Abstract:Pengabdian ini dengan judul : Pendampingan Capaian Kinerja Kelompok Usaha Bersama Ibu Kreatif Gerai Kembang Setaman Melalui Monitoring Dan Evaluasi” pengabdian ini dengan tujuan Untuk mengidentifikasi capaian kinerja program…
rogram PKM melalui monitoring dan evaluasi di KUB Ibu Kreatif Gerai Kembang Setaman Kota Pekanbaru dan untuk memberikan solusi terhadap kendala yang ditemui dalam melaksanakan program PKM di KUB Ibu Kreatif Gerai Kembang Setaman Kota Pekanbaru. Berdasarkan hasil observasi yang dilakukan oleh Tim PKM permasalahan yang ada di KUB Ibu Kreatif Gerai Kembang Setaman menjadi permasalahan prioritas yang harus dicarikan solusinya agar capaian kinerja dapat berjalan dengan baik, dan tentunya akan membawa perkembangan bagi usaha di KUB Ibu Kreatif Gerai Kembang Setaman Adapun permasalahan prioritasnya adalah: Pertama, Koperasi yang sudah terbentuk di KUB Gerai Kembang Setaman pada tahun 2021 belum berjalan baik karena masih minimnya pemahaman para pengurus dan anggota mengenai koperasi. Kedua Sistem data base persediaan barang yang telah diberikan pada tahun 2022 juga belum berjalan dengan baik karena terdapat kendala di SDM yang tidak begitu memahami penggunaan teknologi. Metode yang digunakan: observasi, diskusi, pendampingan dan dokumentasi kegiatan. Dengan adanya kegiatan Pengabdian ini Tim memberikan beberapa solusi dalam bentuk pendampingan, monitoring, evaluasi dan memberikan bantuan sistem data base,alat mesin kasir serta modal usaha.
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: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: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:Everbright has become one of the distributing companies selling various products, one of which is the Dettol product. In optimizing the stock and marketing of Dettol products, the company faces difficulties in identifying…
g the best-selling products and those less favored by customers. This research data originates from the sales transaction data of Dettol soap in November, comprising 77 sales data. Through the use of data mining, particularly the K-means Clustering method, it becomes a relevant approach to solving this issue. The objective of this research is to avoid excess inventory that remains unsold while meeting the diverse needs of customers. The results of this study show the visualization of the distribution of Dettol soap product clusters and grouping based on sales levels. There were 21 best-selling item data, 31 well-selling item data, and 25 less popular item data, enabling the company to manage inventory based on the best-selling items.
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.