Abstract:Sales forecasting is a crucial component of operational strategy and inventory management in the culinary industry, particularly for businesses dealing with raw materials that have short shelf lives. The Sate Padang Hapis…
s business faces the challenge of unpredictable monthly fluctuations in consumer demand, which often lead to supply imbalances, overproduction, or lost profit opportunities due to stockouts. This study aims to implement and analyze the accuracy of the Single Moving Average (SMA) quantitative forecasting method in predicting Sate Padang Hapis sales volumes for June 2026. Model performance was evaluated by assessing mathematical accuracy across two time-interval variations: 3-month and 5-month moving averages. Projection error rates were rigorously measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) parameters. The analysis reveals that the Single Moving Average model with a 5-month interval yields projections closest to actual data, achieving the lowest error rates (MAD: 28.00; MSE: 1,304.00; MAPE: 2.28%). Implementing this forecasting model provides management with an objective basis for decision-making, enabling the effective and efficient optimization of raw material logistics and supply management.
Abstract:Signal interference or noise is one of the main problems in data transmission that can reduce information quality in both analog and digital systems. This study aims to visually analyze the effects of noise using audio software.…
oftware. The method used is simulation-based, where a pure signal is used as an initial reference before being subjected to various levels of interference. The analysis results show that in analog signals, noise causes permanent waveform distortion that is difficult to recover. In contrast, digital signals tend to maintain data integrity as long as the interference does not exceed a certain threshold. These differences in signal characteristics can be visually observed through waveform displays in the software. The results indicate that digital systems have advantages in maintaining signal quality in noisy environments and have the potential to be used as educational media for basic telecommunication and signal processing studies.
Abstract:Alumni data management is an important aspect for educational institutions, especially vocational high schools that focus on graduates’ readiness for the workforce. SMK Negeri 1 Kisaran still faces problems in managing alumni…
alumni data and tracking career paths due to manual and unintegrated processes. This study aims to implement a web-based alumni and career tracking information system to improve the effectiveness of alumni data management, facilitate communication between the school and alumni, and support graduate career tracking. The research methods used include observation and interviews with school staff and alumni. The system was developed using PHP programming language and MySQL database, with system design based on Unified Modeling Language (UML). The results show that the developed system is able to manage alumni data centrally, provide career path information, and generate accurate and accessible alumni reports. Therefore, this system can serve as an effective solution to improve alumni information services at SMK Negeri 1 Kisaran
Abstract:This study aims to develop a web-based information system to improve the management of inventory and equipment borrowing at the Network Laboratory of Universitas Royal Asahan. The system is designed to replace manual procedures…
cedures that are prone to data errors, duplication, and time inefficiency. The system development adopts the Waterfall methodology, which includes requirement analysis, UML-based system design (Use Case, Class, Activity, and Sequence Diagrams), implementation using PHP, MySQL, and the CodeIgniter framework, as well as functional testing through the black-box method. The results show that the system provides core features such as inventory management, borrowing transactions, and automated reporting. System testing indicates improved data accuracy, a 70% increase in search efficiency, and enhanced transparency in laboratory asset management. Overall, the system enables a more organized, accountable administrative process and supports the campus digitalization program.
Abstract:Academic achievement is a measure of students' learning outcomes, encompassing aspects of knowledge and skills. Academic performance serves as a crucial indicator in evaluating students' learning progress. MAS Al-Wasliyah…
h Petatal is committed to providing quality education but still faces limitations in applying technology to evaluate student learning. The current evaluation process relies on teachers' subjective assessments, which restricts the information about students' progress. Therefore, the implementation of machine learning is proposed as a solution to enhance objectivity in student learning evaluation through more effective data processing. The method used is the K-Means Clustering algorithm, which can group or classify data based on specific patterns. This study aims to evaluate the extent to which machine learning can process student learning evaluation data through the analysis results obtained from the clustering process, which are then used as benchmarks to improve the evaluation system and provide feedback for students needing improvement in their academic performance. The data used comprises students' grades from the odd semester of the 2024/2025 academic year, with a total of 210 data points. The clustering results produced three clusters: the "good" cluster with 60 students, the "average" cluster with 99 students, and the "low" cluster with 51 students.
