COVID-19 PROJECTIONS ON JAVA AND BALI ISLANDS INVOLVING VACCINATION AND TESTING INTERVENTIONS USING VARI-X MODEL

The Indonesian government implemented the policy of increasing vaccination and testing of Covid-19 for travel from or to the Java and Bali Islands to reduce the Covid-19 projected spread in there. As participation in these efforts, this study aims to project the Covid-19 spread measured by the active case rates by involving the intervention of vaccination and testing of Covid-19 in the two islands. Projections are performed using a vector of autoregression integrated with the exogenous variables (VARI-X) model. This model is used because it can simultaneously project the Covid-19 spread in the two islands by involving interventions of vaccination and testing of Covid-19 as exogenous variables. The most suitable model obtained is VARI-X (4, 2, 0). The mean-absolute-percentage error (MAPE) of the model for the Java and Bali Islands is 5.3027% and 3.0301%, respectively. Based on the MAPE value, the model is very accurate for projecting the future Covid-19 spread on the two islands. This accuracy can be seen practically from the Covid-19 spread projection results in the next four days, which are very close to the actual data. This research is expected to help the Indonesian government project the spread of Covid-19 on the Java and Bali Islands

Model Autoregressive Moving Average (ARMA) untuk Peramalan Tingkat Inflasi di Indonesia

Salah satu faktor yang mempengarui pertumbuhan perekonomian suatu negara adalah besarnya tingkat inflasi. Pentingnya menjaga kestabilan tingkat inflasi dikarenakan adanya pengaruh negatif terhadap kondisi sosial dan ekonomi negara yang diakibatkan oleh tingkat inflasi yang tinggi dan tidak stabil. Oleh karena itu peramalan dapat dilakukan sebagai salah satu upaya menjaga kestabilan tingkat inflasi. Penelitian ini membahas mengenai penggunakan model deret waktu Autoregressive Moving Average (ARMA) dalam meramalkan tingkat inflasi di Indonesia. Data tingkat inflasi dianalisis untuk menentukan model yang terbaik untuk peramalan. Dengan menggunakan data bulanan tingkat inflasi di Indonesia dari Januari 2016 sampai Desember 2021, diperoleh model terbaik yaitu model ARMA(3,3) berdasarkan nilai Akaike Information Criterion terkecil. Hasil analisis menunjukkan bahwa tingkat inflasi pada bulan Januari 2022 hingga Maret 2022 berada di sekitar 0,2%. Pola grafik hasil prediksi mengikuti pola data aktual sehingga model ARMA(3,3) baik untuk digunakan

Peramalan Return Saham Menggunakan Model Integrated Moving Average

A popular investment that is in great demand among investors is stocked. Stocks are another type of financial instrument offering returns but carrying a higher risk level. Price time series are more difficult to manage than return time series. To equip investors with the knowledge to forecast future stock prices, mathematical models can be used to simulate stock price fluctuations. The time series method, especially the Integrated Moving Average (IMA) model, is a model that can be used to observe changes in stock prices. The Integrated Moving Average (IMA) model will be used in this study to simulate stock returns. The Integrated Moving Average (IMA) model is a Moving Average model that is carried out with a differencing process or an Autoregressive Integrated Moving Average model with a value of Autoregressive being zero. This study uses secondary data simulations from secondary sources, such as data on daily business stock prices for one year, to conduct a literature review and test experiments. The Integrated Moving Average (IMA) model is used in data processing, especially to test the differencing data process. The results obtained are the IMA (1,1) model with the following equation Zˆt = Zt-1 + 0, 5782at-1, which can be used to anticipate future stock returns. Based on these results, it is expected that investors can predict the value of shares within a certain period of time

