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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.

The development of information technology has encouraged companies to adapt in order to improve operational effectiveness and efficiency, particularly in inventory management. CV Dewangga, a company engaged in the distribution of office supplies, still relies on a manual system using book records and simple documents. This condition often leads to problems such as delays in stock checking, inaccurate data entry, difficulties in tracking incoming and outgoing goods, and late report preparation. This study aims to design and develop a web-based inventory information system by implementing the First-In First-Out (FIFO) algorithm to ensure that items entering earlier are also issued first, thereby minimizing the risk of old stock accumulation. The research method applied is Rapid Application Development (RAD), whichconsists of requirement analysis, design, implementation, and testing. Research data were collected through direct observation at CV Dewangga and interviews with staff regarding the inventory workflow. The developed system provides features for real-time recording of incoming and outgoing goods, FIFO-based stock management, and automated report generation. Testing using the black-box method confirmed that all system functions operate as required. The findings indicate significant improvements in data accuracy, reporting speed, and inventory efficiency compared to manual methods. This study contributes by presenting a practical web-based inventory system model suitable for small and medium enterprises (SMEs). The implication is to encourage the digitalization of inventory management to enhance competitiveness in the Industry 4.0 era.

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.

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.

This study aims to analyze the financial performance of PT ABC Tbk during the period 2019–2023 using the Du Pont System as the primary analytical tool. The Du Pont System is widely recognized as a comprehensive method to evaluate a company’s overall performance by breaking down profitability into several key components: net profit margin, total asset turnover, return on investment (ROI), equity multiplier, and return on equity (ROE). The research employs a descriptive quantitative approach, with data sourced from secondary materials in the form of official financial statements published by the Indonesia Stock Exchange (IDX). A purposive sampling technique was applied to ensure the relevance and accuracy of the data analyzed. The findings reveal that the company’s financial performance throughout the five-year observation period has been less than optimal. Each of the main components of the Du Pont System showed average ratios that fell below the industry benchmark, indicating structural weaknesses in both profitability and efficiency. Specifically, the net profit margin and total asset turnover were constrained by high operational costs, while ROI and ROE were further pressured by volatility in foreign exchange rates. These inefficiencies highlight the vulnerability of the company’s financial structure to both internal management challenges and external macroeconomic factors. Based on the results, the study provides several strategic recommendations to improve financial performance. First, optimization of cost management is necessary to reduce operational inefficiencies that directly affect profit margins. Second, the implementation of foreign exchange risk mitigation strategies, such as hedging, is suggested to minimize the negative impacts of currency fluctuations. Finally, to strengthen revenue growth, the company is encouraged to adopt and expand digital marketing initiatives as a means of improving sales performance and market penetration. Overall, this study emphasizes the importance of integrating financial control with strategic innovation to ensure long-term sustainability and competitiveness in the pharmaceutical industry.

In the ever-evolving e-commerce era, the Nibras Online Warehouse plays a crucial role in supporting the distribution of Muslim clothing products throughout Indonesia. This strategic role demands an accurate and efficient inventory management system to ensure smooth and timely order fulfillment. One of the main challenges faced is the discrepancy between inventory data recorded on the website and the actual physical stock available in the warehouse. This discrepancy not only impacts delivery delays but also has the potential to reduce customer satisfaction and the company's overall image. This study aims to analyze the root causes of the inventory management system and develop practical solutions to ensure data consistency between the digital system and real-world conditions. The methods used include literature review, direct field observations, in-depth interviews with warehouse staff, and documentation of daily operational processes. Through an analytical approach using the 5 Whys method and a fishbone diagram, it was found that factors such as delays in data input, lack of synchronization between the operational and IT divisions, and an undocumented goods receipt process were the main causes of inventory data discrepancies. To address this, it is recommended to implement a real-time technology-based inventory management system integrated with the online sales system. Additionally, training warehouse employees on new standard operating procedures (SOPs) and regular stock audits are crucial steps to create transparency and efficiency. These steps are expected to improve data accuracy, accelerate decision-making, and support sustainable business growth. Regular evaluations are also necessary to ensure the implemented system remains relevant and adaptable to the dynamics of warehouse operations.

