in-depth analysis of the damage that might occur due to erosion. One of the factors that can increase erosion is the speed of water on the surface. Therefore, in this paper, measurement of river surface velocity is carried out in a simple way and analyzed using the velocity formula. From the results of this measurement, a recommendation was made to direct all parties involved in measuring the surface velocity of the Cikidang River as a means of supporting the development of ecotourism areas

Systematic Literature Review on Troubleshooting Delivery of Production Product Using n-Vehicle with Vogel Total Difference Approach Method

The product delivery strategy using n-vehicle is the application of optimization for transportation problems. The product delivery strategy using n-vehicle is useful for minimizing the shipping costs of a company’s production. This article presents a peer-reviewed bibliometric analysis based on the topic of production delivery strategies using n-vehicle. Overall, there are 91 articles from the Dimension, Science Direct, and Google Scholar databases in 2013-2021 that use the topic of production delivery strategies using n-vehicle based on the keywords ”Capacitated transportation problem” and ”cost” and ”vehicle” and” optimal solutions”. The researcher presents the relationship of each cited article so that it can show the collaboration of all the cited articles. This article aims to generate and review analysis results through Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) and State of The Art. Bibliometric analysis, PRISMA, and State of The Art show how the development of research on production delivery strategies using n-vehicle. So, it can produce suggestions in conducting the latest research related to studies on the topic of production delivery strategies using n-vehicle. Based on PRISMA’s analysis, 91 articles were obtained, of those 91 articles, 11 articles discussed the strategy of delivering production products using n-vehicle in depth. The State of The Art also shows how the development of research on production delivery strategies using n-vehicle is developing. It can be seen that apart from the classical method, other methods are also emerging to solve transportation problems. One of them is Vogel Total Difference Approach Method (VTDM)

Mathematical Model of Paddy Production using Cobb Douglas Method Based On Weather Factors

This research was conducted to model paddy production based on weather factors. This needs to be done to predict crop yields and regulate paddy cropping patterns. In setting the cropping pattern, the weather is selected which consists of temperature, wind speed, and rainfall, as a variable factor of production. Meanwhile, other factors (such as fertilization, sunshine, air humidity, etc.) are assumed to be in catteries paribus conditions. The research method used is a mixed method between qualitative methods which are descriptive details and quantitative methods which are based on weather data and Paddy's harvest data. The aim of this research is to analyze the influence of weather on paddy production results. Analysis is done to get the production function. Parameters are estimated using the Ordinary Least Square (OLS) method by minimizing the sum of squared errors. Based on data analysis, a correlation of 0.899 was obtained with a standard error of .051665515. the results of model testing also show significant results with the F statistic obtained at 33.98 with a p-value of 0.028 which is less than 5%. So it can be concluded that there is a significant relationship between weather and paddy productivity. In such a way that the weather can be used as a reference in determining the prediction of loss risk and paddy production. This model can also be recommended for further research, namely to determine insurance losses that may arise when extreme weather events occur.