The performance of the manufacturing industry lies in the managers who hold crucial roles. In the revolution industry, data or knowledge holds an important role besides managers’ commitment to work optimally. As intrinsic factors, psychological capital is fundamental for managers’ behavior such as commitment and initiative to share knowledge that simultaneously enables managers’ performance. This research aimed to find the psychology capital’s effect on managers’ performance in manufacturing companies by taking into account sharing knowledge and organization commitment as moderation. Hypothesis testing was done by using data measured with a Likert Scale from 208 managers of a manufacturing company as a representative from each company stationed in the Indonesia Stock Exchange. The results of empirical testing using SEM Lisrel shows that psychological capital affects performance moderated by a variable such as managers’ commitment and knowledge sharing. Based on affected value, the initiative to share knowledge gives greater value to the correlation between psychological capital and managers’ performance in manufacturing companies; compared to commitment. Manufacturing practitioners should be able to facilitate a conducive climate to encourage their managers to share knowledge voluntarily so that the decision-making process and performance are better.

Support vector machine (SVM) has become one of most developed methods for classification, focusing on cross-sectional analysis. However, classification of time series data is an important issue in statistics and data mining. Classification of time series data using SVMs that focus on cross-sectional data leads to improper classification, and hence, the SVM needs to be extended for handling time series dataset. As with cross-section data, the problem of imbalanced data is also common in time series data. Fuzzy method has been proven to be capable of overcoming the case of imbalanced data. In this paper, we developed a Fuzzy Support Vector Machine (FSVM) model to classify time series data with imbalanced class. The proposed method puts the fuzzy membership function on the constraint function. Through simulation studies, this research aims to assess the performance of the developed FSVM in classifying time series data. Based on the classification accuracy criteria, we prove that the proposed FSVM method outperforms the standard SVM method for the classification of multiclass time series data.

This research conducted testing on the influence of business strategy, leadership style, and internal control system (IC) on implementation of Good Government Governance (GGG) and its implication on organizational performance of Social Security Administrator for Health (known as BPJS Kesehatan). Data analysis was performed using a descriptive method, assisted by a statistical tool Structural Equation Modeling (SEM)-Lisrel. The data was tabulated from distributed and returned questionnaires from 325 deputy offices, branch offices, and service offices. The results showed that business strategy, leadership style, and the effectiveness of IC influenced the implementation of Good Government Governance. The result also provides evidence that leadership style had a positive significant influence on performance. Conversely, the business strategy and effectiveness of IC did not have a positive significant influence on BPJS performance.

The genetic algorithm (GA) is a natural selection-inspired optimization algorithm. It is a population-based search algorithm that utilizes the concept of survival of the fittest. This study creates a new crossover operator called “Stas Crossover” that is a combination of four crossover operators, including Single point crossover, Two points crossover, Arithmetic crossover, and Scattered crossover, and then presents the performance of this crossover operator. The area size and probability of Stas crossover can be adjusted.GA is used to find the optimal solution for this multi-product and multi-period aggregate production planning (APP) problem, which was used to test the algorithm, which provides optimal levels of inventory, backorders, overtime and regular production rates, and other controllable variables. According to the findings of this study, the benefit of stable crossover is that it allows for more variety in the way offspring are created and increases the opportunity for offspring to obtain good genetic information directly.

Uncertainty is an important factor in the decision-making process. Hesitant Pythagorean fuzzy sets (HPFS), a combination of Pythagorean and hesitant fuzzy sets, proved as a significant tool to handle uncertainty. Well-defined operational laws and attribute weights play an important role in decision-making. Thus, the paper aims to develop new Trigonometric Operational Laws, a weight determination method, and a novel score function for group decision-making (GDM) problems in the HPF environment. The approach is presented in three phases. The first phase defines new operational laws with sine trigonometric function incorporating its special properties like periodicity, symmetricity, and restricted range hence compared with previously defined aggregation operators they are more likely to satisfy the decision maker preferences. Properties of trigonometric operational laws (TOL) are studied and various aggregation operators are defined. To measure the relationship between arguments, the operators are combined with the Generalized Heronian Mean operator. The flexibility of operators is increased by the use of a real parameter λ to express the risk preference of experts. The second phase defines a novel weight determination method, which separately considers the membership and non-membership degrees hence reducing the information loss and the third phase conquers the shortcomings of previously defined score functions by defining a novel score function in HPFS. To further increase the flexibility of defined operators they are extended in the environment with unknown or incomplete attribute weights. The effectiveness of the GDM model is verified with the help of a supplier selection problem. A detailed comparative analysis demonstrates the superiority, and sensitivity analysis verifies the stability of the proposed approach.

