SHORT COMMUNICATION: COVID-19 Pandemic and Attitude of Citizens in Bandung City Indonesia (Case Study in Cibiru Subdistrict)

In the beginning, the pandemic panicked the people of Cibiru. Over time, the case fell in line with the increasing number of patients recovering. In addition, different views between elements of government make people surrender and believe in the power of nature's creator. Under these conditions, the researchers were interested in learning more. The study was conducted using a descriptive analysis of a number of parties regarding economic and social activities. The results show that there are three important components: First, trust builds the creator and reduces to the government component, communication that a number of parties do not work consistently when responding to COVID-19, and enforcement of unclear rules. In a nutshell. The citizens, grouped into two groups, agree that a pandemic is dangerous and urge them to follow values in the form of existing rules. Also,The pandemic communication competes in a short time and therefore cannot be carried out interactively.The government’s assertiveness of forcing residents to be at home becomes difficult as compensation can be granted for lost opportunities to seek family income Lastly, due to the preparation of the strategy that precedes the arrival of a pandemic, it cannot be face wisely

Application of GSTARI (1,1,1) Model for Forecasting the Consumer Price Index (CPI) in Three Cities in Central Java

Economic development is affected by several factors, one of which is the inflation rate. One indicator used to measure the inflation rate is Consumer Price Index (CPI). The CPI data is recorded simultaneously at several locations over time, produces space-time data. In Central Java Province, CPI is calculated in six regency/cities, so the CPI is affected by the time and other locations named space-time effect. The forecasting methods involve space and time effect simultaneously is GSTAR. This study used the GSTAR model to forecasting the CPI in 3 cities in Central Java, assuming that autoregressive and space-time parameters differ for each location. This study aims to obtain the best GSTAR model to forecast the CPI in three cities in Central Java by using the IDW and NCC weighting. The results indicated that the best GSTAR model for forecast the CPI in three cities (Surakarta, Semarang, and Tegal) was the GSTARI (1,1,1) model. The GSTARI (1,1,1) model fulfils the assumption of homoscedasticity, white noise, and multivariate normal. The MAPE values obtained using the IDW and NCC weighting are 0.2922% and 0.2914%, respectively. From these results, it can be concluded that the best GSTARI (1,1,1) model to forecast the CPI data in three cities in Central Java is NCC weights, as they have a minimum MAPE value . The results of this research can  be used as consideration for the government in making economic policies at the present and in the future

Social Vulnerability and How It Matters: A Bibliometric Analysis

Interdisciplinary and cross-cultural studies of the impacts of environment and social vulnerability must be undertaken to address the problem of social vulnerability in the foreseeable future. Scientist or social scientists should first continuously strive towards approaches can integrate municipal technological expertise, experiences, knowledge, perceptions, and expectations into emergency circumstances, so that people can be sharper on issues and offer responses with their matters. In this paper. We performing the Bibliometric Analysis to review published papers on the keyword 'Social Vulnerability'. There are 29,468 papers published in the last 52 years from 1969 to November 2020. Disaster research by implementing the Internet of Things (IoT), data mining, machine learning is still needed

Sensitivity analysis of the PC hyperprior for range and standard deviation components in Bayesian Spatiotemporal high-resolution prediction: An application to PM2.5 prediction in Jakarta, Indonesia

