Abstract:This study discusses the implementation of a web-based student e-attendance system at Baitussalam Islamic Boarding School using the Scrum method. This system was developed to replace manual attendance recording, which is…
inefficient, error-prone, and complicates the reporting process. The application of the Scrum method aims to ensure that the system development is structured, adaptive to change, and meets user needs. The e-attendance system has key features including multi-level authentication (admin, teacher, and principal), data management for students, teachers, classes, subjects, teaching schedules, semesters, attendance processes, reporting, and teacher performance monitoring. Testing was conducted using the blackbox testing method on eleven main modules. The test results showed that all system functions ran well without major errors. The system has been proven to improve the efficiency, accuracy, and transparency of attendance recording, as well as facilitate reporting. However, the system still has limitations, such as dependence on an internet connection and lack of integration with other systems.
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: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.
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:Education plays an important role in improving the quality of human life, but economic constraints often prevent many students from continuing their education. The Smart Indonesia Program (PIP) was launched to address these…
ese issues by providing educational assistance. However, in its implementation, the selection of PIP recipients at IT Al-Ikhsan Private Junior High School is still inaccurate. This research aims to classify the eligibility of PIP recipients using the Naive Bayes method. This method is applied to student data from the school's Dapodik in 2024 which consists of 265 students. The data is processed through CRISP-DM data mining stages, namely Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. As a result, the Naive Bayes model showed an accuracy of 92.31% with a precision value for the “Yes” class of 89%, recall 100%, and F1-score 94%. In conclusion, variables such as means of transportation, KPS and KIP recipients, parents' income, and distance from home to school affect the eligibility of PIP recipients.
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%.