Over the last two decades, the global financial landscape has changed dramatically, including the corporate and political climates, the creation of more market-based economies, and rapid technological advancements. Micro, Small, and Medium Enterprises (MSMEs) is one form of market-based economies created with the most significant business opportunities and very important socially and economically in developing countries for many reasons. However, MSMEs face significant difficulty related to financial reporting standards to evaluate the achievement of business activities. Most MSMEs use a simple form for financial reporting, such as by calculating the difference between inflow and outflow from their business activities. Also, numerous previous studies that focus on Financial Accounting Standards in Indonesia are still limited. Thus, the present study investigates the factors that influence the implementation of Financial Accounting Standards (FAS) of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province, Indonesia. This quantitative study uses cross-sectional data collected by distributing 200 questionnaires to MSMEs actors that spread North Aceh Regency, namely Lhokseumawe city and Bireuen regency. The data are analyzed using descriptive statistics (e.g., frequency, percentage, mean, standard deviation) and inferential statistic (multiple linear regression) analyses. The study indicated that Perception, Education, Socialization and Incentive factors had significant positive effects on implementing Financial Accounting Standards of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province, Indonesia. In conclusion, this study has successfully investigated the factors that influence the implementation of Financial Accounting Standards of Micro, Small, and Medium Enterprises (MSMEs) in Aceh Province.

The current pandemic era has given uncertainty to the country's economic growth and resulted in many countries experiencing a drastic decline in share prices. This condition impacts investors' perceptions of the funds that have invested in the stock market. This study investigates the effect of essential information and disposition effect on shifting decision investment with the character investor's moderation as the moderator variable. A survey was conducted on 252 investors who have invested in the Indonesian stock exchange. The Data processing used the partial least square (PLS) technique. This study indicates that essential information for investors in the pandemic era can increase the disposition effect in deciding beneficial share ownership. The essential information obtained by investors in the covid era regarding stock market movements and its internal performance in the stock market list can increase investor shifting decisions. The disposition effect can have a significant effect on shifting decision investors. Essential information related to stock price movements and its internal performance affects investors' courage to take risks and provide optimism for shifting decisions. Then the investor type does not affect the disposition effect on shifting decisions. This study contributes to the theory of financial behavior in decision making by considering psychological factors when uncertainty exists in the stock market.

A real inventory system for single item with specific demand characteristics motivates this works. The demand can be seen as two types of independent demand, where compound Poisson process describes the characteristics of each demand. The first type of demand is rarely occurred with relatively large size, while the second type of demand is often happened with relatively small size. In order to maintain inventory level, every time the first type of demand occurs a replenishment of stock is conducted which follows order-up-to-level inventory policy. In order to find the optimal inventory decision for that system, a mathematical model of the system is developed with the objective to minimize expected total inventory cost. Some of model assumptions are infinite replenishment, deterministic lead time, and completely backlogged shortages. To solve the model, it is then divided into two sub-problems and classical optimization technique is employed to help find the solution of each sub problem.

The purpose of this paper is to provide a method of process reference to supply chain relationships in a downstream sector. It serves as a tool to analyze collaboration between entities in the supply chain strategy. The study is categorized into the case study research in Malaysian oil palm refineries. The data collection is accomplished through semi-structured interview using snowball sampling and observation by visiting four oil palm refineries based on selecting non probability sampling technique through purposive personal judgment. Most of downstream industries select produced end products with the aim of getting greater margins. Nevertheless, a process reference analyzes that the downstream industry distributes semi-finished products to the international market than producing end products in the local market. There are the effects of supply chain relationships between upstream and downstream. Furthermore, entities that have the high dependencies tend to follow the strategy of the dominant entities consequently network of business process driven by the strong entities. The result gives implication to leverage SCOR model for analyzing the supply chain relationships between entities. It can be applied to any industry that decide the effective collaboration with their supplier, distributor and buyer.

