In the era of the industrial revolution 4.0, quantity is not a measure of achieving early childhood outcomes, but how teachers create quality resource based inventions. To form creative and adaptive resources for technology, the teacher must changes the facilities, infrastructure and learning reconstruction. Learning that is prepared to welcome children to face the 21st century is learning based on Science, Technology, Engineering, Art, and Mathematics (STEAM). STEAM is used to consider the interconnected essence of science, technology, engineering, arts and mathematics disciplines and their significance in the long-term academic performance of children. Responding to STEAM-based learning that PAUD teachers need to incorporate in cultivating the creativity of children, it is important to know the degree to which PAUD teachers understand STEAM-based learning. This research discusses the use of the X-Means clustering algorithm as one of the data mining algorithms in grouping data on the level of comprehensions of PAUD teachers on STEAM-based learning. This research discusses the use of the X-Means Clustering algorithm as one of the data mining algorithms in grouping data on the level of comprehensions of PAUD teachers on STEAM-based learning.
Children are not allowed to visit the hospital. Children should not visit the hospital for two reasons, namely the patient's side and the child's side. On the patient's side, patients need peace of mind during treatment and recovery. The noise generated by children makes the atmosphere not conducive and increases the patient's stress level. On the child's side, there are two factors, namely immunity, and trauma. Children have incomplete immunity so they are easily infected by viruses and bacteria. A child's immune disorder will harm the child's development. Apart from viruses and bacteria, in hospitals, there are also patients with major injuries such as those resulting from accidents. Children who see these large wounds can traumatize themselves and interfere with the child's growth and development. The age classification of visitors supports for hospital management to limit visitors based on age. Visitors categorized as children are visitors aged 12 years or younger. The method used for age group classification is the pre-trained CNN, including Alexnet, VGGNet, GoogleNet, ResNet, and AqueezeNet. We conducted a preliminary study using the All-Age-Faces (AAF) dataset as test data that represents the age of hospital visitors. The dataset is divided into two classes, namely children and adults. Based on the SqueezeNet test, it is a better method in terms of training accuracy and validation. Based on the order of accuracy validation, SqueezeNet succeeded in recognizing age groups with an accuracy of 93.09%, VGGNet 92.72%, AlexNet 91.44%, GoogleNet 90.92%, and ResNet 90.62%. This research is expected to contribute to helping control visitors to the hospital.
Angga Motor merupakan sebuah usaha bengkel yang berlokasi di kota Medan dan bergerak di bidang usaha penjualan sparepart sepeda motor serta melayani jasa service sepeda motor. Dalam menjalankan usahanya, pihak Angga Motor harus memperhatikan perencanaan dan pengendalian persediaan sparepart yang dibeli oleh pelaggan. Jika permintaan pembelian sparepart sepeda motor meningkat, maka dapat mengambil keputusan untuk menambah stok sparepart sepeda motor agar permintaan pelanggan selalu terpenuhi. Apabila hal ini tidak dikelola dengan baik, sistem persediaan sparepart dapat menjadi tidak efektif. Oleh karena itu, maka perlu dilakukan penelitian mengenai prediksi penjualan sparepart sepeda motor dengan menggunakan metode Trend Moment. Prediksi merupakan cara untuk mencari nilai-nilai yang akan datang berdasarkan pada nilai-nilai yang diketahui sebelumnya. Hasil prediksi penjualan sparepart sepeda motor jenis Kanvas Rem untuk periode bulan Januari 2020 dengan menggunakan metode Trend Moment dan dipengaruhi oleh indeks musim yaitu cenderung stabil atau mengalami trend positif dimana hasilnya sebesar 2 unit, dengan nilai error MAPE sebesar 0,002365 %. Sedangkan total nilai error MAPE hasil prediksi dari bulan Januari 2020 sampai Desember 2020 sebesar 0,1440 %. Hasil prediksi untuk sparepart Ban sebanyak 3 unit dengan total nilai error MAPE sebesar 0,1337 %, sparepart Aki sebanyak 3 unit dengan total nilai error MAPE sebesar 0,1224 %, sparepart Oli Mesin sebanyak 2 unit dengan total nilai error MAPE sebesar 0,1288 %, sparepart Lampu sebanyak 3 unit dengan total nilai error MAPE sebesar 0,1352 %, sparepart Kanvas Kopling sebanyak 2 unit dengan total nilai error MAPE sebesar 0,1440 %, dan sparepart Spark Plug sebanyak 2 unit dengan total nilai error MAPE sebesar 0,1484 %.
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