Trisakti University (Usakti) is Indonesia's largest private university located in Jakarta, Indonesia. Trisakti University, is the only private university in Indonesia which was established by the Government of the Republic of Indonesia. Founded on 29 November 1965, the university has more than 20,000 active students and has produced more than 100,000 alumni. Trisakti University currently employs 742 tenured faculty members (82%) and 160 part-time lecturers (18%).
Accurate prediction of temperature and relative humidity is essential in Solar Dryer Dome (SDD) facilities to ensure strict quality of the agricultural products. Compared to traditional control models, ML-based approaches provide benefits in terms of adaptability, as they are capable of capturing complex and non-linear patterns in time series data. In this project, primary time series datasets collected from SDD facility in North Jakarta were used to forecast the next hour temperature and relative humidity values within the 5-minutes resolution. There were three sensors placed in three different locations: outside the facility near the front door, inside the facility near the front door, and near the fan inlet. The collected data showed daily seasonality patterns with temperature and relative humidity possess an inversely correlated relationship due to their inherent physical interaction. On the modeling part, this research compared three different predictive models such as XGBoost, Linear Regression, and Facebook's Prophet based on the RMSE and R2 scores on the test set. XGBoost and Linear Regression provided on-par performance, with XGBoost showing its best at temperature prediction (RMSE: 1.47 degrees C, R2: 0.96) and Linear Regression showing its best at relative humidity prediction (RMSE: 3.20%, R2: 0.96). Meanwhile, Prophet exhibits the lowest performance in both variables with RMSE: 3.56 degrees C and R2: 0.72 for temperature, and RMSE: 8.42% and R2: 0.76 for relative humidity forecasting. Despite the low performance, the Prophet model was advantageous in terms of consistency across horizons, while the two best models show decreasing performance in higher horizon settings. These findings suggest the promising application of ML techniques in SDD facilities to support sustainability in agroindustrial business.
Drawing on the resource-based view and dynamic capabilities theory, this study examines whether innovation and technology adoption shape organizational agility, how organizational agility and employee moral competence influence corporate reputation, and how corporate reputation affects firm performance in the Indonesian life insurance industry, where stakeholder trust is critical. Using validated multi-item Likert scales, survey data were collected from 237 senior and middle managers in national, multinational, and Sharia life insurers and analysed using partial least squares structural equation modelling. The results show that innovation and technology adoption positively affect organizational agility; organizational agility and, more strongly, employee moral competence enhance corporate reputation; and corporate reputation is strongly associated with financial and non-financial firm performance. These findings indicate that, in a credence service context, corporate reputation is the central mechanism through which technological, organizational, and ethical capabilities are converted into superior performance. By integrating dynamic capabilities, micro-level ethics, and corporate reputation into a single framework, the study highlights employee moral competence as an overlooked strategic capability for insurers seeking stronger trust, reputation, and performance.
There is a growing need to comprehend what influences consumers' confidence in online banking and other digital financial services due to the exponential growth of financial technology (FinTech). This research delves into the ways in which digital financial literacy, technology literacy, perceived risk, trust in artificial intelligence, and financial literacy impact the confidence that people in Indonesia have in FinTech. To provide a more thorough explanation of trust creation, perceived risk was included in the study model. The data was analyzed using Structural Equation Modeling (SEM) with AMOS. A total of 236 employed postgraduate students from the Greater Jakarta region were surveyed using purposive sampling. Digital financial literacy does not substantially impact users' trust, but financial literacy has a substantial negative impact on FinTech trust, according to the empirical results. confidence in FinTech is favorably impacted by technical literacy and confidence in AI, but it is significantly negatively impacted by perceived risk. Among the factors that were taken into consideration, people' faith in FinTech is most strongly explained by their confidence in artificial intelligence. The results of this study add to our knowledge of how people trust digital financial services by demonstrating the importance of users' perceptions of AI-related trust and risk, and by indicating that, in the FinTech context of Indonesia, factors related to technology have a greater impact on users' trust than factors related to literacy. Keywords: Digital Financial Literacy; Financial Literacy; Perceived Risk; Trust; Technological Literacy; Artificial Intelligence; Financial Technology
Post-extraction infections and delayed oral wound healing remain clinical challenges due to bacterial biofilms, oxidative stress, and inflammation. Natural plant-derived compounds offer promising alternatives to conventional antimicrobials. Zizyphus mauritiana (daun bidara), widely used in traditional Indonesian medicine, has not been comprehensively evaluated for its potential role in oral infection control and wound repair. This study investigated the antibacterial, antibiofilm, antioxidant, and anti-inflammatory activities of ethanolic extract of Z. mauritiana (ZM) leaves using both in vitro and in vivo models. The antibacterial activity of ZM extract was tested against Prevotella intermedia, Fusobacterium nucleatum, Streptococcus sanguinis, and Treponema denticola using the microdilution method to determine minimum inhibitory (MIC) and bactericidal (MBC) concentrations. Antibiofilm activity was assessed using a 96-well plate biofilm assay and confirmed by quantitative polymerase chain reaction (qPCR). Antioxidant activity was evaluated using the DPPH assay to determine the IC₅₀ value. For in vivo evaluation, gels containing 50
Artificial intelligence (AI) has become pervasive in biomedicine and is transforming orthopaedic research from bench to bedside. Beyond its established roles in robotic surgery and diagnostics, AI now supports advances in biomechanics, imaging, tissue engineering, drug discovery, genomics, and prosthetic control. In biomechanics, AI enables faster finite-element simulations, markerless gait analysis, and data augmentation using synthetic signals. Imaging applications include automated segmentation of the spine and hip, opportunistic screening for osteoporosis, bone metastasis detection, and three-dimensional analysis of knee osteoarthritis. In regenerative medicine, AI assists in scaffold optimization, bioprinting, and personalized cell therapies, while integration with genomic and proteomic data enhances precision orthopaedics. Machine learning–based control systems also improve the usability of prosthetics and exoskeletons, reducing cognitive burden for patients. Despite challenges such as data scarcity, validation, and ethical considerations, AI is emerging as a powerful complement to traditional research methods. By accelerating workflows, improving accuracy, and enabling individualized care, AI holds strong potential to bridge laboratory discoveries with clinical applications in orthopaedics. This review highlights the application of AI in orthopaedic research and assesses how it could integrate into clinical practice in the future.
