Rescollacomm (E-Journals)
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The Role of Parents in Forming Children\u27s Financial Literacy from an Early Age
Get PDFThis research illustrates the importance of introducing the concept of income and expenditure at an early age by presenting simple, relevant examples. The introduction of income, such as a weekly allowance from parents, helps children understand the meaning of effort in earning money, develops financial decision-making skills, and builds discipline. Positive impacts include a better understanding of financial concepts and growing financial independence, despite potential wasteful habits and dependence on parents. On the other hand, the introduction of expenses, such as buying snacks or toys, helps children learn to choose between different options, understand the value of money, and be responsible for their spending. It also strengthens financial decision-making skills and awareness of financial priorities, although it can lead to wasteful habits and lack of awareness about saving for the future. In conclusion, early financial literacy education has a key role in shaping children\u27s understanding of finance, financial values, and wise financial management practices, which will help them face future financial challenges and build a more stable financial foundation, while creating a strong foundation for healthy financial understanding within the family
Application of Fish Waste Processing for Sustainable Livestock Feed Production A Community Engagement Study in Garut Regency
Get PDFThis community engagement study aimed to develop an application for processing fish waste into animal feed based in an incubator system in Garut Regency. The program was conducted from May to November 2023 with the primary objective of transferring technology in waste processing and animal feed production to partner groups. Methods included socialization, technical and non-technical training, and direct mentoring in animal feed pellet production. Results showed a significant improvement in the knowledge and skills of the groups in producing fish waste pellets, reducing feed production costs, and enhancing the sustainability of local livestock businesses. Challenges encountered included initial production limitations and consumer trust in new products. With in-depth scientific approaches and sustained support, the program successfully created positive impacts on the environment and community economic welfare
Factors Affecting the Uptake of Life Insurance in Botswana
Get PDFThe low uptake of life insurance is a matter of significant concern both within Africa and globally due to its aims to investigate the factors contributing to the poor adoption of life insurance products in Botswana. Through a survey conducted among 800 randomly selected individuals representing diverse genders in Botswana, with an 80% response rate, this study sheds light on the factors that hinder the uptake of insurance products in the country. The findings indicate that the low adoption of insurance products in Botswana can be attributed, at least in part, to various factors including low income, pervasive poverty, limited insurance awareness and education, and inadequate regulatory oversight
The Effect of Workload and Psychological Capital on Burnout for Media Company Employees in Cirebon City, West Java, Indonesia
Get PDFThis study aimed to determine how workload and psychological capital impact burnout among employees of media companies in Cirebon City. This study used the quantitative method to collect primary data by distributing Likert-scale questionnaires directly to 17 media companies around Cirebon City. Secondary data came from research methodology books and national and international official journals. The researcher conducted data analysis using SPSS to test the hypothesis and research model. Using the non-probability purposive sampling method, the research sample was 153, collected from 17 media companies. The results showed that workload has a positive and significant effect on burnout, psychological capital has a negative and significant effect on burnout, and workload and psychological capital have a significant relationship with burnout among employees of media companies in Cirebon City, West Java, Indonesia
Effects of Functional Strategies on Service Delivery in Meru County Government, Kenya
Get PDFThe Meru County government had devised functional strategies; however, their execution had been lacking, resulting in unsatisfactory service delivery. Moreover, funds in Meru County had been redirected to unauthorized expenditures, with the government unable to provide documentation for a spending amounting to Sh209 million in the 2021/2022 fiscal year. Challenges faced by the County government extended to limited financial resources and instances of fund misappropriation. The study aimed to explore the effects of functional strategies on service delivery in Meru County Government. The theoretical foundation of the study incorporated ideas from Strategic Fit Theory. To carry out the research, an explanatory research design was employed. Both primary and secondary data was utilized, with primary data collected through the use of semi-structured questionnaires. A pilot test was carried out in the Embu County government to reveal the reliability and validity of the research instrument. Qualitative data analysis employed thematic analysis, presenting the results in a narrative format. For quantitative data, both descriptive and inferential statistics were utilized. Descriptive statistics encompassed frequency distribution, mean (indicating central tendency), standard deviation (measuring dispersion), and percentages. Inferential statistics involved Pearson correlation analysis and multivariate regression analysis. The study found that Functional strategies account for 83.0% of the performance in Meru County Government. The study recommends that county governments should develop policies to ensure effective control of county activities and enhance staff supervision across various departments