The Gaussian Markov Random Field (GMRF) is widely acknowledged for its remarkable flexibility, especially in the realm of high-resolution prediction, when compared to conventional Kriging methods. Rooted in the fundamental principles of Bayesian estimation, this methodology underscores the importance of a meticulous examination of prior and hyperprior distributions, along with their corresponding parameter values. Sensitivity analyses are crucial for evaluating the potential impact of these distributions and parameter values on prediction results. To determine the most effective values for hyperprior parameters, an iterative trial-and-error approach is commonly employed. In our research, we systematically assessed a variety of parameter values through exhaustive cross-validation. Our study is focused on optimizing hyperprior parameter values, with a particular emphasis on Penalized Complexity (PC). We applied our method to conduct spatiotemporal high-resolution predictions of PM2.5 concentrations in Jakarta province, Indonesia. Achieving accurate predictive modeling of PM2.5 concentrations in Jakarta is contingent upon this optimization. We identified that the optimal values for PC hyperprior parameters, with a range of less than 2,000 and a hyperprior standard deviation greater than 1 with a 0.1 probability, yield the most accurate predictions. These parameter values result in the minimum mean absolute percentage error (MAPE) of 19.35393, along with a deviation information criterion (DIC) of -154.23. Our findings highlight that the standard deviation parameter significantly influences model fit compared to the relatively insignificant impact of the range parameter. When coupled with high-resolution mapping, these optimized parameters facilitate a comprehensive understanding of distribution patterns. This process aids in detecting areas particularly susceptible to risks, thereby enhancing decision-making efficacy regarding air quality management

Deep learning approaches to predict sea surface height above geoid in Pekalongan

Rising sea surface height is one of the world's vital issues in marine ecosystems because it greatly affects the ecosystems as well as the socio-economic life of the surrounding environment. Pekalongan is one area in Indonesia facing the effects of this phenomenon. This problem deserves to be explored further with complex approaches. One of them is a neural network to perform forecasting more accurately. In neural networks, the time series approach can be used with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By adding the bidirectional method to each of these two approaches, we will find the best method to use to perform the analysis. The best results were obtained by forecasting for 960 days using Vanilla BiGRU. The results can be interpreted from multiple perspectives. The forecasting results showed a fluctuating pattern as in previous periods, so it can be said that the pattern is still quite normal, which indicates that the terminal can continue to operate normally. However, the forecasting results from this study are expected to be a reference for information for the government to prevent future dangers

Individualism, nationalism, ethnocentrism and authoritarianism : evidence from Flanders by means of structural equation modeling

The relationships between individualism, nationalism, ethnocentrism and authoritarianism have been discussed amongst others in the political, philosophical and sociological literature. However, empirical analyses of their interdependencies are still scarce. That is why the main purpose of this thesis is to analyze empirically these interdependencies on the basis of the General Election Study for Belgium in 1991, 1995 and 1999. For that purpose the author uses a continuous time model estimated by means of a structural equation modeling (SEM) approach. The reason for continuous time modeling is that socio-economic processes such as the development of political preferences, are the outcomes of large numbers of decisions taken by large numbers of different actors at different points in time. This basic feature gives rise to continuously evolving socio-economic dynamics rather than to processes that change at specific discrete points in time only. The effects found in discrete time in fact are part of an ongoing process. Particularly, equality at a single point in time may be consistent with quite different auto- or cross-lagged effect functions across time. The reason for using SEM is to account for measurement errors and to handle unobserved variables. Individualism, nationalism, ethnocentrism and authoritarianism are unobserved latent variables that can only be measured by means of observed variables. Toharudin found a positive reciprocal relationship between authoritarianism and ethnocentrism. The relationship from authoritarianism to individualism is positive instead of negative.There is a positive effect from from ethnocentrism to individualism and vice versa. Finally, there are positive impacts from individualism and ethnocentrism on nationalism.

Application of an Empirical Best Linear Unbiased Prediction Fay–Herriot (EBLUP-FH) Multivariate Method with Cluster Information to Estimate Average Household Expenditure

Data at a smaller regional level has now become a necessity for local governments. The average data on household expenditure on food and non-food is designed for provincial and district/city estimation levels. Subdistrict-level statistics are not currently available. Small area estimation (SAE) is one method to address the problem. The Empirical Best Linear Unbiased Prediction (EBLUP)—Fay Herriot Multivariate method estimates the average household expenditure on food and non-food at the sub-district level in Central Java Province in 2020. Meanwhile, for the sub-districts that are not sampled, the estimation of average household expenditure is done by adding cluster information to the EBLUP Multivariate modeling. The K-Medoids Cluster method is used to classify sub-districts based on their characteristics. Small area estimation using the EBLUP-FH Multivariate method can enhance the parameter estimations obtained using the direct estimation method because it results in a lower level of variation (RSE). For sub-districts that are not sampled, the Residual Standard Error (RSE) value from the estimated results using the EBLUP-FH Multivariate method with cluster information is lower than 25%, indicating that the estimate is accurate

