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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.

Inventory management is a crucial aspect of business sustainability, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face limitations in data management and technological adoption. Selaras Muba Lestari is Incubation Center, which supports MSMEs in Musi Banyuasin Regency, encounters challenges in managing product inventory due to reliance on manual recording systems. This study aims to design and develop a web-based inventory application using the Laravel framework to improve the efficiency and accuracy of stock management. The research adopts the Software Development Life Cycle (SDLC) Waterfall model, which includes requirements analysis, system design, implementation, and testing. The results indicate that the developed system is capable of improving data accuracy, accelerating the recording process, and providing real-time inventory reports. Furthermore, this study reinforces previous findings that digitalizing inventory systems serves as a strategic solution to enhance the competitiveness of MSMEs.

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.

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.

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.

The use of digital technology in MSMEs is still uneven, especially in small businesses that still rely on manual recording such as at Toko Ibu Siti, so that the transaction process becomes slow and there is a risk of errors. This activity aims to implement an on-premise-based cashier system that is in accordance with business conditions and helps improve user understanding in operating it. The method used is qualitative descriptive with a participatory approach through observation, interviews, and questionnaires. The results of the activity show that there are quite clear changes, where the transaction process becomes faster, recording is neater, and the stock of goods is easier to monitor. In addition, users can run the system independently after training, with a response that tends to be positive. The implementation of this system also encourages technology adaptation in the business environment, improves the accuracy of sales data, and enables business owners to make more effective information-based decisions. Overall, the implementation of this system is able to help improve work efficiency, reduce human error, and encourage the use of technology in small-scale businesses so as to support the sustainable growth of MSMEs.

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.

Drug stockouts in hospitals pose significant risks to service quality, patient safety, and operational efficiency. This study aimed to analyze how drug demand planning and procurement processes at Johar Baru Regional General Hospital contribute to stockout occurrences and to develop data-driven recommendations based on supply chain management principles. A qualitative descriptive design was employed using data triangulation. Data were collected through in-depth interviews with the Head of the Pharmacy Installation, procurement staff, and warehouse pharmacists, complemented by direct observation and analysis of 2024 planning and procurement documents. Thematic analysis was conducted with the support of NVivo software to identify patterns and relationships among key variables, including drug demand planning, procurement, and inventory management.Findings reveal that stockouts stem from interconnected weaknesses in planning accuracy, procurement coordination, and inventory control systems. Effective stock management depends not only on increasing supply but also on improving data quality, integrating inventory information systems with operational workflows, and enhancing cross-functional collaboration. Recommended strategies include implementing a minimum stock alert system integrated with the Hospital Management Information System (HMIS), strengthening standard operating procedures for stockout response and procurement confirmation, improving integration between HMIS, the National Formulary, and budgeting systems, and applying consumption based planning methods combined with ABC VEN analysis to optimize inventory control.

Stock monitoring is a critical phase that must be performed regularly to maintain the accuracy and efficiency of inventory management. Continuous monitoring ensures that all items remain under proper oversight, thereby making stock management processes simpler, more controlled, and highly accurate. PT XYZ operates in the general contracting sector and provides a range of services, including land transportation, crude oil rental, heavy equipment and light vehicle rental, material supply, and well maintenance services. At present, stock monitoring at PT XYZ still relies on a general-purpose application designed solely for numerical calculations. This approach leads to several limitations in the current system, most notably the lack of a login feature and the requirement to recreate reports using a separate application. Based on these problems, this research aims to design a prototype of a stock monitoring information system at PT XYZ. The system is developed using the waterfall development model and documented using use case diagrams. The main output of this study is a prototype information system that allows users to change their own password, perform CRUD operations on data entities including users, items, categories, brands, units, vehicles, suppliers, incoming goods, and outgoing goods, generate various reports related to inventory/stock at PT XYZ.

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 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.

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.

This community service activity was conducted at PT Danny Karya Sukses, a newly established distribution company specializing in stainless steel kitchen equipment in Batam City, which faced challenges in managing inventory due to the use of manual recording systems and the absence of standardized operational procedures. These conditions led to a high risk of data inaccuracies, stock discrepancies, and inefficiencies in operational coordination. The objective of this program was to design and implement a standardized Inventory Standard Operating Procedure (SOP) integrated with a digital inventory management system using Zoho Inventory. The methods employed included interviews, field observations, documentation studies, and literature reviews to identify operational needs and design appropriate solutions. The implementation process involved SOP development, system configuration, employee training, and operational assistance. The findings indicate significant improvements in inventory accuracy, real-time stock monitoring, work efficiency, and interdepartmental coordination between administration, warehouse, and sales divisions. The adoption of Zoho Inventory reduced manual errors, accelerated stock reporting, and strengthened internal control mechanisms. The implications of this activity demonstrate that the integration of digital inventory systems with clear SOPs can serve as a strong operational foundation for newly established distribution companies, supporting sustainable business growth and enhanced competitiveness.

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.

