There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis. This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.
Mitra Program Kemitraan Masyarakat (PKM) ini adalah guru di SMAN 4 Kabupaten Pinrang, yang memiliki masalah kurangnya artikel ilmiah yang diterbitkan oleh guru. Salah satu penyebab guru tidak menerbitkan artikel ilmiah yaitu sebagian besar guru belum memahami tata cara penulisan artikel yang baik. Metode yang digunakan adalah: memberikan workshop pemahaman penulisan artikel ilmiah. Hasil yang dicapai adalah guru memahami teknik-teknik penulisan karya tulis ilmiah pada bidang pendidikan dan mendapat gambaran mengenai teknik pengolahan data dengan menggunakan metode statisika yang baik dan benar.
Survey of career path that students plan after completing their undergraduate in statistics department was carried out through questionnaire on google form from May 6 to May 24, 2021. There were 114 students who filled out the questionnaire, consisting of 20 students from class 2018, 23 students from class 2019, and 71 students from class 2020. Dependent variable is career path plan (Y), while independent variables are tendency to choose career paradigm (CPC), gender (GDR), Grade Point Average (GPA), parental occupation (POC), number of siblings (NOS), place of birth (POB) and year of university entrance (YOE). The data are analysed by binary logistic model with logit transformation and the result is g ( Y ) = ln [ π ( Y ) 1 − π ( Y ) ] = − 13 , 525 + 2 , 332 ( CPC ) − 1 , 036 ( GDR ) + 4 , 466 ( GPA ) + 2 , 421 ( POC 1 ) − 0 , 405 ( POC 2 ) + 2 , 390 ( POC 3 ) + 0 , 236 ( NOS ) − 1 , 817 ( POB ) + 0 , 448 ( YOE 1 ) − 2 , 660 ( YOE 2 ) Results of the analysis show that predictive power of the model to explain tendency of students to choose career according to statistics is around 34% to 56% based on variables in the model. Beside that tendency of career paradigm choice (CPC), Grade Point Average (GPA), parental occupation (POC1 civil servant), place of birth (POB), and year of entry (YOE2 2019) significantly affect chosen career goals by student.
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