The bus rapid transit (BRT) system has become a cheap public transportation option worldwide, including in Indonesia. The problem in the Jababeka area, Indonesia, was the unconnectedness and lack of transportation as a sustainable area with the whole residence, modal shift, and easy access for people. This research aimed to improve access to Bus Rapid Transit (BRT) based public transportation by implementing the Transit Oriented Development (TOD) Model in the Jababeka area, Bekasi Regency. In this research, modeling was made by using PTV Visum with the trip assignment method and continued with the projected movement from 2022 to 2042, resulting from the people movement survey in 2022 and the SWOT strategy. The sample of this research consists of 210 respondents domiciled in nine subdistricts of Bekasi Regency. The result of this research was that the Jababeka area, Bekasi, must be planned as a TOD-based area, facilitating people to fulfill their transportation needs so that derived demand can run efficiently. Therefore, the implemented strategy must improve transportation access by developing TOD areas with a BRT system. Jababeka area was developed using the typology of regional scale city TOD, with a potential sub-city and environmental TOD typology. TOD development using the BRT system must be able to shift the intercity movement to local movement because residential areas were provided as the substitute for intercity movement.

A bibliometric study of 510 enterprise risk management (ERM) papers from the Web of Science Core Collection (WOS-CC) database from 2004 to 2023 is presented in this article. The study's main goal was to give a bibliometric overview of ERM research, focusing on annual publications, references, journals, authors, author affiliations, and nations. Each article's author, document type, publication year, source, volume, edition, pages, number of citations, and references were obtained from WOS in BibTex format. To help the research, Biblioshiny evaluated this data. The survey indicated that ERM research has increased fast over the previous two decades, with a consistent upward trend and increasing pace in the past five years. "What's wrong with risk matrices?" by Cox, LA (2008) was the most cited publication in this topic, and the Journal of Risk and Financial Management was the most influential journal. David L. Olson of the University of Nebraska Lincoln was the most prolific author, and UNL was the premier research institution in this area, according to the survey. ERM research was heavily influenced by the US and several other countries. To further ERM research, the paper recommends international collaboration. More research can refine the identification of ERM research hotspots and emerging trends, according to the report.

Many-objective optimization (MaO) deals with a large number of conflicting objectives in optimization problems to acquire a reliable set of appropriate non-dominated solutions near the true Pareto front, and for the same, a unique mechanism is essential. Numerous papers have reported multi-objective evolutionary algorithms to explain the absence of convergence and diversity variety in many-objective optimization problems. One of the most encouraging methodologies utilizes many reference points to segregate the solutions and guide the search procedure. The above-said methodology is integrated into the basic version of the Moth Flame Optimization (MFO) algorithm for the first time in this paper. The proposed Many-Objective Moth Flame Optimization (MaOMFO) utilizes a set of reference points progressively decided by the hunt procedure of the moth flame. It permits the calculation to combine with the Pareto front yet synchronize the decent variety of the Pareto front. MaOMFO is employed to solve a wide range of unconstrained and constrained benchmark functions and compared with other competitive algorithms, such as non-dominated sorting genetic algorithm, multi-objective evolutionary algorithm based on dominance and decomposition, and novel multi-objective particle swarm optimization using different performance metrics. The results demonstrate the superiority of the algorithm as a new many-objective algorithm for complex many-objective optimization problems.

A nation's health status can be determined by the availability of healthcare services, which is a crucial part of human life. Since 2011, health facilities in Indonesia have been acknowledged as an important health indicator. This study uses correspondence analysis and spatial visualization to look at the primary healthcare facilities in each region of Indonesia. The analysis makes use of information from Indonesia's province-level data on the number of Regions with health facilities in 2021, along with six different types of medical facilities: hospitals, maternity hospitals, polyclinics, health centers, sub-district health centers, and pharmacies. To show the spread of medical facilities in Indonesia, a spatial representation is also produced. In comparison to provinces on other islands, the analysis reveals that the provinces on Java Island have a more varied and adequate distribution of healthcare facilities. Health facilities on other islands' provinces, however, are only focused on public health and sub-district public health. The spatial representation gives a clear picture of the distribution of medical services and draws attention to the distinctions across Indonesia's regions and islands. The geographical visualization offers a thorough perspective of the distribution of health care facilities, and this study delivers insightful information about how health care facilities are distributed in Indonesia. Future research and policy decisions targeted at enhancing Indonesia's healthcare system can be informed by these findings.

Big Data Analytics (BDA) provides an important resource for businesses seeking to enhance their performance and gain a competitive advantage, although not all organizations are adopting BDA techniques, and small and medium-sized enterprises (SMEs) in Jordan have been slow in this regard, despite being key players in any healthy economy, and the fact that BDA adoption can be facilitated by using the Technology Acceptance Model (TAM). The purpose of this study is to investigate the drivers of behavioral intention among managerial-level employees in Jordanian ICT SMEs to adopt BDA through a quantitative correlational research approach. The TAM questionnaire was used to gather data from 271 online survey participants in Jordan using Google Forms. The target group included management level staff working in small and medium-sized ICT firms (SMEs). Confirmatory factor analysis (CFA) was used to evaluate the research instrument's reliability and validity, and structural equation modeling (SEM) was utilized to test the study's hypotheses. The findings revealed that perceived usefulness, perceived ease of use, and perceived “privacy and security” significantly influenced managerial-level employees' behavioral intention to use BDA in their organizations. The research findings also supported the application of TAM, and the results of the investigation indicated that managerial-level employees would be willing to use BDA techniques providing they were perceived to be useful, user-effortless, and posed little concern about privacy and security. Overall, the current study's results demonstrate that the suggested model had good predictive power, 51% of the variance in behavioral intention, and was therefore capable of predicting managers' intentions to use BDA.