Losses experienced by the Indonesian government due to floods are predicted. It is because of the significance of population growth, closure of water catchment areas, and climate change in many regions in Indonesia. The government has tried to reduce the risk but faces insufficient funds. Therefore, new innovative funding sources are essential to overcome these limitations. One way to obtain it is through issuing Flood Catastrophe Bonds (FCB). Unfortunately, Indonesia has had no FCB price estimate until now. On the basis of this problem, this study aims to estimate the FCB price in Indonesia. The primary method used is the approximation method of the aggregate loss distribution. This method can compute the aggregate flood loss cumulative distribution function value faster. The FCB fair price estimation results are cheap because the risk of the instrument is significant. This significant risk is also proportional to the large return. Finally, further analysis shows that in Indonesia, the attachment point of the FCB has a relationship that is in line with the price, while the term of FCB does not. This research is expected to assist the Indonesian government in determining the fair price of FCB in Indonesia. This research can assist the investors in choosing FCB based on expected return, attachment point, and the term they want.

COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.

Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.

This article briefly explains the simultaneous spatial durbin panel (SSDP) model. The study of the SSDP model is substantial because it can explain the interaction between geographic units, is more informative, diverse, efficient, exhaustive, and accurate in reaching conclusions that influence the policy determination. This article’s intention is to derive a parameter estimation method from the SSDP model using a modified generalized estimating equation approach, which is then used to model economic growth in ASEAN nations. This article compares the SSDP model with rook contiguity, 2-nearest neighbors, and a customized spatial weighted matrix in relation to an independent, first-order autoregressive, exchangeable working correlation structure. To model economic growth in ASEAN countries, a customized weighted matrix with first-order autoregressive and exchangeable working correlations is chosen based on the CIC value. The parameter analysis outcomes indicate: 1) it is a significant spatial dependence among ASEAN countries; 2) it is a significant simultaneous interaction among the gross domestic product (GDP) and foreign direct investment (FDI); 3) GDP has a greater influence on FDI than FDI does on GDP; 4) The economic growth is directly affected by the labor force total; and 5) trade openness has a direct effect on FDI.

Traditionally, endemic diseases such as dengue, diarrhea, and tuberculosis are modeled separately, which leads to a limited understanding of current disease dynamics and an inaccurate evaluation of the parameters of the different models. In this study, we propose a joint spatiotemporal model to predict the risks of multiple endemic diseases and identify hotspots. The model includes spatial shared component random effects and a covariate for healthy behavior. The model was applied to the joint modeling of dengue, diarrhea, and tuberculosis in thirty districts in Bandung, Indonesia over a five-year period. Our findings show that the joint model was effective in understanding the characteristics of the diseases. One potential advantage of using shared component models is that they can identify diseases with spatial or temporal distribution patterns and consider shared risk factors that may be spatially correlated, such as climate. It is recommended to conduct exploratory analyses to determine the correlation between the risks of the diseases being studied and the reference disease before using this type of model.

Life insurance is designed to reduce the risk of financial loss due to unforeseen consequences related to the insured's death. In life insurance, the insurer provides death benefits as a claim when the insured suffers death. The claim is the compensation for a risk loss. Individual claim in one-period insurance is called aggregation claim, while aggregation claim is a collective risk. Collective risk is usually measured using a variance. However, the variance risk measure cannot often accommodate any event risk because there is a risk of claims beyond the amount of variance. Using the proposed method CVaR and confidence level are taken from α = 0.25% until 4%. This study found that the proposed method CVaR scored more fairly than Collective Risk. In conclusion, this study indicated that the collective risk model is just included using mean and variance without any confidence level. Therefore, only one result for the Collective Risk model, which automatically shows the model using mean, variance and standard deviation, could not accommodate all risk events.

