Abstract:Kegiatan pengabdian kepada masyarakat ini berupa pengenalan model pembelajaran untuk materi matematika perkalian bagi peserta didik tingkat Sekolah Dasar. Tujuan dari penggunaan metode pembelajaran ini adalah untuk membantu…
ntu Peserta didik memahami dan meningkatkan prestasi peserta didik pada materi matematika perkalian dengan menggunakan matode gasing dan membuat Peserta didik lebih partisipatif dalam pembelajaran. Khalayak sasaran dalam kegiatan pengabdian kepada masyarakat ini adalah Peserta didik sekolah dasar negeri 100206 Pintu Padang kelas V yang berjumlah 30 Peserta didik. Pelaksanaan pembelajaran dilakukan dengan model Quantum Teaching, yakni guru dan siswa melakukan pembelajaran dengan cara bermain sambil belajar. Metode yang digunakan dalam penyampaian pembelajaran adalah dengan metode Gasing, yaitu dengan melibatkan peserta didik secara aktif dalam pembelajaran. Dalam pembelajaran ini, Pengajar menyampaikan sekilas tentang materi perkalian. Selain untuk mereview kembali ingatan Peserta didik, hal ini juga dimaksudkan untuk mengetahui seberapa dalam pengetahuan Peserta didik pada materi ini. Metode Gasing yaitu pelibatan Peserta didik dalam , dimulai dari membangun pertanyaan yang mengarahkan Peserta didik mendefinisikan sendiri konsep pangkat dua, melibatkan Peserta didik secara aktif dalam pengoperasian media pembelajaran, hingga menilai sendiri jawaban Peserta didik dalam pemberian soal dan menyimpulkan hasil pembelajaran Peserta didik. Manfaat yang dapat diperoleh oleh peserta didik dari kegiatan PKM ini antara lain dapat menciptakan suasana belajar yang berbeda, karena biasanya pembelajaran matematika di sekolah dasar disampaikan dengan ceramah saja, sementara pada pembelajaran ini digunakan media pembelajaran yang mendukung Peserta didik untuk menyentuh, merasakan sendiri dan mencari solusi secara mandiri. Proses pembelajaran yang melibatkan Peserta didik secara langsung dan menyenangkan diharapkan dapat meningkatkan pemahaman Peserta didik dan secara otomatis hasil belajar Peserta didik juga akan meningkat.
Abstract:Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan metode optimal dalam pengembangan riset berkelanjutan. Hasil penelitian menunjukkan efisiensi sebesar 85%.
Abstract:Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan metode optimal dalam pengembangan riset berkelanjutan. Hasil penelitian menunjukkan efisiensi sebesar 85%.
Abstract:Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan metode optimal dalam pengembangan riset berkelanjutan. Hasil penelitian menunjukkan efisiensi sebesar 85%.
Abstract:Penelitian ini bertujuan untuk menganalisis dan mengimplementasikan metode optimal dalam pengembangan riset berkelanjutan. Hasil penelitian menunjukkan efisiensi sebesar 85%.
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: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.