Introduction. Moringa leaves (Moringa oleifera), which are members of the Moringaceae family, are one of the herbal plants that are widely known in Indonesia. Phytochemical contents of moringa leaf, such as flavonoid, quercetin, and phenolic acid, are believed to have an effect on improvement of NAFLD. Therefore, moringa leaf is considered as one the herbal plants that can be used as supplementation in the form of adjuvant therapy to NAFLD. The study objective of our research is to review the effect of giving moringa leaf to the liver, especially the histopathologic features. This study will be conducted on literature review research design, more specifically in the form of a systematic review. Research Method. Five major electronic web databases, including PubMed, Cochrane Library, Google Scholar, Scopus, and ScienceDirect, were used in identifying literature from 2014 to 2023. Results. From a comprehensive analysis of 13 relevant literature sources, we elucidate the impact of Moringa oleifera leaf extract on liver histopathology, glucose, and lipid metabolism. Furthermore, we provide insights into its safety profile concerning human health. Conclusion. The phytochemical content of Moringa oleifera leaf extract had shown a significant benefit in plant medicinal sector. From the research that had been done, Moringa oleifera leaf extract contributes to give significant improvement on liver histopathological features, glucose, and lipid metabolism on animal sample model.

Portfolio management has long been one of the most significant challenges in large- and small-scale investments alike. The primary objective of portfolio management is to make investments with the most favorable rate of return and the lowest amount of risk. On the other hand, time series prediction has garnered significant attention in recent years for predicting the trend of stock prices in the future. The combination of these two approaches, i.e., predicting the future stock price and adopting portfolio management methods in the forecasted time series, has turned out to be a novel research line in the past few years. That is, to have a better understanding of the future, various researchers have attempted to predict the future behavior of stocks and subsequently implement portfolio management techniques on them. However, due to the uncertainty in predicting the future, the reliability of these methodologies is in question, and it is unclear to what extent their results can be relied upon. Therefore, probabilistic approaches have also entered the research arena, and attempts have been made to incorporate uncertainty into future forecasting and portfolio management. This issue has led to the development of probabilistic portfolio management for future data. This review paper begins with a discussion of various time-series prediction methods for stock market data. Next, a classification and evaluation of portfolio management approaches are provided. Afterwards, the Monte Carlo sampling method is discussed as the most prevalent technique for probabilistic analysis of stock market data. The probabilistic portfolio management method is applied to future Shanghai Stock Exchange data in the form of a case study to measure the applicability of this method to real-world projects. The results of this research can serve as a benchmark example for the analysis of other stock market data.