The rapid development of information technology has had a significant impact on various sectors of life, including micro-enterprises such as meatball stalls. Amidst increasingly fierce competition and the need for operational efficiency, many micro-enterprises are shifting from manual management systems to digital systems. This study aims to explain the background, objectives, and benefits of utilizing information technology in managing meatball stalls, with a focus on the implementation of the Odoo application as a business management solution. Odoo is an open-source Enterprise Resource Planning (ERP) system that offers various functional modules such as Point of Sale (POS), inventory management, accounting, and Customer Relationship Management (CRM). Through the implementation of Odoo, meatball stalls can manage various operational aspects in an integrated manner, from recording sales transactions, managing raw material stock, financial reporting, to customer relations. The results of this technology implementation show significant improvements in data recording accuracy, service speed, and ease of decision-making based on accurate and real-time data. In addition, this technology also provides opportunities for stall owners to develop their businesses more professionally and competitively. Thus, the integration of information technology like Odoo not only improves efficiency and productivity but also contributes to economic growth by strengthening the micro-enterprise sector. This digital transformation is expected to be a strategic step in realizing a modern meatball stall that can compete in the digital era.

Manual transaction and stock recording at Ubay Galon causes various issues, such as data loss, record inaccuracies, and lack of real-time access. This study aims to design and develop a web-based sales information system to improve operational efficiency and data accuracy. The research was conducted at Ubay Galon, South Tangerang, using the Waterfall development method. Data collection techniques include observation, interviews, and literature studies. The system was designed using UML and implemented with web technologies. The result is a sales information system that records transactions, manages inventory, and generates automatic and real-time reports. The system also enhances data security with login and automatic backup features. In conclusion, the system effectively supports the digital transformation of Ubay Galon's business processes.

Inventory management of raw materials is a crucial aspect in the manufacturing industry, particularly in the pharmaceutical sector, as it directly affects the continuity of the production process. This study aims to analyze the raw material inventory accounting system in inventory control at PT Bernofarm Pharmaceutical Company. The methods used include direct observation of operational processes and interviews with management to obtain relevant and accurate data. The results of the study show that PT Bernofarm has implemented an integrated accounting information system within an ERP framework, covering procedures for raw material requisition, issuance, return, and recording of production costs. Each procedure is systematically arranged with clear task separation and is fully computerized. This facilitates internal control and monitoring of raw material flow, while minimizing recording errors. With this effective system, the company is able to avoid both overstocking and stock shortages that could disrupt production. This study is expected to serve as a reference for improving the efficiency and accuracy of raw material inventory management in other pharmaceutical companies.

This research investigates the implementation of stock opname procedures in the General Affair warehouse at PT Bernofarm Pharmaceutical Company, a major pharmaceutical manufacturer. The study responds to the problem of discrepancies in inventory data, which can disrupt operations due to inaccurate recording or delays in documentation. The objective is to analyze the effectiveness and structure of stock opname procedures in controlling non-productive inventory. A qualitative descriptive method was employed, utilizing observation and interviews with Accounting and General Affair warehouse staff to collect primary data. The findings reveal that stock opname at PT Bernofarm is conducted periodically through coordinated stages: scheduling, preparation of inventory data, physical inspection, data reconciliation, reporting, and inventory adjustment. The procedures are carried out collaboratively between the Accounting and General Affair divisions, with clearly defined responsibilities and documentation such as stock cards, credit memos, and goods receipts. Despite a difference in theoretical and practical implementation—where inventory recording is managed by warehouse staff rather than accounting—the presence of functional segregation and supervisory checks ensures effective internal control. The study concludes that a structured and consistent stock opname process improves data accuracy, minimizes discrepancies, and enhances accountability. This research is limited to non-productive inventory in a single company division, and further studies are suggested to explore digital solutions or comparative analyses across industries.

