This study discusses the comparison of forecasting time series data between the Autoregressive Integrated Moving Average (ARIMA) method and the multi input transfer function model. ARIMA method is one of the most frequently used methods for forecasting time series data. Meanwhile, the transfer function model is a combination of the characteristics of multiple regression analysis with the characteristics of the ARIMA time series. Meanwhile, the multi input transfer function model is a transfer function model that has input variables of more than two time series. The application of these two methods is carried out on rainfall data from January 2012 to December 2017 in Manokwari Regency, West Papua Province. The input variables used are temperature, humidity, solar radiation, air pressure, and wind speed variables. The results showed the best ARIMA model was ARIMA (1,0,0) (2,0,0) 12 with an AIC value of 910.07, while for the best multi input transfer function model was ARIMA (1,1,0) AIC value of 898.24. Between the two methods, the best model used to forecast rainfall in Manokwari Regency, West Papua Province is the multi-input transfer function model (1,1,0).
Latent class cluster analysis (LCCA) merupakan salah satu teknik pengelompokan untuk variabel laten. Pengelompokan objek dilakukan menggunakan peluang probabilitas dan berdasarkan model statistik. Variabel laten yang digunakan dalam penelitian ini adalah kesejahteraan rakyat Papua Barat yang digambarkan oleh 15 variabel indikator kesejahteraan rakyat Tahun 2019. Tujuan penelitian ini adalah menentukan jumlah kelompok berdasarkan model terbaik dan karakteristik yang terbentuk sehingga didapatkan gambaran awal tentang kondisi kesejahteraan di Kabupaten/Kota di Provinsi Papua Barat. Penelitian dilakukan dengan berdasarkan nilai Bayesian Information Criteria (BIC) terkecil yaitu 1836,9406 diperoleh model terbaik adalah tiga cluster. Hasil penelitian menunjukkan Kabupaten Manokwari, Kabupaten Sorong Selatan, Kabupaten Sorong, Kabupaten Raja Ampat, Kabupaten Tambrauw, Kabupaten Manokwari Selatan, dan Kabupaten Pegunungan Arfak berada dalam cluster satu; Kabupaten Fakfak, Kabupaten Kaimana, Kabupaten Maybrat dan Kabupaten Teluk Wondama berada dalam cluster dua dan Kabupaten Bintuni dan Kota Sorong berada dalam cluster tiga.
Panel Data Regression Analysis is a combination of time series data and cross section data. The purpose of this study is to determine the best model for panel data regression analysis on HDI in West Papua Province and to determine the HDI model in West Papua Province. The data used in this study are West Papua data in the 2019 Publication Figures and 2019 Publication Human Development Index data. In the process of determining the best model, estimating model parameters with 3 approaches namely CEM, FEM and REM, then testing model selection, classical assumption test, model equation checking and finally model interpretation. The results of this study indicate that the best regression model is FEM with individual effects and time effects with a good model of 91% which means that HDI in West Papua Province is explained by GRDP, RLS, JPM and UHH. The equation model is as follows:
Based on the equations that have been obtained, the variables that have a significant effect on HDI in West Papua Province are RLS and UHH.
The developments research in the cluster analysis using the fuzzy method. The fuzzy method allocates to each group with membership value located at interval [0, 1], showing the magnitude of the possibility of an object being a member into a particular group. Outlier in data very important known before grouping, because affect the final result. Grouping by using the mean value as the center of the group will be more sensitive than using the median value, so this research applies fuzzy c-means and fuzzy c-medoid method to the grouping of villages in Sorong Regency Year 2016 based on the underdevelopment status and examine the goodness of both methods. There are 23.2% of villages that do not change when done grouping with both methods. Overall average distance of group center object and varians in the resulting group the two methods are the same, the varins between groups of fuzzy c-means is greater than the fuzzy c-medoid method.
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