Aplikasi Peramalan Jumlah Siswa Sekolah Dasar di Kabupaten Tanah Laut Menggunakan Metode Holt's Double Exponential Smoothing

Learning processes in the elementary schools at Tanah Laut District is affected by the number of students. Through the number of students can be predicted how much the need of additional teachers, rooms, textbooks and learning medias that support learning processes in the schools. In other words, the infrastructure of the schools can be predicted by the number of students that is registered in Tanah Laut District. The research use Holt's Double Exponential Smoothing method to predicting the number of the prospective students in Tanah Laut District. Mean Absolute Percentage Error (MAPE) technique is used to calculate the percentage of error from the forecasting's result. The system is designed by Entity Relationship Diagram (ERD) and Data Flow Diagram (DFD). The forecasting that have been done said that the number of Tanah Laut's elementary school students at 2018 is 35655 students, with the value of MAPE is about 0.77%, α = 0.77 and β = 0.8

Prediction Active Case of Covid-19 with ERNN

SARS-CoV-2 is known as Covid-19 has been spread in all world since end of 2019. Indonesia, including South Kalimantan has detected first Covid-19 in March 2020. This pandemic has affected in all entirely live in Indonesia. This makes Covid-19 be the main focus of the government. The government has provided aid and imposed restrictions on activities. These policies require planning that can be a solution. Careful planning requires an overview of the data on active cases that are positive for Covid-19. This overview can be obtained through prediction. In this research, Elman Recurrent Neural Network (ERNN) was used to predict active cases of Covid-19. Architecture of ERNN was used ERNN with 3 input nodes, 2 hidden nodes, and 2 context nodes. The data used is 277 data, which is then divided into training data and testing data, respectively 90%-10%, 80%-20%, and 70%-30%. ERNN with a learning rate of 0.1 until 0.9 is applied to data on active cases of Covid-19, then Mean Absolute Percentage Error (MAPE) is calculated to find out performance of model generated by ERNN. The results showed that all of MAPE were below 10% with the smallest MAPE as 3.21% for scenario 90:10 and learning rate 0.6. MAPE value which is less than 10% indicates that ERNN has very good predictive ability