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Bank stock price prediction is an important topic in the application of information technology because stock price movements are dynamic, sequential, and influenced by historical market patterns. This study aims to predict Indonesian banking stock prices using the Long Short-Term Memory method and evaluate the effect of Bayesian Optimization on model performance. The data used in this study consists of daily historical stock data of BBCA, BBNI, BBRI, BBTN, and BMRI from May 4, 2020, to May 4, 2026, obtained from Yahoo Finance. The input features include opening price, highest price, lowest price, closing price, and trading volume, while the prediction target is the stock closing price. The results show that the baseline model produced MAPE values ranging from 1.892% to 3.147%. The best baseline performance was obtained on BBCA with an R² value of 0.933, followed by BBTN with an R² value of 0.902. After optimization, performance improvement occurred on BBTN, with MAPE decreasing from 3.147% to 2.482% and R² increasing from 0.902 to 0.935. For BMRI, MAPE decreased from 2.385% to 2.206%, and R² increased from 0.687 to 0.743. This study concludes that Long Short-Term Memory can be used to predict Indonesian banking stock prices, while Bayesian Optimization can selectively improve model performance depending on the characteristics of each stock dataset.

This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

Amid intense market competition, the profitability of manufacturing companies is not solely determined by sales volume but is highly dependent on the precision of financial management, particularly in managing the working capital cycle and operating cash flow circulation. This study aims to evaluate the relationship between Working Capital Turnover (X1) and Operating Cash Flow (X2) on Profitability (Y) in consumer goods industry companies listed on the Indonesia Stock Exchange during the 2022–2024 period. Using a quantitative approach and multiple linear regression analysis, this study processes 77 observations that have passed purposive sampling and outlier testing. The partial test results reveal contrasting findings: Working Capital Turnover (X1) does not have a significant effect on profitability, while Operating Cash Flow (X2) is proven to be a strong positive determinant. However, simultaneously, both variables have a significant influence on the financial performance of companies (Fhitung 24,008 > Ftabel 3,08), with operating cash flow acting as the dominant driving factor of profit. The implications of these findings emphasize that to maintain profit stability, management should prioritize the availability of cash generated from core operations, while investors should be more attentive to cash flow trends as an indicator of fundamental financial health before making investment decisions.

The coal mining industry requires accurate stockpile volume measurements for inventory and production reporting. Conventional methods have limitations in accuracy, efficiency, and safety. This study compares the accuracy and efficiency of coal stockpile volume measurements using a Terrestrial Laser Scanner (TLS) Leica MS60 and an Unmanned Aerial Vehicle (UAV) DJI Matrice 4E, validated by the ASTM D6172-98 standard. Conducted on five Run of Mine (ROM) coal stockpiles covering 13,500 m² at PT XYZ, Lahat, South Sumatra, the TLS method used 43 scan positions, while the UAV employed 430 aerial images with specific flight parameters. Data were processed using Leica Infinity, Surpac, and Agisoft Metashape. The results showed volumes of 94,076 m³ (TLS) and 94,965 m³ (UAV), with a difference of 889 m³ (0.95%). Volume deviations ranged from 0.48% to 1.89%, with an average of 1.42%, all within the ASTM tolerance (<2%). Time efficiency analysis revealed that the UAV method required 200 minutes (3.33 hours), saving 63.3% (approximately 6.17 hours) compared to the TLS method (570 minutes). The largest efficiency gain occurred during field data acquisition, with an 85% reduction in time. This study confirms UAV photogrammetry as a valid, accurate, and efficient alternative for coal stockpile volume measurement in mining.

