In a complex digital era, innovative supply chain management is the key to success in overcoming logistical challenges. By implementing the latest technology, adopting innovative strategies, and managing risk well, Micro, Small and Medium Enterprises (MSMEs) can improve supply chain efficiency and effectiveness. The main driver of innovation is leadership in the supply chain. The supply chain network views transformational leadership and innovation as a source of competitive advantage. This study attempts to examine the direct and indirect relationship between supply chain transformational leadership, supply chain innovation performance, and satisfaction with supply chain outcomes. The main objective of this study is to analyze the mediating effect of supply chain innovation performance on the effect of supply chain transformational leadership on supply chain outcomes. The study uses structural equation modeling (SEM) with SmartPLS 3.0 software where primary data are obtained through a survey by distributing questionnaires. Respondents from this study were 507 leaders of MSMEs who were included in the purposive sampling criteria. The results of the instrument scale test used in this study met the standards of validity and reliability analysis. The results of the regression analysis of this study indicate that supply chain transformational leadership had a significant and positive effect on satisfaction of supply chain outcomes. Supply chain transformational leadership also maintained a significant and positive effect on supply chain innovation performance. Moreover, supply chain innovation performance provided a significant and positive effect on supply chain outcomes. There was also a partial support in examining the effect of mediating variables on supply chain innovation performance on the positive effect of chain transformational leadership. The theoretical implication of this research is that the results of this study support previous research studies which state that supply chain transformational leadership had a positive and significant contribution to satisfaction of supply chain performance and encourages the improvement of MSMEs. The practical implication of this research is that MSMEs managers must use transformational leadership to encourage increased performance and innovation because the results of this study have proven that transformational leadership in the supply chain will contribute to increased innovation and performance of MSMEs.

The purpose of this study is to empirically examine the role of technology innovation on the influence of intellectual capital on SMEs performance. Resources based view and stakeholder theory underlies this research. Primary data was collected using a survey method from 399 culinary business SMEs managers spread throughout Bali. The number of samples is determined using the Slovin formula. In this study, the partial least squares structural equation model (PLS-SEM) was applied to be tested for validity and reliability. The results of the study show that human capital, structural capital and customer capital have a significant positive effect on technology innovation. Human capital has no effect on SMEs’ performance, but structural capital and customer capital have no significant effect on SMEs performance. Technology innovation has a significant effect on SMEs’ performance. Furthermore, technology innovation can mediate the influence of human capital, structural capital and customer capital on SMEs performance.

To increase salesperson performance, this study will examine how product knowledge, sales environment, and information suitability affect salesperson adaptation. Random sampling is the approach that is utilized for sampling. 115 respondents from small and medium-sized batik business salespeople in the city of Pekalongan made up the study's sample in Central Java, Indonesia. The AMOS 21 program uses structural equation modeling (SEM) as an analysis tool. According to the study's findings, salesperson adaptability is significantly positively influenced by product knowledge, significantly positively by the sales environment, significantly positively by information appropriateness, and significantly positively by salesperson performance.

This research investigated the relationship between supplier integration and customer integration on operational performance by mediating supply chain flexibility in SMEs in Indonesia. The method used in this research used quantitative methods using a conceptual framework and analysis of research data was performed using structural equation models (SEM) with SmrtPLS 3.0 software tools. Respondents to this study were 650 SMEs owners in Indonesia who were determined using the simple random sampling method. Data collection in this study was carried out on primary data in the form of respondents' statements obtained from answers to online research questionnaires designed using a Likert scale of 7. The results of this study stated that Supplier Integration had a positive and significant effect on supply chain flexibility. Customer Integration had a positive and significant effect on Supply Chain Flexibility. Supply Chain Flexibility had a positive and significant effect on operational performance. Supplier integration had a positive and significant effect on operational performance. Customer integration had a positive and significant effect on operational performance.