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in supporting regional economic development. However, inefficient inventory management remains a significant challenge in operational effectiveness. This study aims to analyze raw material inventory control at Kebab & Burger Foursist MSME in Tarakan City using the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods. A descriptive quantitative approach was employed, utilizing annual sales data, ordering costs, and storage costs of main raw materials. The results indicate that the implementation of EOQ and ROP effectively determines the optimal purchase quantity and reorder timing, thereby minimizing total inventory costs and reducing the risk of stockouts or overstocking. The use of POM-QM for Windows software enhances the accuracy of the analysis. The implications of this study offer practical solutions for MSME actors in managing raw material procurement more efficiently and systematically.

This study aims to analyze the optimization of seaweed seedling inventory management using the Economic Order Quantity (EOQ) method to enhance cost efficiency for farmers in Tarakan City. The research employs a quantitative descriptive approach, integrating EOQ with forecasting techniques (Moving Average and Exponential Smoothing) to predict raw material needs accurately. Data were processed using Microsoft Excel and POM-QM for Windows to ensure precision. The results indicate that EOQ yields an optimal order quantity of 878 ropes per order, with a frequency of 6 orders per year, a reorder point of 16 ropes, and a total inventory cost (TIC) of IDR 842,681. Compared to traditional methods (TIC IDR 2,132,083), EOQ reduces costs by 60.5%. Forecasting analysis reveals that Exponential Smoothing (MAPE 19.67%) outperforms Moving Average (MAPE 22.5%) in accuracy. These findings highlight EOQ’s effectiveness in minimizing waste, preventing stockouts, and improving productivity. The study provides practical insights for coastal small-scale farmers and policymakers in the marine sector.

The increasing competition in the bottled drinking water distribution business in Tarakan City compels companies to improve sales forecasting accuracy as a basis for strategic decision-making. This study aims to analyze the effectiveness of the Exponential Smoothing with Trend method implemented through the POM-QM software in predicting the sales volume of CV Tirta Anugerah Abadi. Monthly sales data over the past two years were quantitatively analyzed using the Exponential Smoothing model with various smoothing parameter combinations. The results indicate that the parameter combination of α = 0.7 and β = 0.1 yields the lowest forecasting error, with a MAPE value of 1.24%. These findings demonstrate that the applied method is highly accurate and reliable for sales forecasting, thus supporting the company in more efficient stock, production, and distribution planning. The implications of this research not only enrich the literature on the application of modern forecasting methods in the local distribution sector but also provide practical contributions to company management in data-driven decision-making.

This study aims to evaluate the accuracy of the Fibonacci Retracement and Moving Average Convergence Divergence (MACD) technical indicators in analyzing the movement of banking stock prices listed on the LQ45 index in the 2020 period. Based on data from PT Kustodian Sentral Efek Indonesia (KSEI) in 2023, there was a significant increase in the number of investors in the Indonesian stock market by 103.6 percent in 2020. This study uses a quantitative descriptive approach with a census method for sampling, which resulted in 5 banking companies as samples: BBCA, BBNI, BBRI, BBTN, and BMRI. Data analysis was carried out using the Fibonacci Retracement indicator to identify potential support and resistance levels, and the MACD indicator to evaluate the strength, direction, and momentum of stock price movements. The results showed that 11 of the 11 signals generated by the Fibonacci Retracement were proven to be accurate, while 43 of the 53 signals generated by the MACD were also proven to be accurate. In conclusion, the buy and sell signals generated by the Fibonacci Retracement and MACD indicators are reliable and effective for use in banking stock trading.