This study conducts a comprehensive literature review to understand the direction and trends in contemporary labor studies, emphasizing significant global issues attracting scientific attention. Employing a scientometric approach, recent research data is explored using bibliometric analysis. The research adopts a mixed-methods approach, utilizing National Labor Statistics and conducting Focus Group Discussions (FGD) for nuanced insights into labor conditions in Indonesia. A bibliometric analysis of Scientific Labor Research Articles in Scopus (2020-2022) identifies trends and classifies global labor-related topics. Results highlight challenges in the labor landscape, driven by technological advancements and globalization, impacting job security, creating skill gaps, and raising concerns about the Fourth Industrial Revolution. The informal sector, particularly pronounced in Indonesia, poses challenges related to poverty, inequality, and the gig economy. Emerging issues like informal care for the elderly, social capital, and informal learning call for nuanced policy approaches. Indonesia's aging population adds complexity, requiring sustainable support mechanisms for healthcare and social services. The digital landscape, specifically Fintech, plays a significant role, yet research gaps persist. Bridging the digital talent gap is crucial for effective digital transformation, necessitating collaboration between government, educational institutions, and industry players. Challenges in Fintech development highlight the importance of initiatives promoting digital literacy, ethical practices, and regulatory frameworks. In conclusion, a holistic and collaborative approach is essential for navigating complexities and fostering sustainable economic growth.

Deep learning techniques are making significant contributions to the rapid advancements in forecasting. A standout algorithm known for its ability to produce accurate forecasts by recognizing temporal autocorrelation within the data is the Long Short-Term Memory (LSTM) algorithm, a component of Recurrent Neural Networks (RNN). The LSTM method employs both stateless and stateful architecture approaches, providing versatility in its application. This research aims to compare stateful and stateless algorithms in LSTM models, focusing on forecasting stock prices, such as those of Apple Inc. This comparative analysis is crucial, taking into account various characteristics of time series data, including the benefits and drawbacks of temporal autocorrelation. The comparison results reveal that, despite the stateful algorithm requiring more computational time, it achieves greater accuracy than the stateless approach. The forecast indicates a potential upward trend in share prices for the period of January to December 2024, according to the projected outlook for Apple's stock value. However, it is essential to exercise prudence in interpreting these results, considering that share price fluctuations are influenced by a significant number of variables.

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.

Responsive, timely, and friendly service quality plays a central role in shaping trust between the government and citizens. With the improvement of service quality, the public feels valued and heard, reinforcing the mutual trust relationship between the government and citizens. In this regard, technology-enabled service processes can streamline time and cost, while automation reduces the risk of human errors. Through web platforms or applications, the government can provide easier access for citizens to various services without the need to physically visit government offices. Good and quality public services are not only aimed at meeting the practical needs of the public but also play a role in shaping the mutual trust relationship between the government and citizens. Therefore, the concept of e-service quality, which encompasses the quality of services provided through electronic platforms, becomes crucial. The objective of this research is to explore the extent to which e-service quality can influence the level of public satisfaction. The research method employs a quantitative approach using primary data sources, where random sampling is applied as the sampling technique. The research respondents are citizens using digital public service platforms organized by the local government of Jakarta. The sample size used in this study is 262. The variables tested in this research involve e-service quality, public trust, and public satisfaction. In analyzing the data, this research utilizes SmartPLS 4 software. The analysis results show that e-service quality has a significant influence on the formation of public trust. Furthermore, findings indicate that e-service quality also significantly affects public satisfaction. However, the analysis results do not support the idea that public trust mediates the relationship between e-service quality and public satisfaction. This signifies that while public trust directly contributes to public satisfaction with public services, other unmeasured factors also play a role in shaping public perceptions and satisfaction.

The space-time model combines spatial and temporal elements. One example is the Generalized Space-Time Autoregressive (GSTAR) Model, which improves the Space-Time Autoregressive (STAR) model. The GSTAR model assumes that each location has heterogeneity characteristics, and that the data is stationary. In this research, the moving average component is calculated by involving the relationship between variable values at a certain time and residual values at a previous time, and it is assumed that the data is not stationary, so the model used is the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Model. The model order for GSTARIMA is determined through the Space-Time Autocorrelation Function (STACF) and Space-Time Partial Autocorrelation Function (STPACF) to ensure accurate forecasting. Previous research only discussed the GSTARIMA(1,1,1) model, so in this research, the GSTARIMA(3,1,1) model will be addressed as a form of development of the GSTARIMA(1,1,1) model and applied to climate data. The climate data used in this research is sourced from NASA POWER and consists of rainfall variables with large data sizes, requiring the use of the data analytics lifecycle method to analyse Big Data. The lifecycle includes six phases: discovery, data preparation, model planning, model building, communicating results, and operationalization. Based on the data processing results with Python software, the GSTARIMA(3,1,1) model has a MAPE value of 9% for out-sample data and 11% for in-sample data. In contrast, the GSTARIMA(1,1,1) model has a MAPE value of 11% for out-sample data and 12% for in-sample data. So the GSTARIMA(3,1,1) model provides more accurate forecasting results. Therefore, selecting the correct model order is crucial for accurate forecasting.

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.