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The steganography process combines mathematics and computer science. Steganography consists of a set of methods and techniques to embed the data into another media so that the contents are unreadable to anyone who does not have the authority to read these data. The main objective of the use of base64 method is to convert any file in order to achieve privacy. This paper discusses a steganography and encoding method using base64, which is a set of encoding schemes that convert the same binary data to the form of a series of ASCII code. Also, the EoF technique is used to embed encoding text performed by Base64. As an example, for the mechanisms a file is used to represent the texts, and by using the two methods together will increase the security level for protecting the data, this research aims to secure many types of files in a particular media with a good security and not to damage the stored files and coverage media that used.

The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine the most effective model for predicting data. The stock closing price of BRIC country data was obtained from Yahoo Finance. That data dates from 01 November 2019 to 11 December 2020, then divided into two categories--training data and test data. Training data covers 01 November 2019 to 02 December 2020. Seven days (03 December 2020 to 11 December 2020) of data was tested to determine the accuracy of the models using training data as a reference. To measure the accuracy of the models, we obtained the means absolute percentage error (MAPE) and mean square error (MSE). Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price (BVSP) while MAPE (0.50) and MSE (579272.65) with Holt-Winters (smaller than ARIMA and SutteARIMA), model SutteARIMA was found most appropriate to predict the stock prices of Russia (IMOEX.ME), India (BSESN), and China (SSE) when compared to ARIMA and Holt-Winters. MAPE and MSE with SutteARIMA: Russia (MAPE:0.7; MSE:940.20), India (MAPE:0.90; MSE:207271.16), and China (MAPE: 0.72; MSE: 786.28). Finally, Holt-Winters predicted the daily forecast values for the Brazil stock price (BVSP) (12 December to 14 December 2020 i.e., 115757.6, 116150.9 and 116544.1), while SutteARIMA predicted the daily forecast values of Russia stock prices (IMOEX.ME) (12 December to 14 December 2020 i.e., 3238.06, 3241.54 and 3245.01), India stock price (BSESN) (12 December to 14 December 2020 i.e.,. 45709.38, 45828.71 and 45948.05), and China stock price (SSE) (11 December to 13 December 2020 i.e., 3397.56, 3390.59 and 3383.61) for the three time periods.

Searching is a common process performed by many computer users, Raita algorithm is one algorithm that can be used to match and find information in accordance with the patterns entered. Raita algorithm applied to the file search application using java programming language and the results obtained from the testing process of the file search quickly and with accurate results and support many data types.

This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data in Indonesia. It undertakes a comparison of the most popular and widely used four forecasting methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, and SutteARIMA. The data used were obtained from the website of the World Bank. The data consisted of the annual infant mortality rate (per 1000 live births) from 1991 to 2019. To determine a suitable and best method for predicting Infant Mortality rate, the forecasting results of these four methods were compared based on the mean absolute percentage error (MAPE) and mean squared error (MSE). The results of the study showed that the accuracy level of SutteARIMA method (MAPE: 0.83% and MSE: 0.046) in predicting Infant Mortality rate in Indonesia was smaller than the other three forecasting methods, specifically the ARIMA (0.2.2) with a MAPE of 1.21% and a MSE of 0.146; the NNAR with a MAPE of 7.95% and a MSE of 3.90; and the Holt-Winters with a MAPE of 1.03% and a MSE: of 0.083.

This study was conducted with the aim to the clustering of provinces in Indonesia of the risk of the COVID-19 pandemic based on coronavirus disease 2019 (COVID-19) data. This clustering was based on the data obtained from the Indonesian COVID-19 Task Force (SATGAS COVID-19) on 19 April 2020. Provinces in Indonesia were grouped based on the data of confirmed, death, and recovered cases of COVID-19. This was performed using the K-Means Clustering method. Clustering generated 3 provincial groups. The results of the provincial clustering are expected to provide input to the government in making policies related to restrictions on community activities or other policies in overcoming the spread of COVID-19. Provincial Clustering based on the COVID-19 cases in Indonesia is an attempt to determine the closeness or similarity of a province based on confirmed, recovered, and death cases. Based on the results of this study, there are 3 clusters of provinces.

