Publication Search

Tea plantations are an important sector in Indonesia's agricultural industry that contributes significantly to the country's economy. The success of tea cultivation is highly dependent on the quality of the seedlings, making data collection and management of tea seedlings a crucial element. However, the manual data collection process still used at PTPN4 faces various challenges such as recording errors, data loss, and information delays, which will have an impact on operational efficiency and decision making. This study analyzes the application of a web-based information system for data collection of tea seedlings in PTPN4 plantations, with a focus on the provision or stocking of seedlings. The results show that a web-based system using the waterfall method can improve the efficiency, accuracy, and transparency of data management, while facilitating integration between units in the organization. Abstrak. Perkebunan teh merupakan sektor penting dalam industri pertanian di indonesia yang memberikan kontribusi signifikan terhadap ekonomi dalam negara. Keberhasilan budidaya tanaman teh sangat bergantung pada kualitas bibit, sehingga pendataan dan pengelolaan bibit teh menjadi elemen krusial. Namun, proses pendataan manual yang masih digunakan di PTPN4 menghadapi berbagai tantangan seperti kesalahan pencatatan, kehilangan data, dan keterlambatan informasi, yang nantinya akan berdampak pada efisiensi operasional dan pengambilan keputusan. Penelitian ini menganalisis penerapan sistem informasi berbasis web untuk pendataan bibit teh di perkebunan PTPN4, dengan fokus pada penyediaan atau stok terhadap bibit. Hasil penelitian menunjukkan bahwa sistem berbasis web dengan menggunakan metode waterfall dapat meningkatkan efisiensi, akurasi, serta transparansi pengelolaan data, sekaligus memfasilitasi integrasi antar unit dalam organisasi.

Structured and efficient inventory management is very important for cooperatives, which have special operational and logistical needs. This study aims to develop a web-based inventory system and also develop it into a mobile application that is able to support real-time stock and asset management, using Javascript, PHP, HTML, CSS, and implemented on the XAMPP platform with phpMyAdmin support. This system is designed to record and monitor goods accurately, optimize the flow of goods in and out, and provide automatic inventory reports. The waterfall approach is applied in the development of this system, starting from needs analysis, design, implementation, to testing. The results of the study show that this system has succeeded in increasing the accuracy of recording goods, accelerating the decision-making process through data-based reports, and reducing manual errors that often occur in inventory management. This system not only supports operational transparency, but also helps a cooperative in achieving logistical efficiency and resource optimization.

This study aims to analyze the effect of accounting policies on the quality of financial reporting of listed companies in Indonesia, with company size as a control variable. In this context, accounting policies refer to the rules and procedures applied by companies in preparing financial statements, while financial reporting quality includes the accuracy, completeness, and transparency of the information presented. This study uses panel data from five companies listed on the Indonesia Stock Exchange during the period 2018 to 2023. The analysis method used is multiple linear regression with t test and F test to test the research hypothesis. The results showed that accounting policies have a significant effect on the quality of the company's financial reporting, while company size has no significant effect.

XYZ is a manufacturing company that produces Yamaha brand motorbike parts such as electrical switches, electrical sockets, lever assembly and horns. Many of the products sent were returned by customers because they did not meet quality standards, for example in March 2024 there were 1,420 pcs out of a total of 19,900 pcs that were returned. This means that companies have to increase production time and costs to replace defective products that are returned because they do not have safety stock. Forecasting is needed to control the production system so that it does not experience over stock and safety stock shortages. This research aims to provide recommendations for forecasting methods that companies can use to minimize the occurrence of production excesses and shortages in the company. Forecasting is done based on historical company sales data for 12 months. The method used is the exponential smoothing method which is then tested whether this method can be used in the future. This calculation uses the help of POM-QM (Production Operation Management – Quantitative Method) software. Calculations are carried out by testing the MAD, MSE, and MAPE values to obtain calculation error values. The results obtained by forecasting the main sw srtg lock assembly product using the exponential smoothing method were 15,708 for demand for the next period, MAD 3.38, MSE 22.84 and MAPE value 26%. Based on these results, the exponential smoothing method can be a recommendation for companies to forecast future demand. This is because the value of forecasting accuracy or MAPE is reasonable. The smallest percentage of MAPE values has a fairly minimal possibility of error in forecasting results.

Stock management is an important aspect in the operations of a grocery store. By knowing when to add stock, shop owners can optimize the availability of goods without excess stock. In this research, the fuzzy logic method is applied to determine stock procurement decisions based on sales data and remaining stock. The results of the research show that fuzzy logic can be used to assist in making stock procurement decisions with a fairly high level of accuracy.

KPRI KOKARNABA Baturraden faces challenges in managing increasingly complex sales data, particularly in identifying the most in-demand products to maximize profit. This study aims to analyze sales patterns using the Naïve Bayes algorithm as a probability-based classification method. The collected sales data were analyzed to identify categories of best-selling and less popular products within the cooperative. The results indicate that the Naïve Bayes algorithm has an accuracy rate of 77.56% in predicting product categories. This research is expected to assist the cooperative in optimizing stock management and improving member satisfaction.

