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The architecture of consumer decision-making has completely changed due to the quick development of recommendation systems based on artificial intelligence (AI). The majority of earlier studies saw algorithms as instruments for forecasting and maximizing preexisting preferences. This study, however, makes a different claim: algorithmic curation actively shapes preferences rather than just reflecting them. This study creates and evaluates a structural model that examines the impact of algorithmic curation intensity on perceived search autonomy, identity resonance, affective evaluation, and the development of initial preferences. The model is based on identity-based consumption theory and the literature on human-AI interaction. The study's findings, which are based on survey data from Generation Z consumers and Structural Equation Modeling (SEM) analysis, demonstrate a contradictory dynamic: algorithmic curation improves identity resonance and directly influences initial preferences while simultaneously decreasing feelings of autonomy. The primary mediating mechanism that links algorithmic exposure to emotional assessment and preference creation is identified as identity resonance. In addition to introducing the concept of algorithmic consumer formation as a new conceptual framework for comprehending consumer behavior in the AI-based digital era, our findings expand the notion of bounded rationality toward algorithmically bounded agency.

The idea of functions in mathematics and how they are used to build different mathematical models are methodically examined in this publication. Functions are basic mathematical constructs that show relationships between two or more variables in explicit equations, tables, or graphs. The fundamental building blocks of mathematics are functions, which enable the representation of variable interdependencies in a variety of formats, including formal mathematical expressions, data tables, and graphs. The classification of function types, such as linear, quadratic, and exponential, and their corresponding uses in the domains of physics, economics, and epidemiology are the main topics of this study, which takes a descriptive and exploratory approach.This article illustrates how knowledge of functions greatly aids processes through a review of the literature and an examination of secondary sources from current textbooks and academic publications. of judgment, forecasting, and analysis. In both academic and professional contexts, mathematical modeling based on functions has demonstrated efficacy in accurately and efficiently representing real-world occurrences. Thus, the significance of incorporating functional thinking into STEM education and multidisciplinary practice is emphasized in this essay.

The SMEs sector has an important role in supporting economic development in Indonesia. One of the sub-districts in Gresik, namely Sidayu, has many SMEs that are members of an association called ASUMSI. Almost all SMEs in these locations have not carried out a structured production planning. Production planning plays a vital role in a product-producing industry, including SMEs. Production planning can be started by forecasting demand. The results of this demand forecasting become the basis for production planning. This community service takes the theme of introducing the concept of demand forecasting which is expected to improve the production planning process for SMEs. This community service is carried out through counseling regarding the basic concept of demand forecasting for SMEs actors who are incorporated in ASUMSI. This community service is a form of academic participation in increasing the capacity of local resources, namely SMEs

In geotechnical engineering, building robust structures is crucial to ensure the bearing capacity of structures against external forces, so making sure soil strength and unreliable build cost and duration prediction are also very important and preliminary aspects of any construction project. Therefore, in this first-of-its-kind modern examine, the capability of various artificially intelligent (AI)-based models toward reliable forecasting and estimation of preliminary construction expenses, duration, and strength at shear is explored. First, background information about the revolutionary artificial intelligence (AI) technique along with its many distinct models ideal for geotechnical and building engineering problems is presented, The use of AI-based models in the literature for the aforementioned construction and maintenance applications is discussed in a number of current works, together with their benefits, drawbacks, and future directions. Several important input elements that significantly affect the preliminary price of construction, construction time, and soil's shear strength estimation are listed and given through analysis. Finally, some obstacles to employing AI-based models for precise forecasts in these applications are discussed, along with elements influencing the problems with cost overruns. Thus, this work can help civil engineers make effective use of artificial intelligence (AI) to solve difficult and risky tasks. It can also be used to Internet of Things (IoT) environments for self-learning applications like smart architectural health-monitoring systems

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.

