Indonesia Democracy Index (IDI) Forecasting in 2019 using Moving Average and Correlation Between IDI's Aspect Using Pearson Correlation Coefficient

Faisal Rahutomo, Dimas Rossiawan Hendra Putra, M Bisri Musthofa, Ngat mari

Abstract

Abstract—This experiment aims to analyze the forecasting of the Indonesian Democracy Index (IDI) in 2019, which uses each province data by the Moving Average method. The parameters used in this experiment refer to data obtained from the Central Statistics Agency (BPS) in 2009-2018. The level of achievement of IDI is measured based on the development and implementation of 3 aspects, 11 variables, and 28 indicators. Experiment purposes to find the average percentage of absolute error MAPE (Mean Absolute Percentage Error) for each province and looks for correlations between the three main aspects of forming IDI namely civil liberties, political rights, and democratic institutions. IDI Indonesia's forecasting results in 2019 the IDI has an average value of 68.28 with a MAPE of 4.78%. The results of the correlation between the three aspects of forming the IDI using the Pearson correlation coefficient resulted in the aspect of civil liberties having no correlation with aspects of political rights or aspects of democratic institutions with Pearson values of -0.05 and -0.19. Whereas aspects of political rights correlate with democratic institutions with Pearson's value of 0.48.

Keywords—Forecasting, Indonesian Democracy Index, Moving Average. Pearson Correlation Coefficient

Full Text:

PDF

References

Nurlifa Alfian, Kusumadewi Sri. 2017. Sistem Peramalan Jumlah Penjualan Menggunakan Metode Moving average pada Rumah Jilbab Zaky. Jurnal Inovtek Polbeng. Vol-2. No. 1.

C. Narendra Babu, B. Eswara Reddy. 2014. A moving-average filter-based hybrid ARIMA-ANN model for forecasting time series data. Applied Soft Computing. Elsevier. Vol-23. Pages 27-38.

Dana Indra Sensuse, C. Elin, Wibowo. Wahyu Catur. 2015. Identifying Knowledge Management Process of Indonesian Government Human Capital Management Using Analytical Hierarcy Process and Pearson Correlation Analysis, Procedia Computer Science. Elsevier. Vol-72. Pages 233-243.

Iwan, Eneng Iviq H.R, Yulianto A 2018. Analisa Peramalan Permintaan Mobil Mitsubishi Xpander dengan tiga Metode Forecasting. Cakrawala. Jurnal Humaniora Bina Sarana Informatika. Vol-18 No. 2.

5Shigeru Furuichi. (2014). Operator inequalities among arithmetic mean, geometric mean, and harmonic mean. Jurnal of Mathematical Inequalities. Vol. 8, No. 3. Pp. 669-672.

DOI: https://doi.org/10.20961/jeeict.2.2.41361

Refbacks

  • There are currently no refbacks.