Recording drug stock cards is an important process in managing drug inventory at Batua Health Center, Makassar City. This study aims to evaluate the drug stock card recording system and its effectiveness in inventory monitoring. The methods used include in-depth interviews with informants, direct observation, and document analysis related to drug recording. The results showed that all aspects of the recording system were well fulfilled, reflecting a structured and accurate system, as well as the use of distribution books to verify the suitability of the data. Although the recording system was considered efficient, some informants recommended improvements in the layout of the storage space to increase accessibility. In conclusion, the application of technology in drug stock management needs to be improved to increase the efficiency and accuracy of inventory management at Puskesmas Batua.

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 study aimed to know the prediction of rice sales for Employee Cooperatives Republic of Indonesia Bina Warga Benjeng in the following month. Rice sales are often difficult to predict market demand. When consumer demand increases, rice supplies sometimes suffer from shortages. If consumer demand decreases, stock builds too much and results in a decrease in rice quality. In order for the rice sales process to run smoothly, it is necessary to have a sales prediction so that there are no excesses or shortage in rice supplies. The method of discussion used to predict in this study using the Single Moving Average method which is a prediction method that uses new actual data requests to raise the predictive value of the next month’s demand. The best results were using the Single Moving Average methods using rice sales data variant 25 kg variant were successfully implemented with an RMSE value of 9.3% which means this prediction accuracy of 90.7% accurate.

This community service activity aims to enhance the efficiency of stock management at CV Bina Anak Papua through the development and implementation of a web-based application. The current manual system is prone to errors, data inconsistencies, and inefficient inventory tracking. The proposed web-based application will streamline stock management processes, enabling real-time stock monitoring, automated reorder calculations, and comprehensive reporting. The project involves analyzing the current stock management practices, designing and developing the web application, providing staff training, and evaluating the application's impact on operational efficiency. Expected outcomes include reduced stockouts and overstocking, improved data accuracy, and enhanced decision-making capabilities for stock management. By leveraging web technology, this project aims to boost the competitiveness and productivity of CV Bina Anak Papua, ultimately contributing to the company's growth and sustainability.

MJM is a leading workshop in Malang that focuses on car repairs and is known for its reliable reputation and quality service on Jl. Lake Tondano Raya F5/A26, Sawojajar. These workshops offer a variety of services, from engine to car body repairs, which makes manual transactions difficult and time-consuming due to increased service and cost complexity. This research aims to simplify transaction and operational processes in MJM workshops by developing supporting applications that include recording customer transactions, spare parts stock management, monitoring employee attendance, and mechanic payroll. The information technology provided can improve service quality, optimize operations, strengthen competitiveness, enable a sustainable approach, increase data accuracy, and support more timely decisions. The methodology used is waterfall, which follows a certain flow process starting from requirements analysis, planning, design, software development, testing, to the maintenance stage. The research focus is on the initial development of the system to ensure that the system functions well in the maintenance phase. The Operational Management Information System designed for MJM workshops includes important features such as recording spare parts, purchase and sale transactions for spare parts, service and car wash transactions, stock taking records, management of damaged or lost goods, stock cards, cash flow, and reports. transaction. This system is expected to reduce manual errors, provide better service, and improve overall operational performance.    

In the digital era, the advancement of information technology is crucial for enhancing operational efficiency in retail businesses. This study aims to design and develop a sales and inventory information system with a shelf mapping feature for Toserba Decky, which faces challenges in managing stock and manual transactions. The methodology employed is the Waterfall approach, encompassing requirements analysis, design, implementation, and system testing. Data was collected through direct observation, literature review, and analysis of the store's shelf layout. The findings indicate that the developed system can improve data accuracy, reduce recording errors, and expedite product searching. The implications of this research suggest that an effective information system in managing transactions and inventory can enhance operational efficiency and customer satisfaction. This study recommends regular system updates and technical support to ensure optimal system performance and security.    

Sustainable agriculture faces a major challenge in maintaining the availability and efficient use of fertilizers. Information technology, particularly web applications, has the potential to optimize fertilizer stock management, improve productivity, and enhance household food security for farmers. This study aims to analyze how web technology can improve the efficiency of fertilizer stock management in sustainable agricultural practices. Through literature review and interviews with stakeholders, this research identifies the benefits of web technology in fertilizer stock management, such as improved accuracy in demand forecasting, minimized stock losses, and increased timeliness in fertilizer distribution. The results show that web-based information systems can integrate production data, stock, and fertilizer needs, facilitating decision-making and supporting real-time fertilizer distribution. The implementation of this technology provides significant benefits for farmers, web technology developers, and policymakers, and contributes to efficient and environmentally friendly sustainable agricultural practices.    

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 study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

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.

The oil fuel stock monitoring system (BBM) is becoming crucial to the management of valuable energy assets. The aim of the author is to conduct an effective system analysis and development to monitor BBM stocks. The research uses prototyping methods, which include the following steps: identification of initial needs, initial prototype making, evaluation and feedback, improvement and development, final implementation, and final evaluation. An easy-to-understand user interface design and an efficient database structure are important components of system design. Web-based software development is part of the system implementation. The testing process is carried out to ensure the performance, reliability, and security of the system. The system is regularlyined to remain functional and relevant. It is expected that the results of this practice will make a significant contribution in improving the efficiency and accuracy of monitoring BBM stocks. Moreover, these results will provide the basis for similar system development in the future.  

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.