Forecasting the number of electricity customers is a crucial aspect of resource planning and management at PT PLN Tasikmalaya. One method that can be used to improve forecasting accuracy is Lagrange Interpolation, which constructs an interpolation polynomial based on known data points. Although this method has been widely applied in various fields, its use in forecasting the number of electricity customers remains rare. Therefore, this study aims to explore the application of the Lagrange Interpolation method in forecasting the monthly number of electricity customers at PT PLN Tasikmalaya.In this study, historical electricity customer data was used as the basis for constructing an interpolation polynomial, which was then utilized to forecast the number of customers from January 2024 to May 2024. The forecasting results indicate an increase in the number of electricity customers each month. After rounding, the estimated number of customers is 274,952 in January 2024, 278,142 in February 2024, 284,402 in March 2024, 296,031 in April 2024, and 316,391 in May 2024.The findings of this study demonstrate that the Lagrange Interpolation method can be applied to forecast the monthly number of electricity customers at PT PLN Tasikmalaya. However, to improve forecasting accuracy, it is recommended to compare this method with other forecasting techniques and consider external factors that may influence the number of customers, such as government policies, economic growth, and changes in electricity consumption patterns.

This research aims to identify the nature of the cash flow statement, methods of preparing it and its indicators. Identify the nature of profitability and explain its indicators, shed light on the topic of predicting the financial distress of economic units, the causes of distress and ways to treat it, and use cash flow and profitability indicators to help predict the financial distress of Iraqi industrial companies listed on the Iraq Stock Exchange in the second and third years preceding the financial distress. The research community is represented by the industrial companies listed on the Iraq Stock Exchange, which number (21) companies until January 2023, while the study sample is the Iraqi Engineering Works Company in order to apply the current research in it. The research reached several conclusions, the most important of which was that the increase in cases of financial distress to which Iraqi industrial companies are exposed is due to the lack of instructions or directives specific to the industrial sector and the failure to use financial indicators through quantitative methods and methods to predict financial distress before it occurs, and to determine what the financial position will be in the future.

With the rising demand in the food sector, particularly for ready-made spices, CV. Peduli Pangan, which focuses on producing sachets of pepper, has a notable chance for expansion. To address this demand, accurate forecasting of production is essential for guiding choices related to production planning. Forecasting is an important tool for decision-making that underpins various manufacturing and service sectors.This research aims to estimate the ideal production quantity of pepper sachets over the next 12 periods. Regression analysis is utilized to identify the best-fitting model based on the gathered data, facilitating an exploration of how important factors affect production. The resulting regression formula shows that the production levels are impacted by the cost of raw materials (HBB), product defects (PG), and workforce (TK). The constant figure of 11,109.687 signifies the fundamental production level when these factors are not considered. If all other factors are ignored, a decrease in production volume occurs when the raw material price (X1_HBB) is -0.15 and the defect rate (X2_PG) is -0.617. On the other hand, production volume rises if the labor factor (X3_TK) is valued positively at 37.317. This forecasting model is designed to aid CV. Peduli Pangan in making informed and precise production choices.

The purpose of this research is to create a JST (artificial neural network) model that can forecast population growth at the Population and Civil Registration Office of West Sumba Regency. population growth at the Population and Civil Registration Office of West Sumba Regency. Regency. Regional development planning must consider the increasing number of population, therefore proper forecasting is essential to encourage sustainable policies and initiatives. sustainable policies and initiatives. Because it can identify complex patterns in past data and produce more accurate forecasts than traditional techniques, an ANN model is used. traditional techniques, the ANN model is used. The data used in this study is the population growth of Southwest Sumba Regency over the past including characteristics such as birth and death rates and population movements. deaths and population movements. The backpropagation algorithm is used to optimize the multilayer perceptron (MLP) architecture for ANN training. Separating the data into training and testing sets and assessing the models model using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) based on the error. Error (RMSE) based on the prediction error are the steps involved in the training process. involved in the training process. The research findings show that, with a low level of error, the artificial neural network model can estimate the population increase in Southwest Sumba Regency with a reasonable level of accuracy. reasonable level of accuracy. The model is expected to serve as a reference for relevant authorities to better manage population data and as a tool to create more focused and successful population policies.

Indonesia, as a country with complex geological conditions due to the convergence of various tectonic plates, is highly susceptible to natural disasters such as earthquakes, tsunamis, and volcanic eruptions. The city of Semarang, as the capital of Central Java Province, also frequently faces disasters such as floods, landslides, and earthquakes. Predicting the occurrence of natural disasters becomes crucial to mitigate the negative impacts they cause. This study uses the Markov chain method to predict natural disasters in the city of Semarang based on disaster data from 2018-2022. The prediction results indicate a 16% chance of floods, 34% chance of landslides, 10% chance of tornadoes, 22% chance of fires, and 17% chance of falling trees in 2023. Validation of the predictions against actual data for 2023 shows a relatively good match for floods and fires, but there are significant differences in the predictions for tornadoes and falling trees. These results indicate that the Markov chain method has potential in predicting disaster occurrences, but accuracy improvements are needed to account for weather variability and dynamic environmental factors. This research is expected to assist the government and society in enhancing disaster preparedness and mitigation in the future.