MCMC Algorithm for Bayesian Heterogeneous Coefficients of Panel Data Model

Panel data models have been applied widely in many subject areas related to economic, social, and epidemiology. In some cases (e.g. epidemiology studies), the phenomena encountered have a complex relationship structured. The risk factors such as house index, healthy behaviour index, rainfall and the other risk factors of particular infectious disease may have different effect on the outcome due to the heterogeneity of cross-section units. The effect of the covariates on outcome could vary over individual and time units. This condition is called as a non-stationary or instability relationship problem. This problem leads to bias and inefficient of the estimators. It is important to examine the heterogeneous coefficients model for avoiding inefficient estimator. We present in detail a statistical estimation procedure of the heterogeneous coefficients for fixed effect panel data model by means of the hierarchical Bayesian estimation approach. The challenges of the Bayesian approaches are finding the joint posterior distribution and developing the algorithm for estimating the parameters of interest. We find that the joint posterior distribution of the heterogeneous coefficients fixed effect panel data model does not follow any standard known distribution form. Consequently, the analytical solution cannot be applied and simulation approach of Markov Chain Monte Carlo (MCMC) was used. We present the MCMC procedure covering the derivation of the full conditional distribution of the parameters model and present step-by-step the Gibbs sampling algorithm. The idea of this preliminary research can be applied in various fields to overcome the nonstationary proble

COMPARISON OF AUTOREGRESSIVE MODEL WITH MISSING DATA TREATED USING ORDINARY LEAST SQUARES AND INTERPOLATION WITH WEIGHTING METHOD

Bandung is committed to contributing to the achievement of the Sustainable Development Goals (SDGs) in Indonesia. One of the efforts that can be made to support the 13th pillar of SDGS regarding climate change is to forecast the air temperature of Bandung City in the future. One of the models that can be used for forecasting air temperature data in Bandung is the Autoregressive (AR) model. Based on BMKG data, often the time series data obtained has missing data. Therefore, in order to do a good time series analysis, it is necessary to make an effort to correct the missing data. The purpose of this research was to examine the procedure for overcoming missing data in the AR model using the Ordinary Least Squares (OLS) method and Interpolation with Weighting, which was applied to forecasting the average air temperature data in the city of Bandung. The research methodology followed the Box-Jenkins 3-step procedure. The first-order AR estimation parameter model was estimated using the OLS method and then used to overcome missing data using both methods with weighting using R software. Both methods resulted in an estimated value of 0.9991 and the same Mean Average Percentage Error (MAPE) value of 2,459% with very accurate criteria. Therefore, to overcome the missing data on the average air temperature data in the city of Bandung with a parameter estimator close to one, we got the same result for both methods

Canonical Correlation Analysis of Global Climate Elements and Rainfall in the West Java Regions

Indonesia has a diversity of climate influenced by several global phenomena such as El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Asian-Australian Monsoon. Continuously climate changing indirectly causes a hydrometeorological disaster. The purpose of this study was to analyze the relationship between global climate elements (ENSO, IOD, Asian-Australian Monsoon) with rainfall in the West Java regions (Bogor Regency, Bandung Regency, Sukabumi Regency, Garut Regency, and Kuningan Regency) simultaneously. The selection of the five regions was based on the natural disaster reports of Badan Nasional Penanggulangan Bencana (BNPB). The research method used was a quantitative research method through one of multivariate analysis technique called canonical correlation analysis. The results of this study indicate that there was a simultaneous relationship between global climate elements, with rainfall in the West Java regions by 0.819. The global climate element and rainfall in the West Java regions that most influenced the relationship were Asian-Austalian Monsoon and Kuningan Regency rainfall

PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL USING DATA MINING APPROACH TO CLIMATE DATA IN THE WEST JAVA REGION

