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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.

The increasing trend of secondhand clothing consumption, commonly known as thrift shopping, reflects a major shift in consumer behavior. This study aims to analyze the influence of product quality, price, and lifestyle on consumers’ purchase decisions for secondhand clothing at the Tugu Pahlawan Sunday Morning Market in Surabaya. This research employed a quantitative approach using a non-probability sampling design. Primary data were obtained from 167 respondents through structured questionnaires utilizing a five-point Likert scale. Data analysis was conducted using multiple linear regression with the support of SPSS software, preceded by validity, reliability, and classical assumption tests. The results show that product quality, price, and lifestyle simultaneously have a significant influence on purchase decisions. Partially, product quality and lifestyle have a positive and significant effect, while price shows a weaker but still significant influence. These findings indicate that consumers’ decisions to purchase secondhand clothing are not solely driven by low prices but also influenced by perceived product quality and lifestyle preferences. This study contributes to the understanding of consumer behavior in informal markets and provides practical implications for business practitioners and policymakers to enhance sustainable consumption patterns.

This study aims to design, implement, and compare the performance of the Backpropagation algorithm from Artificial Neural Networks and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in predicting the optimal daily rice requirement at Grillme Restaurant in Pontianak. The main problem faced by the restaurant is the uncertainty in determining the required daily rice stock, which periodically results in either understocking (shortage) or overstocking (wastage), leading to operational losses. To address this, the study utilizes historical daily rice sales data from January 2023 to April 2025 as the database for training and testing both predictive models. The SARIMA approach is employed to capture time series components (trend and seasonality), while Backpropagation is utilized to model non-linear patterns. Comparative test results indicate that the SARIMA model achieved superior accuracy compared to the Backpropagation model. This is confirmed by the Mean Absolute Percentage Error (MAPE) value of the SARIMA algorithm being 17.35%, which is lower than the MAPE value of Backpropagation at 19.62%. The MAPE values obtained by both models demonstrate good predictive capability, but it is concluded that SARIMA is more recommended for a more efficient and planned management of rice stock at Grillme Restaurant in Pontianak.

The rapid development of Islamic banking in Indonesia faces significant challenges in maintaining liquidity and profitability amidst dynamic capital market conditions. The urgency of this study arises from the need to examine whether traditional financial ratios, such as the Financing to Deposit Ratio (FDR) and Return on Equity (ROE), play a decisive role in influencing investment decisions, which are proxied by the Price to Earning Ratio (PER). The main objective of this research is to empirically test the effect of liquidity and profitability, both partially and simultaneously, on investment decisions in Islamic commercial banks listed on the Indonesia Stock Exchange during the 2021–2025 period. This study adopts an associative design with a quantitative approach, utilizing secondary data from financial reports obtained from the IDX, and analyzed using multiple linear regression on 68 observation samples. The findings reveal that neither liquidity nor profitability significantly influence investment decisions, either partially or simultaneously. These results suggest that investors in the Islamic banking sector tend to prioritize non-financial factors such as sharia compliance, governance, macroeconomic conditions, and ESG trends, rather than conventional financial indicators. In conclusion, this research extends the understanding of the limitations of Signaling Theory in the sharia context and recommends the development of a more holistic investment evaluation model. Future studies are encouraged to incorporate non-financial variables for a more comprehensive analysis.

This study aims to analyze the influence of financial literacy, paylater usage, and income on the consumptive behavior of Generation Z in Indonesia. The background of this research is based on the increasing use of digital financial services, particularly paylater, and the growing trend of consumptive lifestyles among Generation Z, who are exposed to the convenience of technology and have relatively low financial control. The research uses a quantitative approach with descriptive and causal analysis. The population in this study consists of Generation Z in Indonesia aged 18–27 years who have income and have used paylater services. The sampling technique used was non-probability sampling with the purposive sampling method, and the sample size was 100 respondents. Data were collected using questionnaires and analyzed using SPSS version 26. The analysis included validity tests, reliability tests, multiple linear regression, t-tests, F-tests, and the coefficient of determination (R²). The results indicate that financial literacy has a negative and significant effect on consumptive behavior, meaning that the higher the financial literacy, the lower the tendency to act consumptively. Conversely, paylater usage and income have a significant joint influence on Generation Z’s consumptive behavior.