This study aims to provide implications for the application of the halal logistics traceability model to food commodities in Indonesia and to be able to recommend alternative policy scenarios for the government. The system dynamic approach is used to model the traceability of halal logistics for food commodities in Indonesia. The urgency of this research is that it will contribute to improving the quality of halal logistics which has implications for the halal industry in Indonesia. This research also has implications for food quality and safety which helps food security in Indonesia, the relationship between Halal Certification and Traceability on SMEs Performance, analyzing the relationship between Halal food supply chain management and Halal Certification and Traceability. This research method is a quantitative survey, research data obtained by distributing online questionnaires to 390 food SME owners who have implemented the Halal Assurance Management System. Data analysis used a structural equation model (SEM) with SmartPLS 3.0 software. The stages of data analysis are validity, reliability and significance tests. The sampling technique used is non-probability sampling. The questionnaire used in this study uses a Google form which will be distributed to respondents. This questionnaire measurement method uses a Likert scale of 5, namely Strongly Disagree (STS), (2) Disagree Answers (TS), (3) Neutral Answers (N), (4) Agree Answers (S), Strongly Agree (SS). The independent variable used in this research is halal supply chain management. The dependent variables used in this study are halal certification and traceability and SMEs Performance. The results of this study indicate that Halal food supply chain management has a positive and significant effect on the performance of SMEs, Halal Certification and Traceability have a positive and significant effect on SMEs Performance, Halal Certification and Traceability have a positive and significant effect on Halal food supply chain management.

In Indonesia, natural disasters cases have significantly increased from time to time and have the largest impact on economic losses. To avoid losses in the future due to natural disasters, the insurance company needs to estimate the risk and determine the rate of premium that would be charged to the policyholder. In conjunction with the present issue, this study seeks to determine the premium rate and estimate the size claim of insurance by considering the mitigation fund reserve decisions using The Collective Risk Model (CRM). The data was analyzed using the Poisson process with Weibull distribution to determine the natural disaster frequency and losses. The distribution of losses is estimated using Maximum Likelihood Estimation (MLE), and the magnitude of losses was estimated using the CRM. Also, the mean and variance estimators of the aggregate risk were used to estimate the premium charged. The results indicated that expectation and variance of the frequency of incident claims have the same value, i.e., 2562. Also, the loss claims follow the Weibull distribution with the expected value and variance of 5.81309×10¹⁰ and 2.5301×10²², respectively. The mean and variance of the aggregate (collective) claims are 148,931,365,800,000 and 7.35×10²⁵, respectively. In conclusion, this study has successfully determined the efficient pure premium model through the Standard Deviation Principle (SDP). SDP provides a much cheaper premium than the Expected Value Principle with the same loading factor. In addition, SDP considers the standard deviation of the collective risk of natural disasters. The implications of the results of the premium determination are expected to be the basis for decision-making for insurance companies and the government in determining insurance policies for natural disaster mitigation.

Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.

In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.

COVID-19 is a contagious disease caused by the coronavirus (SARS-CoV-2) that attacks the respiratory tract. On August 14th, 2021, 653,741 persons had been proven positive for COVID-19. The number of patients tends to increase as the number of COVID-19 cases grows. The more infected people, the more cases of COVID-19 there will be. The Bed Occupancy Ratio (BOR) in West Java reached an all-time high of 91.6 percent in June 2021, far exceeding the WHO recommendation of 60 percent, before gradually declining to 30.69 percent in August. Because of the new cases mentioned, the rate of spread of COVID-19 in West Java, the forecast of new cases is very strategic. The number of new cases in this study was predicted using a Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive and new cases from 34 provinces in Indonesia from March 2nd, 2020, to August 14th, 2021. The results of the evaluation using test data on the number of active cases in the last 19 weeks, namely April 10th - August 14th, 2021, The MLP is accurate in forecasting the number of new cases 18 times for both forecast periods with APE < 15%, with the value MAPE, RMSE and MAE obtained were 5.52%, 1157,61, and 706.811. The results of this study can be helpful for the government as a reference in conditioning hospital bed capacity to deal with active COVID-19 cases in West Java in the next two weeks so that the hospital rejects no COVID-19 patients because the hospital is full.

Demand elasticity is the sensitivity of changes in the number of goods demanded by consumers due to changes in the price of goods. This paper compares the price elasticity of demand with and without memory effect using fractional-order derivatives. This study is designed using the development theory of fractional derivatives for the economic field in determining the price elasticity of demand. The result of numerical simulation using the value of α and p indicated that the price elasticity of demand with memory effect is more accurate than without the memory effect. Furthermore, this study concluded that the price elasticity of demand does not only depend on the latest price (current price) but changes in all prices from a specific time interval. The findings of this study suggest future studies can examine the phenomenon of market equilibrium using fractional-order derivatives.