This study develops a strategic information system plan to improve customer service at the Food Mood MSME in the food and beverage sector. The Ward and Peppard framework is used to map the business and technology environment through Value Chain, SWOT, PEST, and Porter's Five Forces analyses, which are then broken down into Critical Success Factors and measurable key performance indicators. The research design is a qualitative case study with semi-structured interviews with the owner and employees, observations during peak hours, and a review of operational documents. The mapping results in a prioritized portfolio that places a cloud-based point-of-sale system integrated with QRIS, a lightweight inventory and procurement module, a kitchen display system, and basic accounting as the foundation, followed by a mini customer relationship management and loyalty program, online channel integration, a sales dashboard, and simple demand forecasting. The formulated performance targets include a wait time of no more than eight minutes, an order error rate below one percent, stock-outs of less than one day per month, and 100% transaction recording. The suggested three-month roadmap is operational and provides immediate benefits in terms of increased service speed, data accuracy, and potential customer retention, while also confirming the relevance of Ward and Peppard's approach for the Indonesian MSME context.

The implementation of the Frequent Pattern Growth (FPG) algorithm in a web-based drug purchasing application at Sumber Sehat Pharmacy aims to improve efficiency and accuracy in analyzing customer drug purchasing patterns. The FPG algorithm is a method used to identify frequent purchase patterns or frequent itemsets in purchase transactions, which can then be used to make relevant drug recommendations for customers. This study uses a case study at Sumber Sehat Pharmacy to explore drug purchasing patterns and provide a data-driven solution that can help pharmacies improve service and adjust drug stocks according to customer needs. The results show that the application of the FPG algorithm can identify significant purchasing patterns and assist pharmacies in determining more appropriate promotional strategies and inventory management. By using a web-based application that implements this algorithm, Sumber Sehat Pharmacy can provide drug recommendations that are more in line with customer preferences, thereby increasing customer satisfaction and pharmacy operational efficiency.

Earnings quality refers to the accuracy of a company in presenting its earnings information. It reflects the quality of the company’s financial statements, indicating whether the reported earnings can be considered reliable or not. Earnings quality is influenced by several factors, one of which is the decision-making of the board of directors (CEO). The CEO is regarded as the most powerful individual within a company, exercising authority over corporate decisions, including the disclosure of financial information. In recent times, many women have taken on the role of CEO, and their presence is no longer underestimated. The purpose of this study is to provide empirical evidence on the effect of female CEO presence and CEO education on earnings quality. The research was conducted on all companies listed on the Indonesia Stock Exchange (IDX) for the 2019–2022 period. The sample size was determined using a saturated sampling method, resulting in 2,792 observations. Data were collected using a non-participant observation method, and the analysis technique employed was multiple linear regression analysis. The results of this study show that female CEO presence and CEO education have no significant relationship with earnings quality.

This study aims to analyze the role of inventory management in improving financial efficiency at CV. Sumber Jaya Putra Perkasa. The main problems faced by the company are manual inventory management, technological limitations, dependence on certain suppliers, and suboptimal demand planning, which affect distribution effectiveness and financial efficiency. This study uses a quantitative descriptive approach with data collection techniques through interviews, observation, and documentation. The analysis was conducted on the stock management process, inventory turnover, and its impact on storage costs and operational efficiency. The results show that good inventory management contributes significantly to increased financial efficiency. With proper stock planning, companies can minimize the risk of excess and shortage of goods, reduce storage costs (holding costs), and increase inventory turnover so that working capital can circulate more quickly. However, the inventory management system currently used by CV. Sumber Jaya Putra Perkasa still has limitations, especially in terms of digitization and information integration. This study recommends the implementation of a technology-based inventory management system, a multi-supplier strategy, and the application of demand forecasting methods to improve stock planning accuracy. With this strategy, it is hoped that the company can achieve more optimal financial efficiency and strengthen its competitiveness in the distribution industry.

Inventory management plays a crucial role in ensuring smooth business operations by preventing stockouts that may cause losses. Toko Cat Gani still relies on manual recording, which is prone to data loss, duplicate entries, and delays in stock reporting, thus requiring a systematic solution. This study aims to design and implement a web-based inventory information system using the buffer stock method to overcome these issues. The research method employed is Research and Development (R&D) with a prototype model, consisting of needs analysis, system design, validation, and testing stages. The system was developed using PHP, MySQL, and XAMPP, featuring item data management, supplier management, inbound and outbound transactions, buffer stock calculations, and real-time inventory reports. The implementation results show that the system facilitates transaction recording, minimizes data entry errors, provides notifications when stock reaches the minimum threshold, and generates accurate and timely reports. Expert validation and user testing confirm that the system is feasible and effective in supporting inventory management at Toko Cat Gani. Therefore, the implementation of a web-based inventory information system with the buffer stock method can be considered an efficient and reliable solution to improve accuracy and effectiveness in inventory control.

This study aims to compare and analyze the accuracy levels of four financial distress prediction models—Altman Z-Score, Springate, Grover, and Zmijewski—in anticipating the potential bankruptcy of companies subjected to delisting from the Indonesian Stock Exchange (IDX). The delisting phenomenon, which is strongly linked to severe financial deterioration, provided the core motivation for identifying the most reliable predictive instrument, utilizing secondary data from the annual financial reports of delisted companies during the 2019-2023 observation period. Descriptive analysis techniques were employed to calculate the accuracy rate and Type Error for each model. The comparative results consistently indicate that the Springate Model is the most effective, consistent, and accurate model for predicting financial distress in delisted firms, achieving an accuracy rate of 89% in both the first and second years prior to delisting, while the Altman Z-Score model exhibited lower accuracy (68.75% and 62.50%). This key finding emphasizes the superiority of the Springate Model as a crucial diagnostic tool for investors and regulatory bodies in assessing corporate bankruptcy risk.