Comparative Analysis of the Effectiveness Between Malwarebytes and BitDefender to Prevent Malware Attacks
Get PDFMalware is the largest type of cyber-attack case in Indonesia. With the number of cases of malware occurring, many emerging software that provides services to ward off malware attacks. It takes the most effective anti-malware software to ward off malware attacks, so research is carried out. This study tested the detection and removal power of two anti-malware software (BitDefender and Malwarebytes). The initial research method used is to make a Pilot test which is a prefix in malware testing. In the Pilot test, the initial testing process for anti-malware software is carried out. Software that tested in the Pilot test include Malwarebytes, BitDefender, Avast, Cybereason, AVG, Avira. In the Pilot test, as many as 30 malwares were tested to determine which two software had the highest percentage of detection and removal tests. Furthermore, the data from the previous test got analyzed using the proportion of two populations test to determine the most effective software. With the tests of 500 malwares, it was found that the proportion of detection and removal of the BitDefender software is better than the Malwarebytes software. Therefore, it can be concluded that the BitDefender software is more effective than the Malwarebytes software as seen from the results of the test of the proportion of malware detection and removal
Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques
Get PDFPredicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT\u27s natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices
Optimal Portfolio Using Single Index Model (SIM) For Health Sector Stocks
Get PDFInvestment is one of the fund management activities with the aim of obtaining future profits. In addition to profits, investors also need to consider the risks that will be faced by diversifying. Diversification is done by forming an optimal portfolio. This research aims to determine the proportion of stocks in the optimal portfolio and calculate the expected return and risk value of the optimal portfolio. The object used to form the optimal portfolio is health sector stock group for the period January 2020 - December 2022. The method used to form the optimal portfolio is Single Index Model (SIM). The results showed that there were 6 combinations of health sector stock in the optimal portfolio, such as IRRA, PRDA, SAME, SILO, MERK, and HEAL stocks of 8.94%, 9.24%, 9.34%, 11.92%, 27.15%, and 33.41% respectively with expected return of 2.68% and a risk value of 1.85%
Implementation of Bidirectional Long Short Term Memory (BiLSTM) Algorithm with Embedded Emoji Sentiment Analysis of Covid 19 Anxiety Level and Socio Economic Community
Get PDFThe COVID-19 pandemic has presented multidimensional challenges in Indonesia, significantly affecting social, economic, and public health at the level of anxiety. Public anxiety related to the pandemic can be reflected in online media, especially Twitter, which is the main channel for information sharing and emotional expression. This study aims to understand the level of public anxiety in relation to the aftermath of the COVID-19 pandemic by using a classification method. Classification is carried out using the Knowledge Discovery in Database method with the Bidirectional LSTM algorithm and emoji embedding sentiment analysis, and K-Fold Cross Validation testing is also carried out with various optimizers. The final result of the best accuracy rate obtained was 98.08%. This shows that the classification model created is good
Best Distribution Selection in Modeling the Interest Rate as a Random Modifier
Get PDFThe interest rate is seen as a random variable because the interest rate has an unpredictable nature or changes over time. This means that the interest rate cannot be anticipated in the future with a certain degree of certainty. Therefore, mathematical models are needed to predict the behavior and value of future interest rates. The models used in this study were interest rate, uniform distribution , and lognormal distribution. The data used in the study were interest rate data for 2014-2015 and sample data for uniform distribution. The resulting model in interest rate modeling as a random variable uses for uniform and lognormal distributions with the application of data and . The interest rate model as a uniformly distributed random variable is considered better with a smaller standard deviation, , and values compared to the lognormal distribution based on the data used