Purpose: Indonesia is facing an increasing occurrence of non-communicable diseases (NCDs) every year. We assessed the modifiable, non-modifiable, and physiological risk factors of NCDs among the Indonesian population. Methods: Secondary data was analyzed from the 2018 Indonesian basic health research (RISKESDAS). The national survey included participants aged 15– 54 years and obtained 514,351 responses. Linear systematic two-stage sampling was conducted by RISKESDAS. Furthermore, chi-square and binary logistic regression were utilized to explore the determinant of NCDs with a significance level of 95%. Results: We found that almost 10% respondents in Indonesia had NCDs. We observed that depression has a higher odd (aOR: 2.343; 95% CI: 2.235– 2.456) contributed to NCDs and followed other factors such as no education (aOR: 1.049; 95% CI: 1.007– 1.092), passive smoking (aOR: 0.910; 95% CI: 0.878– 0.942), fatty food (aOR: 1.050; 95% CI: 1.029– 1.073), burnt food (aOR: 1.033; 95% CI: 1.005– 1.062), food with preservatives (aOR: 1.038; 95% CI: 1.002– 1.075), seasoned food (aOR: 1.057; 95% CI: 1.030– 1.084), soft drinks (aOR: 1.112; 95% CI: 1.057– 1.169), living in an urban area (aOR: 1.143; 95% CI: 1.119– 1.168), living in central Indonesia (1.243; 95% CI: 1.187– 1.302), being female (aOR: 1.235; 95% CI: 1.177– 1.25), and obese (aOR: 1.787; 95% CI: 1.686– 1.893). Conversely, people in Indonesia who undertook vigorous activity (aOR: 0.892; 95% CI: 0.864– 0.921), had employment (aOR: 0.814; 95% CI: 0.796– 0.834), had access to improved sources of drinking water (aOR: 0.910; 95% CI: 0.878– 0.942), and were aged 35– 44 years (aOR: 0.457; 95% CI: 0.446– 0.467) were less likely to develop NCDs. Conclusion: Modifiable, non-modifiable, and physiological risk factors have a significant influence on NCDs in Indonesia. This finding can be valuable information for Indonesian Government to arrange a cross-collaboration between government, healthcare workers, and society through advocacy, partnership, health promotion, early detection, and management of NCDs.

Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Although the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This article investigates the potential stratospheric sulphate aerosol injection (SAI) to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models. We applied a simple downscaling method and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We evaluated changes in geoengineering model intercomparison project (GeoMIP) experiment G4, an SAI experiment in 5 Tg of SO₂ into the equatorial lower stratosphere between 2020 and 2069, concurrent with the RCP4.5 emissions scenario. G4 is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. The spatial response of changes forced by RCP4.5 scenario and G4 are notably heterogeneous in the archipelago, highlighting uncertainties that would be critical in assessing socio‐economic consequences of both doing, and not doing G4. In general, SAI has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability. G4 is also effective at reducing the likelihood of WBT > 27°C events compared with RCP4.5 after some years of SAI deployment as well as during the post‐termination period of SAI. Regional downscaling may be an effective tool in obtaining policy‐relevant information about local effects of different future scenarios involving SAI.

Solar radiation management (SRM), or solar geoengineering, reduces the earth’s temperature by reflecting more sunlight back to space. However, the impacts of SRM remain unclear, making it difficult to project the benefits as well as consequences should this approach be adopted to combat climate change. To provide novel insight into the SRM impact on hydro-climatic extremes in Southeast Asia, this study conducts a simulation experiment for the Kelantan River Basin (KRB) in Malaysia by incorporating three bias-corrected Stratospheric Aerosol Geoengineering Large Ensemble (GLENS) members into the Soil and Water Assessment Tool Plus (SWAT+) model. The study found that SRM practices could generate substantial cooling effects on regional temperatures, leading to a reduction in projected annual precipitation and monthly precipitation during the flooding season (from November to mid-January) under SRM relative to the Representative Concentration Pathway 8.5 (RCP8.5) scenario. In addition, SRM could reduce the number of days with heavy precipitation as well as the intensity of maximum daily precipitation as compared to RCP8.5, during the 2045–2064 and 2065–2084 periods, leading to a reduction in high flows. Nevertheless, under SRM impacts, the driest months from February to May would experience comparable decreases in monthly precipitation and streamflow.

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

East Nusa Tenggara Province is one of the most vulnerable regions in Indonesia to drought. Drought prediction is definitely needed as a mitigation action to minimize the risk of drought. However, a sparse dataset has led to difficulties in accurately predicting future droughts in areas without meteorological stations, and hence a dataset with a finer resolution is required. This research investigates the performance of a 3-month Standardized Precipitation Index (SPI) derived from the Tropical Rainfall Measuring Mission (TRMM) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) to predict drought. CART and Random Forest are applied as the classification methods. Using several predictors, the analysis finds that CART has lower predictability than Random Forest. The average accuracy of the prediction using Random Forest reaches 100% with an average Area Under Curve (AUC) of about 0.8. The analysis also shows that predictions using the MERRA-2 dataset lead to higher accuracy and AUC than those using the TRMM. Therefore, using the MERRA-2 dataset predicted by Random Forest can be an optimal way to predict drought in East Nusa Tenggara. The methods confirmed that average soil surface temperature (day and night), Multivariate ENSO Index (MEI), Arctic Oscillation Index (AOI) and Normalized Difference Vegetation Index (NDVI) are strong predictors of drought. The performance of CART and Random Forest is improved with the Synthetic Minority Over-Sampling Technique (SMOTE). The techniques described: •translate drought information and predictors of drought into a base classifier that optimizes the AUC;•allow drought to be predicted for many grid points efficiently and with high accuracy; and•are computationally efficient and easy to implement.