In developing countries, solar energy is the largest source of energy, accounting for 35%–45% of the total energy supply. This energy resource plays a vital role in meeting the energy needs of the world, especially in Vietnam. Vietnam has favorable natural conditions for this energy production. Because it is hot and humid, and it has much rainfall and fertile soil, biomass develops very quickly. Therefore, byproducts from agriculture and forestry are abundant and continuously increasing. However, byproducts that are considered natural waste have become the cause of environmental pollution; these include burning forests, straw, and sawdust in the North; and rice husks dumped into rivers and canals in the Mekong Delta region. Biomass energy is provided in a short cycle, is environmentally safe to use and is encouraged by organizations that support sustainable development. Taking advantage of this energy source provides energy for economic development and ensures environmental protection. Due to the abovementioned favorable conditions, many biomass energy plants are being built in Vietnam. Like other renewable energy investment projects, the selection of the construction contractor, the selection of equipment for the installation of the power plant, and the choice of construction site are complex multi-criteria decisions. In this case, decision-makers must evaluate many qualitative and quantitative factors. These factors interact with each other and it is difficult to use personal experience to choose the optimal solution for such complex decision-making problems, especially in a fuzzy decision-making environment. Therefore, in this study, the authors use a Multi-Criteria Decision-Making (MCDM) model that uses a Fuzzy Analytic Hierarchy Process (FAHP) model and the Combined Compromise Solution (CoCoSo) algorithm to select biomass furnace suppliers utilizing both qualitative and quantitative factors. Furthermore, the results of this work will provide the first look at a hybrid CoCoSo/FAHP method that decision-makers in other fields can use to find the best supplier.

Decision Support System is a system commonly used to assist management in assisting decision-making in top managerial sections, Multi-Criteria Decision Making (MCDM) is one of many decision-making methods that can be used to select the best alternative from a number of alternatives based on certain criteria, one of the methods that can be utilized is the Elimination Et Choix Traduisant la Realite (ELECTRE) method that works based on the concept of outranking using pairwise comparisons of each alternative based on appropriate criteria, this paper applies the ELECTRE method in a web based application that can be employed for input and output dynamic for alternative, criteria, user values and also a fast ranking process, the choice of web based application because there is many research about decision support system but only few that applied to application, and this research tries to applied ELECTRE method to web based application.

The purpose of this research is to apply RcmdrPlugin.sutteForecastR in forecasting data. RcmdrPlugin.sutteForecastR is the Rcmdr Plug-in package by using the package from α-Sutte Indicator i.e. sutteForecastR. Data used in this research are Rainfall Data of Indonesia 1991-2015.

Cryptography is a field of science that can be learned to secure data and information, cryptography is used in almost all communications both in network and non-network; and one of the algorithms could use is RC4 algorithm, publication about RC4 algorithm is quite a lot but the discussion is dominant only theory alone does not complete the RC4 algorithm process in detail and applications only show input and output none of the processes include, in this research paper illustrates the process of RC4 algorithm in detail and with visualization to demonstrates the work of RC4 algorithm to make it easiest for readers to learn cryptography

This study examines the effectiveness of augmented reality (AR)-based learning in enhancing early childhood social skills through the integration of social learning theory and experiential learning theory. Using a quasi-experimental pre-test and post-test control group design, the research involved 30 children aged 5-6 years at Telkom Kindergarten, Makassar, Indonesia. The experimental group (n = 15) participated in AR-based storytelling activities designed to foster social skills, while the control group (n = 15) engaged in conventional learning activities. Statistical analysis showed that the AR intervention produced significantly greater effectiveness (N-Gain = 0.73) compared to conventional methods (N-Gain = 0.03). The experimental group demonstrated about 49% improvement across all social skills components, including teamwork, adjustment, interaction, self-control, empathy, discipline, and respect for others. The novelty of this research lies in combining observational learning with direct experiential engagement through AR, creating an effective environment for social skills development that surpasses single-framework approaches. These findings provide strong evidence that well-designed AR-based learning can transform early childhood education by addressing multiple aspects of social competence. The study highlights the potential of emerging technologies to support holistic child development when grounded in comprehensive theoretical foundations and implemented with child-centered pedagogical practices.