A bayesian spatial autoregressive model with k-NN optimization for modeling the learning outcome of the junior high schools in West Java

Increasing the human capital development index of Indonesia is needed to realize the country’s dream to become a developed country in the world. Quality education is needed for that purpose, and this should start from an early age. School is a formal institution for knowledge transfer, which is very useful in building the quality of an Indonesian’s character. Since 2000, Indonesia has made enormous effort to improve the quality of education, which is measured by increased learning outcome, which is measured by mean national examination score. Indonesia has focused on three major aspects, namely, improving equity and access, enhancing quality and relevance, and strengthening management and accountability. These three aspects are translated into eight standards accreditation score. Education quality is believed to have spatial characteristics that follow the Tobler law. In general, schools close to each other, especially in one administrative area, have the same quality characteristics. The spatial characteristics need to be included in modeling the national examination score. Because of the normality assumption problem, we use a Bayesian spatial autoregressive model (BSAR) to evaluate the effect of the eight standard school qualities on learning outcomes and use k-nearest neighbors (k-NN) optimization in defining the spatial structure dependence. We use junior high schools data in West Java. West Java is one of the largest provinces in Indonesia with the highest number of junior schools. The result shows that the national examination score of the junior high schools in West Java is significantly influenced by the standard of graduate competence, and the standard of assessment. We found that the spatial effect also significant which means the average of the national examination score of the nearest schools influences the national examination of the junior high observed

PENERAPAN MODEL SPACE TIME AUTOREGRESSIVE INTEGRATED (STARI(1,1,1)) PADA DATA NTP TANAMAN PANGAN DARI TIGA PROVINSI DI PULAU JAWA

Indikator yang digunakan untuk mengukur kesejahteraan petani tanaman pangan adalah Nilai Tukar Petani (NTP) tanaman pangan. NTP tanaman pangan dipengaruhi oleh waktu dan lokasi. Oleh karena itu, peramalan NTP tanaman pangan dapat menggunakan model Space Time Autoregressive Integrated (STARI). Pada paper ini, model STARI diterapkan untuk data NTP tanaman pangan pada tiga provinsi di Pulau Jawa, yaitu: Jawa Tengah, Daerah Istimewa Yogyakarta (DIY), dan Jawa Timur. Berdasarkan kestasioneran data menunjukkan bahwa data tidak stasioner, sehingga harus dilakukan proses differencing sebanyak satu kali. Identifikasi orde model AR secara univariat berdasarkan plot PACF yang terpotong pada lag 1. Lag spasial yang digunakan pada penelitian ini adalah lag spasial 1, artinya posisi Jawa Tengah, DIY, dan Jawa Timur berada dalam satu wilayah. Oleh karena itu, NTP tanaman pangan dapat dimodelkan dengan model STARI(1,1,1). Penaksiran parameter model STARI(1,1,1) digunakan metode OLS dengan matriks bobot invers jarak. Berdasarkan analisis yang dilakukan diperoleh kesimpulan bahwa model STARI(1,1,1) memenuhi asumsi residual berdistribusi normal multivariat dan white noise. Hasil peramalan NTP di tiga provinsi menggunakan model STARI(1,1,1) menunjukkan pola yang mendekati data aktualnya. Hal ini ditunjukkan dengan nilai MAPE yang diperoleh di tiga provinsi, masing-masing kurang dari 10%. Dengan demikian, model STARI(1,1,1) dapat digunakan dalam meramalkan NTP tanaman pangan di tiga provinsi dan dapat dijadikan bahan rekomendasi kepada instansi terkai