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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.

This study aims to analyze the fluctuations in chili production in Kabanjahe District, Karo Regency, which affect market price instability and uncertain supply. One approach applied in this study is the Single Exponential Smoothing (SES) method to forecast chili production. SES was chosen for its simplicity, ease of implementation, and its ability to generate accurate predictions even when the data lacks significant seasonal patterns. The data used is secondary data on chili production obtained from official publications by the Karo Regency BPS for the period of 2020–2024. The analysis results show that a smoothing parameter (α) of 0.8 produced the lowest Mean Absolute Percentage Error (MAPE) of 3.08%. These findings indicate that applying a higher α makes the model more responsive to recent data changes, thus yielding more accurate forecasts. This study demonstrates the effectiveness of the SES method in forecasting chili production in areas with significant seasonal fluctuations.

Ineffective spare part inventory planning may lead to supply delays and reduced compliance with lead time supply key performance indicators (KPIs). This study aims to implement the Collaborative Planning, Forecasting, and Replenishment (CPFR) method in spare part inventory planning at PT XYZ and to compare lead time supply performance before and after the implementation of the CPFR method. This research utilizes spare part usage data from January to June 2025, focusing on fast-moving spare parts. Demand forecasting is conducted using an error forecasting approach with the moving average method. Forecast accuracy is evaluated using the Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). Furthermore, inventory planning is carried out through the calculation of safety stock and reorder point (ROP) as the basis for determining replenishment decisions. The results indicate that the simulated implementation of the CPFR method provides a more structured and anticipative inventory planning process. The comparison of performance before and after the application of CPFR shows an improvement in lead time supply compliance with the established KPIs. Therefore, the CPFR method has the potential to support improved spare part inventory planning performance at PT XYZ.

International student enrollment is a critical driver of financial sustainability for Higher Education Institutions (HEIs). While advanced forecasting is standard in the corporate sector, its application in educational planning remains limited. This study addresses this gap by comparing the predictive performance of ARIMA, Facebook Prophet, and Long Short-Term Memory (LSTM) models. Using a publicly available annual dataset from a US-based institution (2000–2022), the analysis employed a strategic partition training on 2000–2017 and testing on 2018–2019 to validate models on stable, pre-pandemic data. Empirical results revealed that the statistical ARIMA (2,1,0) model demonstrated superior accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 1.26%. Conversely, Prophet (11.81%) and LSTM (13.84%) struggled with the limited sample size, failing to generalize effectively compared to the linear approach. The findings suggest that for annual enrollment trends, parsimonious statistical models outperform complex deep learning architectures, providing administrators with a robust, accessible framework for data-driven strategic decision-making.

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

Stock price prediction remains a complex challenge due to the dynamic and non-linear nature of financial markets, especially for banking stocks like PT Bank Negara Indonesia (Persero) Tbk (BBNI). This study aims to optimize BBNI stock price forecasting by integrating an automated Extract, Transform, Load (ETL) pipeline with the Long Short-Term Memory (LSTM) algorithm within a data engineering framework. Historical data from 2019 to 2025 were processed through a structured ETL sequence—including data cleaning, feature engineering, and MinMaxScaler normalization—to ensure high data quality. The dataset was partitioned into 80% for model training and 20% for testing to ensure rigorous evaluation. The results demonstrate that the systematic ETL approach significantly enhances model stability and predictive accuracy compared to conventional methods. The LSTM model effectively captured long-term temporal dependencies, providing reliable trend forecasts with an impressive test accuracy, achieving a Root Mean Squared Error (RMSE) of 0.0354. This research underscores that integrating robust data engineering practices with deep learning is essential for building resilient financial decision-support systems.

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

This study examines the development of a Crude Palm Oil (CPO) price forecasting model using an artificial neural network algorithm, specifically the backpropagation algorithm. As one of Indonesia’s main export commodities, CPO has a significant economic impact and influences the income of oil palm farmers. The CPO price data used in this study were obtained from CIF Rotterdam, covering the period from January 2019 to December 2023. The research methodology consists of several stages, including data collection, preprocessing, model design, and model implementation using Python programming. The training results of the backpropagation algorithm show an error value of 0.537829578 after 1,000 epochs, while the evaluation using Mean Squared Error (MSE) indicates an MSE of 0.022709 during the training process and 0.017604 during the testing process. The model also produces CPO price predictions for the next three months, namely 932.578 for the first month, 949.568 for the second month, and 774.855 for the third month. These findings indicate that the developed model is capable of predicting future CPO prices with adequate accuracy, which can assist companies in making better financial decisions and managing risks associated with CPO price fluctuations.

The dynamic fluctuations in stock prices present a major challenge for investors in making informed decisions. To anticipate such uncertainties, forecasting methods that can provide accurate predictions are required. This study compares two time series forecasting methods Autoregressive Integrated Moving Average (ARIMA) and Double Exponential Smoothing (Holt) in predicting the stock prices of PT Telkom Indonesia (TLKM). The dataset consists of monthly closing prices from January 2018 to December 2023. The performance of each model is evaluated using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the ARIMA(1,1,1) model yields higher predictive accuracy than the Holt method, with MAE of 787.71, MSE of 771,844.2, and RMSE of 878.55. In contrast, the Holt method records a MAE of 837.19, MSE of 878,393.4, and RMSE of 937.23. These findings confirm that ARIMA is superior in capturing the complex patterns of stock price movements and is more effective in volatile market conditions such as the stock exchange.

The relatively high volatility of Robusta coffee prices creates uncertainty for farmers, business actors, and policymakers in making economic decisions. This study aims to analyze the price movement patterns of Robusta coffee, determine the most appropriate Autoregressive Integrated Moving Average (ARIMA) model, and conduct short- to medium-term price forecasting for Robusta coffee. The data used consist of monthly Robusta coffee price data from January 2023 to September 2025, sourced from the World Bank Commodity Price Data. The analytical method employed is ARIMA using EViews software, beginning with stationarity testing using the Augmented Dickey-Fuller (ADF) test, model identification through ACF and PACF, parameter estimation, and residual diagnostic testing. The results show that Robusta coffee price data are non-stationary at the level but become stationary at the first difference, indicating integration of order one I(1). Based on model identification and diagnostic testing, the ARIMA (0,1,0) model is found to be the most appropriate and satisfies the white noise assumption. Forecasting results indicate that Robusta coffee prices are projected to remain relatively stable with a moderate upward trend through December 2026. These findings are expected to serve as a reference for decision-making by farmers, business actors, and the government in responding to Robusta coffee price dynamics.

This study analyzes and designs a dual-role web-based ordering information system to optimize order management at Sunrise Bakery. This SME currently faces inefficiencies due to manual recording. The system, developed using the SDLC Waterfall method with PHP and MySQL, serves two main actors: customers, who can order online, browse catalogs, track orders, and pay digitally; and administrators (admin, cashier, owner), who manage products, update stock, input in-store orders, generate daily/monthly sales reports, and manage user access. Black Box Testing confirms all core functions work correctly. The system successfully addresses manual process shortcomings by improving data accuracy and providing real-time monitoring for both customers and management. It offers a comprehensive digital solution to enhance operational efficiency and service quality. Limitations include the lack of integrated digital payment gateways and external messaging. Future development should incorporate payment gateways (e.g., OVO, GoPay), WhatsApp notifications, a mobile application, and predictive analytics for sales and stock forecasting.