Behavioral economics has highlighted how cognitive biases influence financial decisionmaking, often leading to suboptimal outcomes. This paper explores the impact of behavioral biases such as overconfidence, loss aversion, and herding behavior on accounting and economic forecasting. By reviewing empirical evidence from market behavior, the study assesses how these biases affect financial reporting, auditing practices, and economic predictions. The paper concludes with recommendations for accountants and economists to incorporate behavioral insights into their practices to improve decisionmaking and forecasting accuracy.

This study explores the integration of predictive analytics into supply chain management within national e-commerce enterprises. Predictive analytics, which utilizes historical data combined with machine learning algorithms, regression analysis, and time series forecasting, has shown significant improvements in operational efficiency. The study focuses on four key areas: demand forecasting, inventory management, transportation optimization, and customer satisfaction. By predicting demand more accurately, e-commerce platforms can reduce stockouts and overstock situations, streamline logistics routes, and lower logistics costs. The implementation of predictive analytics led to a 20% reduction in delivery times and a 15% decrease in logistics costs, thereby enhancing customer satisfaction. However, the study also highlights challenges in integrating real-time data from multiple sources and scaling predictive models across diverse product categories and geographic regions. The results emphasize the need for e-commerce platforms to invest in technology that enables seamless data integration and the development of region-specific predictive models. The findings are compared with industry benchmarks, showing that the improvements in logistics and supply chain performance align with global trends. Based on these results, the study recommends best practices for implementing predictive analytics, including effective data collection, machine learning model training, and scalability considerations. By following these practices, e-commerce companies can optimize their supply chains, reduce operational costs, and increase customer satisfaction, positioning them for greater competitive advantage in the marketplace.

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.

The daily needs of the people of Central Aceh cannot be separated from agricultural commodities such as tomatoes, shallots, garlic, and others. Some of these agricultural commodities have sharp price fluctuations, such as tomatoes. When the supply of tomatoes in the market is reduced, the price can be much higher than the normal price. Conversely, when the supply of tomatoes is excessive, the price will fall far below the normal price. This is influenced by various factors such as the harvest season, the amount of production, the amount of public consumption and others. Based on these problems, we need a method to be able to estimate the price of tomatoes so that it can be used to support decision making related to price issues. Forecasting is one of the solutions to be able to estimate the movement of tomato commodity prices. The method used for forecasting tomato prices is High Order Fuzzy Times Series Multifactors. In this method, subinterval formation is carried out using Fuzzy C–means. To calculate the error rate of forecasting results in this study using the Mean Square Error (MSE). Based on the results of the tests carried out, the large values ​​of the training and order data used in forecasting do not guarantee a low error rate.

Company CV. AM Nanda Putra is located in Sidoarjo and operates in the scaffolding industry. Currently the company is experiencing losses due to a lack of optimization in planning the amount and time of ordering raw materials, which results in shortages and excess material inventory. To overcome this problem, the company uses the MRP (Material Requirement Planning) method in optimizing raw material planning. In terms of the lot sizing approach, the company applies the LFL and EOQ methods. The forecasting methods used are Moving Average (MA), Weight Moving Average (WMA), and Exponential Smoothing (ES). The smallest MAD results were obtained using the Exponential Smoothing (ES) method for all scaffolding products. The forecasting results are obtained to determine the MPS (Master Production Schedule) for the next 10 months. After the determination of  MPS, the results of Material Requirement Planning (MRP) were obtained, namely the supply of raw materials for MF 170 AM scaffolding of 35402 units or 17700 sets, MF 170 K1 scaffolding of 28906 units or 14453 sets, MF 190 AM scaffolding of 16250 units or 8125 sets, and MF 190 K1 scaffolding of 7656 units or 3828 sets. From the results of calculating the cost of raw material requirements using the Lot for Lot (LFL) method and the Economic Order Quantity (EOQ) method, it can be seen that the total cost of planning the smallest raw material inventory with an amount of Rp. 12,975,818,022.