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: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:Bread product sales have become an important aspect of the bakery business, influenced by fluctuations in demand that are not easily predictable. Efficient sales management requires a deep understanding of sales patterns.…
. This study aims to optimize bread product sales by using the K-Means Clustering algorithm to analyze daily sales performance at Toko Roti Amin. The data used includes sales volume and transaction frequency for bread products, consisting of 356 data points. The results show that the bread products can be grouped into three clusters: 129 data in the “Good Sales” cluster, 28 data in the “Moderate Sales” cluster, and 199 data in the “Low Sales” cluster. These findings assist bakery owners in managing stock, production planning, and more targeted marketing strategies. Although there are limitations in using K-Means Clustering, such as dependence on the initial centroid selection, this study proves that applying this technique can enhance inventory management and maximize profit in the bakery business.
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:This research aims to apply the Naïve Bayes method to determine the eligibility of receiving the Smart Indonesia Program (PIP) at the 017107 Kisaran Naga State Elementary School by analyzing 207 student data. The CRISP-DM…
DM approach was used through six stages: business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The variables analyzed included means of transportation, KPS and KIP recipients, worth a pip, reasons for eligibility, number of siblings, distance from home to school, and parents' income.
The results showed that this method achieved 89% accuracy, 85% precision for the positive class, and 92% for the negative class. A total of 125 students (59.9%) were declared eligible to receive assistance, while 82 students (40.1%) did not meet the criteria. The Naïve Bayes method is effective in supporting decision-making for the provision of targeted educational assistance
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:This research is motivated by the large number of prospective students who simply choose a major when they want to enter a vocational school without considering their abilities. The Decision Tree or C45 method is used because…
cause it is able to make decision trees that are easy to describe, and has a level of efficiency in handling discrete and numeric attribute data. While the Naive Bayes method is used because it has a high accuracy of results. This research was conducted based on data from students of SMK Muhammadiyah 10 Kisaran which contained questions about feelings of wrong majors, interests, and determinants of other majors. Data is divided into 2 labels, namely free labels (y) and bound labels (x). Followed by dividing the dataset into training data and testing data with a ratio of 70:30 in both methods to get the level of accuracy. From the results given, it can be seen that the C45 algorithm has an accuracy of 85% and the Naive Bayes algorithm has an accuracy of 26%. This shows that the C45 algorithm is more effective in classifying the available datasets compared to the Naive Bayes.
Abstract:Graduation marks the completion of a certain level of schooling. This study aims to predict the graduation of students at SDN 016528 BP Mandoge based on their abilities. The goal of this research is to reduce the rate of…
student failure to graduate by making predictions based on examination scores collected by the institution. The method used in this study is Naive Bayes, a technique in Data Mining that utilizes probability and statistics to predict future outcomes based on previous data. This method was chosen due to its advantage in predicting graduation rates from concrete data, ensuring the results are reliable and applicable for future predictions. The dataset used in this study includes graduation data for SDN 016528 BP Mandoge students for the 2019/2020 academic year, comprising 171 students, with 120 students used for training data and 51 students for testing data, achieving a model accuracy of 98%.
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.