Peramalan Curah Hujan di Kota Bandung dengan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

The need for future rainfall information, modeling and forecasting is important. The forecasting method is a method used to predict future conditions based on past data. Rainfall data is time-series data in the form of seasonality, a pattern that repeats at fixed time intervals, so the authors use the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, which is appropriate for data with seasonal characteristics. The author takes monthly rainfall data in Bandung city for the period January 2016 to December 2021 to forecast rainfall in Bandung city for next year. After calculations using the SARIMA method, the best model for forecasting rainfall in the city of Bandung is then obtained, namely the SARIMA model (0,0,0)(0,1,1)12

Determinants of Greenhouse Gas Emissions in the Transportation Sector in Indonesia: Official Statistics and Big Data Approach

The Covid-19 pandemic has affected every aspect, including the greenhouse gas emissions from the transportation industry. The adoption of Lockdown during the Covid-19 outbreak has decreased greenhouse gas emissions in the transportation sector. Studying the variables that affect the transportation sector's greenhouse gas emissions during the COVID-19 pandemic is particularly fascinating. Big data and official statistics were combined to create the data for this study.  Official statistics are sourced from Statistics Indonesia (BPS) and the National Development Planning Agency (BAPPPENAS) while big data is sourced from the google mobility index. Based on the results of the generalized linear model with the gamma link, it can be concluded that the growth of GRDP per capita and the mobility of people to workplaces have a negative effect on greenhouse gas emissions in the transportation sector, mobility of the population to groceries and pharmacies has a positive effect on greenhouse gas emissions in the transportation sector, while people's mobility to recreation and retail has no effect on greenhouse gas emissions in the transportation sector. During the Covid-19 pandemic, population mobility to Workplaces which showed reduced work from an office (WFO) and increased work from home (WFH) had the greatest influence on reducing greenhouse gas emissions in the transportation sector. Work from home (WFH) can be used as a solution to reduce greenhouse gas emissions in the transportation sector at the beginning of the Covid-19 endemic

Assessing service availability and accessibility of healthcare facilities in Indonesia: A spatially-informed correspondence analysis with visual approach

A nation's health status can be determined by the availability of healthcare services, which is a crucial part of human life. Since 2011, health facilities in Indonesia have been acknowledged as an important health indicator. This study uses correspondence analysis and spatial visualization to look at the primary healthcare facilities in each region of Indonesia. The analysis makes use of information from Indonesia's province-level data on the number of Regions with health facilities in 2021, along with six different types of medical facilities: hospitals, maternity hospitals, polyclinics, health centers, sub-district health centers, and pharmacies. To show the spread of medical facilities in Indonesia, a spatial representation is also produced. In comparison to provinces on other islands, the analysis reveals that the provinces on Java Island have a more varied and adequate distribution of healthcare facilities. Health facilities on other islands' provinces, however, are only focused on public health and sub-district public health. The spatial representation gives a clear picture of the distribution of medical services and draws attention to the distinctions across Indonesia's regions and islands. The geographical visualization offers a thorough perspective of the distribution of health care facilities, and this study delivers insightful information about how health care facilities are distributed in Indonesia. Future research and policy decisions targeted at enhancing Indonesia's healthcare system can be informed by these findings

Unraveling educational networks: Data-driven exploration through multivariate regression, geographical clustering, and multidimensional scaling

Enhancing rates of school participation holds significant importance for a nation’s educational achievements. This research employs a comprehensive approach that combines various methodologies, including multivariate regression analysis, geographic categorization, and multidimentional visualization, to examine the factors influencing school enrollment in Indonesia. Through the integration of diverse data sources, we investigate the connections among variables such as economic status, school accessibility, educational quality, and societal considerations concerning enrollment rates. This discrete impact of each factor on enrollment variations is analyzed through multivariate regression. Geospatial clustering analysis reveals enrollment trends in different regions, while multidimensional visualization untangles the intricate interplay of influencing factors. This holistic approach facilitates a nuanced comprehension of these dynamics within Indonesia’s varied geographical and society offering guidance in the formulation of more efficient strategies to improve school attendance, tackle enrollment disparities, and advocate for inclusive education based on fundamental determinants

Negative binomial mixed model neural network for modeling of pulmonary tuberculosis risk factors in West Java provinces

Tuberculosis (TB) is still a major public health concern in many regions of the world, including Indonesia's West Java Provinces. Accurate TB risk factor prediction can enhance overall TB control efforts by directing focused therapies. In this study, utilizing a combination of Negative Binomial Mixed Models (NBMMs) and Feed-Forward Neural Networks (FFNNs), we offer a unique method for the predictive modeling of TB risk variables. A variety of sociodemographic, behavioral, and environmental factors that are known to be linked to TB are included in the dataset utilized in this investigation. To correct for overdispersion and include both fixed and random effects in the model, we first fitted an NBMM major problem in epidemiological investigations is modeling count data with overdispersion, and the NBMM component of the model offers a versatile and effective framework for doing so. Following that, we include an FFNN component in the model, which helps us to detect relevant predictive features and alter the model's weights accordingly. Backpropagation methods are used by the FFNN to adjust model parameters and enhance accuracy. The resulting Negative Binomial Mixed Model Neural Network (NBMMNN) model has a high accuracy value of up to 0.944. Our research suggests that the NBMMNN model outperforms conventional models that are frequently used to predict TB risk factors. By contrast to simpler models, the NBMMNN model can capture complicated and nonlinear interactions between predictors and outcomes. Additionally, the inclusion of random variables in the model enables us to take into account potential sources of variability in the data as well as unmeasured confounding. This work emphasizes the opportunity to enhance TB risk prediction and control efforts by integrating NBMMs with FFNNs. In West Java Provinces and other comparable contexts, the NBMMNN model might be a helpful tool for identifying and resolving TB risk factors, guiding targeted interventions, and enhancing overall TB control efforts

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