Publication Search

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.

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.

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.

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.

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.

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 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.

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 determine the effect of CAR, NPL, BOPO, and LDR on ROA in National Private Commercial Bank companies listed on the Indonesia Stock Exchange (IDX) in 2018-2022. The technique used for sampling is purposive sampling method with data from 5 banking companies. This type of research is quantitative research, namely research presented in the form of numbers and statistics. In determining the accuracy of the model that needs to be done is financial data analysis, then testing some of the classical assumptions that underlie the regression model. The analysis technique used is multiple linear regression analysis.Data analysis and hypothesis testing in this study used Eviews software version 12.0. The results of this study indicate that CAR, NPL, BOPO and LDR partially have a positive effect on ROA. The results of this study also show that CAR, NPL, BOPO and LDR simultaneously have an effect on ROA. The ability of several independent variables to influence the dependent variable is 98.2% and the other 2.8% is influenced by other factors outside of this study.  

This study aims to determine the difference in results in predicting bankruptcy using the Altman Z-Score model and the Springate S-Score model, as well as ineasures which model is most accurate in predicting bankruptcy at retail companies at Indonesia. The population used in this study are retail companies listed on the Indonesia Stock Exchange (IDX) for the 2016-2020 period with a sample of 11 companies. The sampling method used in this study was purposive sampling. The data analysis technique used multiple discriminant analysis. The results showed that there were differences in the results in predicting bankruptcy before and during the pandemic. Based on the two measurement methods used, before the pandemic there were 4 companies that were declared bankrupt and 7 companies that were declared healthy. During the pandemic, 8 companies were declared bankrupt and 3 companies were declared healthy. The bankruptcy model that has the highest level of accuracy is the Springate S-Score.