Over a long time, atmospheric changes have been caused by natural phenomena. This study uses the Principal Component Analysis (PCA) model combined with Vector Autoregressive Integrated (VARI) called the PCA-VARI model through the data mining approach. PCA reduces ten variables of climate data into two principal components during ten years (2001-2020) of climate data from NASA Prediction Of Worldwide Energy Resources. VARI is a non-stationary multivariate time series to model two or more variables that influence each other using a differencing process. The Knowledge Discovery in Database (KDD) method was conducted for empirical analysis. Pre-processing is an analysis of raw climate data. The data mining process determines the proportion of each component of PCA and is selected as variables in the VARI process. The postprocessing is by visualizing and interpreting the PCA-VARI model. Variables of solar radiation and precipitation are strongly correlated with each measurement location data. A forecast of the interaction of variables between locations is shown in the results of Impulse Response Function (IRF) visualization, where the climate of the West Java region, especially the Lembang and Bogor areas, has strong response climate locations, which influence each other

Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models

PENERAPAN METODE UNIVERSAL KRIGING (UK) UNTUK PREDIKSI KONSENTRASI PARTICULATE MATTER 2.5 (PM2.5) DI KOTA BANDUNG

The tendency of increasing industrial and population activities in the city of Bandung causes a decrease in air quality. One material that causes air pollution which is very dangerous for health is particulate matter 2.5 (PM2.5). Efforts to control and overcome PM2.5 concentration are carried out through measurements in several locations. However, due to limitations of measuring devices and costs, so not all locations in the city of Bandung can be measured PM2.5 concentration. PM2.5 concentration data tends not to be stationary. Therefore it is necessary to interpolate the PM2.5 concentration data using the Universal Kriging method. In this paper, the Universal Kriging method can be applied to predict PM2.5 concentrations in locations that are not sampled because the observed PM2.5 concentration data are spatial data that have different PM2.5 concentrations at each location. Data were analyzed with the help of software R 3.5.3 and ArcGIS 10.4 to create contour maps. Based on the prediction results of PM2.5 concentrations at locations that were not sampled, obtained a first order trend equation is better used to predict PM2.5 concentrations because it has a minimum Universal Kriging error variance value compared to the second order trend equation. The results showed that the location with high PM2.5 concentration was in the northwest of Bandung City. Contour maps show locations that have low to high PM2.5 concentration

Comparison of distance-based spatial weight matrix in modeling Internet signal strengths in Tasikmalaya regency using logistic spatial autoregressive model

To ensure that national development objectives in rural areas are achieved evenly and sustainably, the Government of Indonesia applies the principles of Village Sustainable Development Goals (SDGs), which are derivative programs of SDGs. One of the indicators in measuring the progress and independence of villages in Indonesia is the availability of cellular phone signal access. Cellular phone signals have a vital role because most internet users in Indonesia rely on mobile data connections from cellular operators. However, the signal emitted by a provider tower has a limited range. According to the data of the Developing Villages Index in 2022, Tasikmalaya Regency is one of the regencies with the highest number of villages that have weak signal strength in West Java Province, Indonesia. To examine the effect of distance and height difference between the placement of the nearest provider tower and the location of the Village Office on the internet signal strength category in Tasikmalaya Regency, Logistic Spatial Autoregressive modeling is needed. In this study, the Bayesian Markov-Chain Monte Carlo estimation method was used, because it has advantages in flexibility and computational efficiency. In spatial modeling, there is a spatial weight matrix determined by the researcher’s understanding of the observed phenomenon. The variable observed in this study is signal strength, which has an orientation at a distance. However, there are several types of distance-based spatial weight matrices, such as K-nearest neighbor, radial distance, power distance, and exponential distance. To determine the most suitable distance-based spatial weight matrix in internet signal strength modeling, the four (4) weight matrices were compared based on the goodness of fit measure models, calculated from the confusion matrix. The results of the analysis showed that the radial distance weight matrix with a threshold distance of d = 1.7km is the most suitable use of distance-based spatial weight matrix in internet signal modeling in Tasikmalaya Regency. The weight matrix exerted a positive spatial autocorrelation effect of 57.141%. In addition, the height difference factor between the location of the provider tower with the location of the village office has a greater effect than the horizontal distance