Corporate governance, audit committee, and audit quality are important factors that can influence company performance. Good corporate governance can create an effective internal control system, increase transparency and maintain company accountability. The audit committee, as part of corporate governance, plays a role in overseeing the financial reporting process, evaluating the internal control system, and monitoring external audit performance. High audit quality can increase stakeholder confidence in the company's financial reports and provide guarantees for the accuracy and reliability of financial information. This research aims to analyze the influence of corporate governance, audit committee, and audit quality on company performance. Corporate governance variables are measured using corporate governance scores, while audit committee variables are evaluated based on the characteristics and effectiveness of the audit committee. Audit quality is assessed by the reputation and experience of the external auditor. Meanwhile, company performance is measured using financial ratios such as return on assets (ROA) and return on equity (ROE). This research uses data from companies listed on the Indonesia Stock Exchange (BEI) during a certain period. Data analysis was carried out using the multiple linear regression method to test the influence of independent variables on the dependent variable. It is hoped that the findings of this research will contribute to the development of corporate governance practices, audit committee management, and improvement of audit quality in Indonesia, as well as provide insight for companies in efforts to improve their financial and operational performance.

This research relates to predictions of laptop sales in computer shops in Central Aceh, with a focus on laptop brands Acer, Asus, HP and Lenovo. Over the last three years, sales of these laptops have reached 1,629 units, with a monthly average of between 108 and 150 units. Business owners today prefer brands with the highest percentage of sales, but this can lead to dead stock problems. Therefore, the author proposes using data mining techniques, especially the K-Nearest Neighbor (K-NN) method, to make recommendations for the number of products to be purchased by business owners based on past sales data. The K-NN method requires complete, structured and continuous sales data. It is important to choose an appropriate K value, and other factors such as weather, seasons, promotions, and special events also affect laptop sales. K-NN models may need to be combined with other data to improve prediction accuracy. It is hoped that this research will provide academic benefits in expanding knowledge about the use of the K-NN method in sales prediction, as well as practical benefits for business owners in planning their sales strategies. The research conclusions highlight the importance of good data collection, choosing the right K value, and considering external factors in the laptop sales prediction process.      

This research aims to identify the issues faced by one of the paint distributors in managing their sales information system. The study notes that despite the store's successful sales activities, the utilization of the sales information system remains limited, with the store preferring to manage products without online information handling applications. Based on observations, some weaknesses of the current system include lack of stock data accuracy, hindrances in providing real-time stock information, and limitations in tracking stock changes. The author suggests providing training to users of the web-based sales information system and establishing clear task allocation, as well as system improvements to address existing weaknesses. These suggestions are expected to enhance efficiency and effectiveness in sales management and meet the needs of both administrators and buyers.

This study aims to evaluate the application of a new methodology in investment decision-making, specifically using the regression tree approach on stock market indices. This approach is expected to enhance prediction accuracy and assist investors in making more informed investment decisions, especially in volatile and uncertain markets. Based on the literature review, regression trees offer advantages in identifying non-linear relationships between market variables that are often undetected by traditional models such as the Capital Asset Pricing Model (CAPM). Despite its advantages, the application of regression trees also faces challenges, such as overfitting issues and the need for large and complex data. This study concludes that regression trees can improve investment decision-making, but careful attention is required regarding model tuning and data quality.

The purpose of this study was to determine the effect of ROA, ROE, and DER on profit growth in insurance sub-sector companies listed on the Indonesia Stock Exchange (BEI) in 2018-2022. The technique used for sampling is purposive sampling method with data from 7 insurance companies. This research is quantitative, which is research presented in the form of numbers and statistics. In determining the accuracy of the model that needs to be done is analyzing financial data, then testing several classical assumptions underlying the regression model. The analysis technique used is multiple linear regression analysis.Data analysis and hypothesis testing in this study using Eviews software version 12.0. The results of this study indicate that ROA has a simultaneous positive effect on earnings growth, while ROE and DER have no significant effect simultaneously on earnings growth. The suggestion in this study is that there is a need to improve the company's financial performance in order to increase company profits so that company prices can increase.