Suchergebnisse

Kemiskinan merupakan sebagian dari masalah pembangunan yang berkaitan dengan berbagai dimensi yang meliputi sosial, ekonomi, budaya, politik, regional dan waktu.Penelitian ini bertujuan untuk menilai ukuran sampel dari ukuran kemiskinan moneter yang digunakan oleh Badan Pusat Statistik Indonesia (BPS) dan juga untuk mencari solusi alternatif dalam mengestimasi ukuran kemiskinan moneter dalam contoh berukuran kecil. Metode yang digunakan dalam penelitian ini adalah estimasi langsung yang dilengkapi dengan simulasi yang bertujuan untuk mengevaluasi ukuran sampel dalam perhitungan estimasi pengukuran kemiskinan dan Bayes empiris sebagai solusi alternatif dalam mengestimasi ukuran kemiskinan moneter dengan ukuran sampel kecil. Hasil penelitian ini menunjukkan nilai estimasi memiliki varians kecil dan tidak bias jika ukuran sampel yang digunakanbesar, dan sebaliknya. Ini terbukti dalam perbandingan ukuran sampel, seperti yang ditunjukkan padaperilaku indeks bias Relative Bias (RB), Absolute Relative Bias, dan Relative Mean Square Error (RMSE). Dengan demikian, estimasi langsung dapat dikoreksi dengan estimasi Bayesian empiris dalam menangani masalah ukuran sampel yang kecil.

Rice is one of the main foods in Indonesia. A change of rice price will cause a major effect in the lives of consumers. On the other hand, there are so many factors that influence the rice price. Thus finding key factors which are significant to the rice price, as well as forecasting the consumer's rice price are needed in order to maintain the stabilization of rice price. The second objective is to find key factors which influence the rice price by using multiple linear regression models. The parameters were estimated by ordinary least square methods. There are 6 variables that are significant at α=5%, which are the consumer's rice price at the previous period, rice production at the current and previous period, farmer's GKP price, realization of domestic stock, and total rice import. The rice price will increase if the GKP price and realization of domestic stock increase whereas total rice import and the consumer's rice price at the previous period have negative influences towards the rice price. The impact of imported rice is negative towards domestic rice. This condition will also drive negative effect towards the farmer's income, in this case the price does not meet the farmers cost for production. To protect the farmers, the government applied a 430.00 Rp/Kg imported rice fee but this is not effective to decrease the amount of imported rice. In this model rice production at the current and previous period have positive signs, contradictory to the microeconomic theory where when the rice production increases, there will be an excess supply and the price will drop. That condition will occur only if the commodity is a free commodity and the rice is at the sufficiency level but in Indonesia, rice is affected by the government's policy and the rice productivity is left behind by the demand. Forecasting the consumer's rice price for the next five years was the last objective of this research. ARIMA Box–Jenkins Method, X-12 ARIMA, Winter's Method, and Trend Analysis were compared to find the best statistical model to forecast the consumer's rice price. X-12 ARIMA turns out to be the best method because it has the smallest MAPE, MAD, and MSD value. This result is a satisfactory because according to Findley et al. (1998) X-12 ARIMA has the capability to adjust seasonal and trading day factors which usually causes fluctuations in an economic time series data. Keyword : X-12 ARIMA

Economic growth in the first quarter of 2021 based on YoY (Year on Year) is around -0.74%. This figure caused the Indonesian economy to recession after contracting four times since the second quarter of 2020. With positive and negative growth in the value of GDP for each category based on the business sector each quarter, can do future economic growth modelling. The prediction results can be used as an early warning for the government on factors that can maximize and factors that must improve. This study aims to predict the state of economic growth in the next quarter using Random Forest classification. Random Forest combines tree classification and bagging by resampling the data, which reduces the variance of the final model, which is for low variance overfitting. The data used in this study was scrapped from January 2021 to March 2021 on 5 Indonesian online news portals, namely Kompas, Antara, Okezone, Detik, and Bisnis. The independent variable is online news based on GDP category. The dependent variable results from data labelling on each news, up or down, carried out by the Directorate of Balance Sheet of BPS. Based on the calculations with cross-validation of 10, the modelling results obtained 96.51% accuracy, 97% precision, and 97% recall. The random forest method is good for predicting economic growth in the next quarter, namely the second quarter of 2021. Incorrectly predicted only three categories of GDP were: the construction category, the transportation and warehousing category, and the company service category