This study aims to determine the effect of product availability, communication effectiveness, and promotional strategy on sales volume at PT.Distribusindo Cemerlang Jaya, a cosmetic and skincare distribution company in Batam city. The background of this research is based on pre-survey result indicating suboptimal sales volume along with operational challenges including stock shortages, communicatiob gaps, and limited promotional activities.ths research employs a quantitative method. The population consist of all comsumers, with a sample of 43 respondent selected using a saturated sampling technique. Data were collected through questionnaires using a likert scale and analyzed using validity and reliability test, classical assumption tests, multiple linear regression, t-test, F-test, and coefficient of determination (R²) with SPSS. Result show that partially, product availability does not significantly affect sales volume (sig. 0,313 > 0,05), and communication effectiveness also does not significantly affect sales volume (sig. 0,898 > 0,05). However, promotional strategy has a positive and significant effect  on sales volume (sig, 0,000 < 0,05). Simultaneously, all three variables significantly affect sales volume (F = 212.642, sig. 0,000 < 0,05). The coefficient of determination (R²) is 0,942 indicating that 94,2% of the variation in sales volume is explained by the three independent variables. Promotional strategy is the most dominant factor influencing sales volume is explained by the three independent variables. Promotional strategy is the most dominant factor influencing sales volume at PT.Distribusindo Cemerlang Jaya.  

Coal stockpile volume measurement is an essential part of mining production control. This study aims to evaluate the calculation of coal stockpile volume using a Total Station (TS) at PT. Bukit Baiduri Energi, Kutai Kartanegara Regency, East Kalimantan Province, and to assess the accuracy and effectiveness of this method in operational activities. Data acquisition was carried out through direct field measurements using a Total Station with a detailed surface point survey method. The collected data were processed using Minescape 5.7 software to generate a triangulated surface model, and the volume was calculated using the Cut and Fill method. The results show that the Total Station method produced a stockpile volume of 21,069.15 m³ with a high level of accuracy due to direct field measurement. This method provides advantages in elevation data accuracy; however, it requires relatively more time and manpower during the data acquisition process. Based on these findings, the use of Total Station is recommended for stockpile volume calculations that require high accuracy, particularly for production evaluation and coal reserve reporting.

One of the most actively traded and liquid stock groups in the capital market is the LQ45 index, which consistently attracts investor attention due to its strong market capitalization and transaction volume. This study aims to analyze the influence of financial performance and financial management strategies on firm value among companies listed in the LQ45 index on the Indonesia Stock Exchange during the 2018–2022 period. The study population consisted of 73 LQ45-indexed companies, with purposive sampling used to select 23 companies that met the research criteria. This research employed a quantitative approach using path analysis to examine both direct and indirect relationships among variables. The findings indicate that profitability and leverage have a positive and significant effect on firm value. In addition, profitability and leverage also positively influence firm size, indicating that companies with stronger profitability and effective debt management tend to expand their operational scale. However, firm size does not significantly affect firm value and is unable to mediate the relationship between profitability, leverage, and firm value. These results suggest that investors place greater emphasis on profitability and leverage indicators than company size when evaluating firm value in LQ45 companies. Therefore, effective financial performance remains the primary factor in enhancing corporate value and investor confidence.

Measuring coal volume in the Stock ROM area played an important role in production control and mining evaluation. This research was conducted in the Stock ROM area at PT. Victor Dua Tiga Mega, where volume measurements were generally carried out using Total Station (TS) as the main method, howefer the use of Unmanned Aerial Vehicles (UAV) has begun to be implemented as a more efficient alternative. This study aims to compare the results of Stock ROM coal volume calculations using the Total Station and UAV methods. The research method was carried out by collecting data in the field, processing the digital Elevation Model (DEM), and calculating the volume using the Cut and Fill method. The results of the study the difference in volume between the two methods, where the UAV measurement results tend to be greater than those of the Total Station. The difference in Fine Coal volume was 724,15 m3 or 16,74% and Raw Coal volume of 9.335,98 m3 or 8,03%. Based on a comparison with weighing data, measurements using the Total Station provided results that were closer to the actual conditions in the field.

This study aims to analyze the reaction of the Indonesian capital market to the announcement of Donald Trump’s import tariff policy using an event study approach. Market reactions are measured through abnormal return and trading volume activity of exporting companies listed on the Indonesia Stock Exchange (IDX), with an event window of three trading days before and three trading days after the initial tariff announcement on April 2, 2025 and the revised tariff announcement on July 15, 2025. This study employs secondary data in the form of daily stock prices and trading volumes, analyzed using descriptive statistics, normality tests, and the Wilcoxon Signed Rank Test. The results indicate that the Indonesian capital market reacts to the announcement of Donald Trump’s import tariff policy, as reflected by differences in abnormal return and trading volume activity before and after the announcements, thereby supporting signaling theory and the semi-strong form of market efficiency.