This study aims to analyze the effect of promotional strategies on consumer purchasing decisions for Freedom Street Wear products on the Shopee platform. The research population consists of Shopee users who have purchased products from official stores. A quantitative approach using a survey method was applied, and data was collected from 100 respondents selected through non-probability sampling. Multiple linear regression analysis was used to test the influence of each variable, both partially and simultaneously. The findings show that Double Date Discount has a positive and significant effect on purchasing decisions. Meanwhile, Free Shipping and Cash on Delivery (COD) did not have a significant effect, although COD showed a positive trend. Simultaneously, these three variables were found to have a significant effect on consumer purchasing decisions. In addition to examining the individual effects of promotional strategies, this study also explored the interactive relationships between the variables. It was found that while Free Shipping and COD did not individually influence purchasing decisions, their combination with other promotional strategies like Double Date Discount enhanced the overall effectiveness of the promotions. This suggests that promotional strategies should be strategically integrated to maximize their impact on consumer behavior. Furthermore, the study highlights the growing importance of e-commerce platforms like Shopee in influencing purchasing decisions and shaping consumer perceptions. As online shopping continues to grow, understanding the nuances of promotional strategies is essential for businesses aiming to attract and retain customers in a competitive market. The findings contribute to a deeper understanding of the factors driving consumer purchasing decisions and offer valuable insights for businesses looking to optimize their promotional efforts in the online retail environment.

Data from the Business Competition Supervisory Commission (KPPU) indicate that the impact of COVID-19 in 2020, 2021, and peaking in 2022 led to a significant increase in merger and acquisition (M&A) activities. This trend suggests that M&A actions have become an essential strategy for sustaining and enhancing business performance. However, not all M&A activities result in success, making it crucial to understand the factors influencing their outcomes. This study aims to examine and provide empirical evidence on the effect of board size, institutional ownership, and firm size on merger and acquisition performance. Agency theory and signaling theory are employed as the theoretical frameworks to explain the relationships between the independent and dependent variables. The population of this study consists of publicly listed companies that conducted mergers and acquisitions between 2019 and 2023. The sampling technique used was purposive sampling, resulting in a total of 150 samples. Data were collected through non-participant observation, and the data analysis technique applied was multiple linear regression. The results show that institutional ownership has a positive effect on merger and acquisition performance. In contrast, board size and firm size do not significantly influence M&A performance. These findings indicate that monitoring by institutional shareholders can enhance the effectiveness of strategic decision-making, while a larger organizational structure and firm size do not necessarily support post-merger integration success.

In today’s global economy, accurately predicting foreign exchange rates or estimating their trends correctly is crucial for informed investment decisions. Despite the success of standalone models like ARIMA and deep learning models like LSTM, challenges persist in capturing both linear and nonlinear dynamics in highly volatile exchange rate environments. Motivated by the limitations of these individual models and the need for more robust forecasting tools, this study proposes a hybrid ARIMA-LSTM model that integrates ARIMA’s strength in modeling linear trends with LSTM’s capability to capture nonlinear dependencies, using historical USD/NGN exchange rate data from the Central Bank of Nigeria (CBN) spanning 2001 to 2024. The research hypothesis posits that the hybrid ARIMA-LSTM model will significantly outperform standalone models in forecasting accuracy. By comparing these models against state-of-the-art approaches, the study highlights the advantages of hybridizing statistical and deep learning methods. The findings demonstrate that the hybrid model achieved the lowest Root Mean Squared Error (RMSE) of 2.216 and the highest R² of 0.998, indicating superior forecasting performance. This study fills a critical research gap by demonstrating the effectiveness of hybrid deep learning in financial time series forecasting, providing valuable insights for investors, policymakers, and financial analysts. Future research will extend this work by incorporating the latest dataset and evaluating model robustness during the recent surge in the Naira/Dollar exchange rate from 2023 to 2024.