Abstract:Technological advances support digital transformation in sales data management. Wan Toys & Sport stores face difficulty understanding sales patterns, such as the highest sales months and most popular products. This research…
rch uses the K-Means clustering method with the CRISP-DM approach to group sports products based on their sales level. The analysis results show that this method is able to divide products into three categories: high, medium and low, thus providing strategic insight for stock management and marketing. Products with high sales are prioritized for stock, while products with low sales are targeted for promotion. This method effectively supports operational efficiency and data-based decision making at Wan Toys & Sport stores.
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Abstract:PDAM Tirta Kualo is a regional company that supplies clean water to the surrounding community. Accurate estimation of water bill amounts is crucial to assist PDAMs in managing resources and finances efficiently. This study…
dy aims to create a prediction model for total water bills using the linear regression method. The data used is historical customer billing data which is analyzed to identify the relationship between air usage volume and total billing.
The findings show that a simple regression model can describe the water bill amount with an impressive accuracy of 0.9926. This precise model allows it to be used effectively in PDAM financial planning and assists customers in estimating their water usage.
Abstract:The development of information technology provides solutions for increasing efficiency and accuracy in decision-making, such as in determining students eligible for BLT at SD Swasta IT ABI Husni. This study aims to implement…
ment the Naive Bayes algorithm to support a more objective BLT recipient selection process. The method used is CRISP-DM, starting from understanding the problem, data preparation, to model implementation. The data analyzed included type of residence, KPS recipients, parents' income, KIP recipients, number of siblings, distance from home and reasons for eligibility for BLT used were data from students of SD Swasta IT ABI Husni in the odd semester of 2024/2025, with a total of 137 data. The results of the study showed that the Naive Bayes algorithm was able to achieve an accuracy level of 98% with precision and recall of up to 100%, proving the effectiveness of the model in minimizing classification errors. In conclusion, the use of the Naive Bayes algorithm can help make decisions that are more targeted, transparent, and fair in the distribution of BLT.
Abstract:The use of smartphones in Indonesia has been steadily increasing each year. In the era of the Fourth Industrial Revolution, smartphones have become a lucrative business sector, leading to intense market competition. Consequently,…
equently, smartphone companies must pay closer attention to the market segmentation desired by consumers. Data mining is the process of discovering significant relationships and patterns by analyzing large datasets using statistical and mathematical techniques. This study aims to identify and analyze the market segments of Android smartphone users among students at STMIK Royal. The data used in this research were collected from 122 student respondents. The study employs clustering using the K-means algorithm. The resulting data modeling will categorize market segments into several clusters. This segmentation yields three clusters: Cluster 1 (features), consisting of 36 respondents who prioritize price, battery, camera, and warranty; Cluster 2 (product), with 49 respondents who value all attributes except warranty; and Cluster 3 (superiority), comprising 37 respondents who prioritize camera, brand, and RAM.
Abstract:The independent curriculum provides opportunities for educators and students at SMP Muhammadiyah 51 Sidikalang to obtain information. Non-formal education can be obtained by participating in various activities, one of which…
ich is socialization outside of school. This socialization provides additional knowledge, it turns out that mathematics and biology can be connected, for example regarding learning Biomathematics which can help model populations from the spread of disease, in this case the model for the spread of Covid-19. The aim of this event is so that educators and students can apply and model the spread of disease and read real phenomena through data. The method used in this event is assistance which helps in explaining the modeling of the Covid-19 disease and analyzing the data so that conclusions can be drawn on the spread of the population so that the spread can be stopped. The results of this event were that 85% of educators and students were able to take part in this socialization by being able to form and model in a simple way the formation of disease models both by analyzing data.