The dynamics of the conflict in the South China Sea (SCS) have begun to enter a new chapter. Currently, the South China Sea (SCS) is a flashpoint in the Asia Pacific region. This study aims to provide an analysis of the concept of the Revolution in Military Affairs (RMA) strategy by the Indonesian Armed Forces (TNI) toward Competitive Advantage in the South China Sea region. This study employed an analytical approach with a qualitative sequence exploratory method supported by some quantitative data. PEST (Political, Economy, Socio-cultural, Technology) analysis and SWOT analysis methods were used to support the study. Furthermore, this study also utilized an Analytical Hierarchy Process (AHP) method approach to provide strategic value and sensitivity analysis as a strategy scenario analysis toward competitive advantage. Based on the results of the research analysis, a strategy under the development of Indonesian Armed Forces capabilities towards a competitive advantage in the South China Sea was obtained, namely the WO strategy which consists of 6 substrate aspects with eight subfactors, namely the combination of all components and strengths in handling security disturbances in the South China Sea (0.162), increased competence of human resources (0.159); development of integrated defense forces and capabilities (0.147), protection of information systems and state secrets (0.145), development of defense facilities and infrastructure (0.109), increasing the capacity and capability of modernizing intelligence technology (0.093), utilization and capacity building of the national defense industry (0.089), deployment of Indonesian Armed Forces troops in the South China Sea (0.075). This study is expected to contribute to the strategy for handling conflicts in the South China Sea and provide strategic steps in increasing capabilities on competitive advantage.

This study examines the correlation between internal drivers, external pressures, Green Entrepreneurial Orientation (GEO). In the context of wood processing companies in Lumajang, this study examines how green supply chain management (GSCM) practices and performance interact. The study relies on theoretical underpinnings grounded in both institutional theory and the Natural Resource-Based View (NRBV) theory to thoroughly explore and comprehend these complex interconnections. Data was collected from a sample of 98 wood processing companies registered as Primary Timber Forest Product Industries (IPHHK) in the Lumajang District Forestry Office up to 2020, using a saturated sampling technique over three months from January to March 2022. This study's data analysis was carried out using structural equation modeling (SEM), which uses the partial least squares (PLS) methodology. The results of the analysis indicate that internal drivers do not exert a significant influence on Green Supply Chain Management (GSCM) performance. In contrast, external pressure and Green Entrepreneurial Orientation (GEO) have a notable and statistically significant impact on GSCM performance. Furthermore, GSCM practices play a crucial mediating role, fully mediating the correlation between internal drivers and GSCM performance and partially mediating the correlation between external pressure, GEO, and GSCM performance. This research holds practical implications for managers, supply chain specialists, and Lumajang wood processing industry policymakers. It clarifies the significance of particular drivers in putting GSCM practices into practice and reaching improved performance levels. Future research should consider expanding the sample size, extending the scope of the survey, exploring additional research avenues, and implementing longitudinal designs to investigate green supply chain integration and firm behavior over time.

This research tries to analyze the explicit and implicit impact of the smooth supply chain and fairness of the tax system on corporate taxpayer compliance and its implications for state tax revenues. This research uses quantitative methods, using random sampling techniques, and obtained a sample of 100 respondents consisting of various MSMEs registered with the Ministry of Cooperatives and SMEs in 2023 who are taxpayers in South Jakarta City, DKI Jakarta Province, Indonesia. The data obtained from the surveys was utilized in Structural Equation Modelling with Partial Least Squares (SEM-PLS) for additional analysis. Research results and data analysis show that tax system equity has a direct and significant impact on corporate taxpayer compliance; tax system fairness directly has no and minimal impact on state tax revenues; and corporate taxpayer compliance directly and significantly impacts state tax revenues. A smooth supply chain has a direct and substantial impact on corporate taxpayer compliance. Furthermore, a smooth supply chain has a direct and substantial influence on state tax collections. A seamless supply chain and state tax revenues are largely mediated by corporate taxpayer compliance, which has a substantial impact on both. Furthermore, the relationship between the fairness of the tax system and state tax collections in MSMEs in South Jakarta City is totally mediated by corporate taxpayer compliance.