Task scheduling in cloud computing environments is a crucial aspect in optimizing resource allocation and improving system efficiency. This research aims to analyze trends in task scheduling algorithms in cloud computing using a Systematic Literature Review (SLR) approach on various scientific publications published between 2018 and 2025. The results of the study show that Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) algorithms are the most commonly used methods in solving task scheduling problems. PSO stands out as an effective algorithm due to its ability to find global optimal solutions, handle non-linear and multimodal problems, and its efficiency in managing computational resources. Additionally, various studies have shown that optimization of scheduling algorithms can be achieved through a combination or modification of existing methods to improve system performance. This study provides in-depth insights into the development of scheduling algorithms in cloud computing and opens up opportunities for further research in developing more innovative and adaptive approaches.

The entry of K-Beauty trend in Indonesia has influenced the interest of skincare consumers in Indonesia. Indonesia. The local skincare market continues to grow every year leading to increased competition in the local cosmetic business in Indonesia. This study aims to examine how the influence of product quality, brand image product quality, brand image and Korean Brand Ambassador on consumer buying interest in local skincare products. local skincare products. The sampling method was carried out with the technique Non-Probability Sampling technique where data is obtained through distributing questionnaires to 250 respondents with the determination of the number of respondents using the formula. to 250 respondents by determining the number of respondents using the Lemeshow formula. Then the data analysis was carried out using multiple linear regression analysis multiple linear regression analysis with data processing carried out using IBM software SPSS version 23 which then the data is analyzed and the results of the research are obtained in the form of: (1) Product Quality has a positive and significant effect on consumer interest in local skincare products, (2) Product Image consumer buying interest in local skincare products, (2) Brand Image has a positive and significant effect on consumer buying interest in local skincare products. significant effect on consumer buying interest in local skincare products, (3) Korean Brand Ambassador has a positive and significant effect on consumer purchase intention local skincare products, and (4) Product Quality, Brand Image, and Korean Brand Ambassador together have a positive and significant effect on consumer buying interest in local skincare products. Ambassadors together have a positive and significant effect on consumer buying interest in local skincare products

The rise of the trade in fake cosmetics and skincare in various well-known e-commerce makes consumers more selective in purchasing cosmetic and skincare products. However, Sociolla as the number one well-known beauty store in Indonesia comes with its e-commerce platform that specifically sells cosmetic and skincare products from various well-known product brands and is able to stay in first place in the beauty product category in Top Beauty-Commerce. This trend can be seen from the increasing frequency of repeated transactions on the Sociolla platform. This study aims to determine the effect of the relationship between electronic service quality (E-Service Quality) and repurchase intention (Repurchase Intention) through electronic satisfaction (E-satisfaction) as a mediation variable. This research is quantitative. The data collection technique was carried out by distributing a 4-level linear scale statement questionnaire. Samples were taken using Non-Probability Sampling techniques with data analysis techniques using Smart PLS tools. The respondents involved were 160 respondents who met the criteria for having made purchases on the Sociolla platform twice. The results showed that good E-Service Quality skills increased E-Satisfaction which led to Repurchase Intention by Sociolla platform users. This shows that E-Satisfaction effectively acts as a mediation between E-Service Quality and Repurchase Intention.

Peramalan merupakan proses untuk memperkirakan beberapa kebutuhan di masa yang akan datang, yang meliputi kebutuhan dalam ukuran kuantitas, kualitas, waktu dan lokasi yang dibutuhkan dalam rangka memenuhi permintaan barang atau pun jasa. Penelitian ini bertujuan untuk meramalkan jumlah angkatan kerja di Kota Medan menggunakan metode Trend Non Linear Kubik. Data yang diperlukan untuk penelitian ini adalah data jumlah angkatan kerja di Kota Medan dari tahun 2011-2020 dan Sumber data penelitian ini diperoleh dari Badan Pusat Statistik (BPS) Kota Medan. Data tersebut dianalisis dengan metode Trend Non Linear Kubik  untuk meramalkan jumlah angkatan kerja pada tahun 2021 dan pengolahan datanya menggunakan software SPSS. Hasil analisis Model peramalan jumlah angkatan kerja di Kota Medan menggunakan metode Trend Non Linear Kubik diperoleh persamaan modelnya adalah y=512819+155612x-28928x^2+1754x^3 dan hasil keakurasian model sebesar 3,94%, peramalan yang dilakukan menghasilkan jumlah angkatan kerja di Kota Medan untuk tahun 2021 adalah 1.058.837.