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Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.

This study explores the role of sentiment analysis as a predictive tool for understanding and forecasting product launch success in the digital market. Sentiment analysis involves the classification of consumer sentiment expressed on social media platforms such as Twitter and Instagram, and it can significantly impact businesses by predicting consumer behavior and product performance. The research highlights the relationship between social media sentiment and product success, demonstrating that positive sentiment is strongly correlated with higher sales and consumer engagement, while negative sentiment can lead to declines. Machine learning models, including Support Vector Machines (SVM) and Random Forest, were employed to classify sentiment from large volumes of social media data and correlate it with product performance indicators such as sales volume and consumer interaction. The study found that sentiment analysis models were highly effective in predicting product success, with positive sentiment generally driving product profitability and negative sentiment posing a potential threat to brand reputation. Moreover, the analysis showed that social media sentiment provides real-time insights into consumer perceptions, enabling businesses to quickly adjust marketing strategies and product development plans. These findings underscore the importance of integrating sentiment analysis into product launch evaluations and strategic decision-making. Future research should explore the integration of sentiment analysis with other predictive market models and investigate the effects of fake reviews and post-purchase consumer behaviors on product success.

Renewable energy microgrids integrated with smart control systems are emerging as a sustainable solution for electrifying rural industrial zones, offering substantial improvements in energy efficiency and reductions in carbon emissions. This study explores the implementation of hybrid renewable energy systems, combining solar and wind energy, and the integration of Internet of Things (IoT) sensors to optimize energy consumption in real-time. The findings highlight that the combination of solar and wind energy in microgrids leads to up to a 30% increase in energy efficiency, with a significant reduction in CO₂ emissions, reaching up to 50% compared to traditional grid systems. IoT sensors play a crucial role in load forecasting, optimization, and system stability, enabling real-time monitoring and proactive adjustments to energy distribution. Additionally, the implementation of these systems in rural industrial zones not only provides reliable, clean energy but also reduces reliance on fossil fuels, making them economically viable and environmentally sustainable. However, challenges such as high initial investment costs, integration complexities, and the need for skilled technicians remain. Despite these barriers, the long-term benefits of reduced energy costs, improved energy security, and lower carbon footprints make renewable energy microgrids a promising solution. The study suggests that these systems can be scaled to other rural regions facing similar challenges in energy access and carbon emissions, offering a path to sustainable development. Further research is recommended to explore alternative renewable energy combinations and advancements in IoT applications to improve system scalability and efficiency.

Profit growth is important for businesses as it can be used to forecast future business plans. Earnings growth is difficult to separate from the company's financial performance as measured by financial ratios. This study aims to determine the effect of accounts receivable turnover ratio, current ratio, debt to equity ratio, and inventory turnover on profit growth. This study has a population of 17 companies over three years (per quarter), and a sample of 204 collected through purposive sampling method, and this study uses data analysis methods, namely multiple regression analysis and panel data with Eviews 12 software. The analysis shows that ITR has a positive and significant effect on earnings growth (Ln), while RTR, CR, and DER have no effect on earnings growth (Ln). All independent variables, namely RTR, CR, DER, and ITR, affect earnings growth (Ln) simultaneously. Earnings growth (Ln) is influenced by the four independent variables by 9%. This shows the capability of financial ratios in anticipating profit increases and can influence investor investment decisions.

Technology develops from time to time influenced by human activities and needs that continue to increase as well. Technological developments also play a role in helping the agricultural sector. Through technological developments, many problems in agriculture can be resolved. One of the problems is in agricultural extension activities, where farmer participation in extension activities is low in some areas. This research aims to build UI/UX design for an agricultural mobile application called Sobatani which is useful for connecting farmers with agricultural extension workers.  This research uses the Design Thinking method which consists of five stages including empathize, define, ideate, prototype, and testing. The test results using the Single East Question (SEQ) method obtained a total average of 6.6, indicating that the UI design is fairly effective and successful. Although there are some misclicks, so it is necessary to iterate on the Log in and Weather Forecast pages. The use of the Design Thinking method has proven effective in understanding user needs and designing solutions that can provide these needs.

Sprite drink is a soda drink that is very popular among all groups. Demand is uncertain and always changes from time to time, making product availability difficult to control and often causes overstock or stockout problems. Therefore, inventory control is needed, which can be done by forecasting, determining safety stock and good re-order points. To obtain effective and efficient planning, the number of orders must be based on the number of past mass requests so as to reduce the occurrence of overstock or stockouts. With the problems experienced by PT. XYZ, the forecasting method used is the time series forecasting method. In this case, the time series methods used are Simple Average, Single Moving Average and also Single Exponential Smoothing. After carrying out several calculations, we obtained a Mean Absolute Centage Error (MAPE) value of 49.379%, a Mean Absolute Deviation (MAD) of 2297.145, a Root Mean Squared Error (RMSE) of 2912.495 and also a Mean Squared Error (MSE) of 8,482 .628 and forecasting results of 4504 pcs every month. Based on the calculation results, the proposal given is to reorder Sprite 250ML when the inventory in the warehouse reaches 1548 pcs with a safety stock of 540 pcs.

PT. Kalonika Bara Kusuma is a company operating in the mining sector located in the city of Samarinda, East Kalimantan province. To achieve maximum profits, PT. Kalonika Bara Kusuma adds or subtracts units according to the amount of turnover obtained in the previous month. However, after being evaluated, it turned out that this method was not effective. Because you only see at a glance the fluctuations in historical data. Sometimes when you have reduced units, it turns out that demand in the following month actually increases. This results in less than optimal profits because they cannot serve existing customer requests. Vice versa. This is what causes PT. Kalonika Bara Kusuma experienced difficulty in making a decision to add or subtract units. From this problem, the author created an application that can predict the amount of turnover in the next month and provide recommendations for deciding which camera units should be increased or decreased in number. To predict the amount of turnover using the Multiple Linear Regression method. After obtaining the predicted results for the amount of turnover, a test was carried out using the Mean Absolute Percentage (MAPE) with a result of 200%, which means that the Multiple Linear Regression method is not suitable to be used to predict the amount of turnover in the next period. Production forecasting is a form of decision making that is used as a basis in many manufacturing and service industries. Therefore, companies that are able to produce products on time and in the right quantities are companies that are able to survive the competition. This demand forecasting is used to forecast demand for products that are independent (not dependent), such as forecasting finished products. The multiple linear regression method is an analytical technique that tries to explain the relationship between two or more variables, especially between variables that contain cause and effect, called regression analysis. So in relation to the description above, this research aims to determine production forecasting using the multiple linear regression method at PT. Kalonica Bara Kusuma.The mining industry is a series of activities that have a long period of time and costs a lot of money, a series of industrial activities, namely mining activities which include digging, loading and hauling to obtain optimal profits from activities. One of the mining industries needs to be a study of operational costs for transportation equipment