SUS316L stainless steel has been widely used in medical applications. However, some germs frequently adhere to the device surface, resulting in infections following implantation surgery. Unfortunately, the material lacks antibacterial characteristics that prevent microorganisms from adhering to the surface. This study aims to use electrophoretic deposition to deposit chitosan/silver (Ag) as an antibacterial agent on stainless steel 316L. The antimicrobial effects of chitosan and silver are well established. During the deposition, the rectifier voltage was adjusted to a constant 10 volts with a suspension pH range of 2.7 to 5.1. The effect of varying the pH of the suspension on the physical, mechanical, and antibacterial properties of chitosan/Ag thin films was investigated. The materials’ structure and morphology were studied using X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier- transform infrared spectroscopy (FTIR). The antimicrobial inhibition was examined using the Kirby-Bauer antimicrobial test. The results reveal that increasing the pH of the suspension causes an increase in the thickness, size, and aggregation of the chitosan/Ag thin film. The highest thickness achieved during deposition with a pH 5.1 suspension is 5.265 𝜇m. The best antibacterial agent is achieved at a pH 3.5 suspension sample with an inhibitory zone diameter of 4 mm

Project scheduling based on probabilistic methods commonly uses the Program Evaluation Review Technique (PERT). However, practitioners do not widely utilize PERT-based scheduling due to the difficulty in obtaining historical data for similar projects. PERT has several drawbacks, such as the inability to update activity dura- tions in real time. In reality, changes in project conditions related to resources have a highly dynamic nature. The availability of materials, fluctuating labor productiv- ity, and equipment significantly determine the project completion time. This research aims to propose a probabilistic scheduling model based on the Hybrid Bayesian Network-PERT. This model combines PERT with Bayesian Network (BN). BN is used to accommodate real-time changes in resource conditions. The modeling of BN diagrams and variables is obtained through an in-depth literature review, direct field observations, and distributing questionnaires to experts in project scheduling. The model is validated by applying the proposed model to a 60 m concrete bridge construction project in Indonesia. The simulation results of the proposed model are then compared with the case study project to assess the model’s accuracy. The result of the study shows that the proposed hybrid Bayesian-PERT model is accurate and can eliminate the weaknesses of the PERT method. Besides being able to provide an accurate prediction of project completion time (93.4%), this model can also be updated in real-time according to the actual condition of the project

The process of manually making and setting course schedules using Microsoft Excel is ineffective, time-consuming, and still prone to errors. This research develops a website-based scheduling management application with a case study at SMK Pariwisata Margarana so that it can solve scheduling problems manually. The UserCentered Design (UCD) method is applied in the application prototype design stage. Open interviews, field observations, simulations, and questionnaires were used as research data collection methods. Three iterations were carried out at the prototype design stage to fulfill all user needs. The high-fidelity prototype in the last iteration is then implemented into an application. Application quality is measured using ISO/IEC 25010 with five characteristics. The test results on usability characteristics show that the scheduling management application obtains an average usability score of 91.2%. The appropriateness recognizability sub-characteristic obtained the highest usability score of 93.53%. UCD can help produce an application that can meet all the user’s needs when implemented in the application design phase.

Maintenance activities on power plant projects, which are located in remote areas, are prone to risks related to project delay due to the high complexity of work and limited completion time. This research aimed to fill the existing research gap by developing a delay risk assessment of those projects using a probability impact matrix, which is then transformed into a fuzzy set theory. A case study has been undertaken to represent this research topic at three PLTMGs in Nusa Tenggara. The research steps were potential delay risks identification, critical risk assessment, and risk response development. The results show that the critical risks are lack of skilled and experienced manpower, delay in material and equipment delivery, poor communication and coordination between the contractor and the client, unavailability of materials in the local market, and incomplete material received. Several risk responses are provided in this research, such as strengthening the qualification of the job vacancy, having regular training or sharing sessions before the project execution, and having a stock of emergency spare parts in the central warehouse.

Breast cancer is the leading cause of death in women worldwide; prevention of possible death from breast cancer can be decreased by early identification ultrasound image analysis by classifying ultrasound images into three classes (Normal, Benign, and Malignant), where the dataset used has imbalanced data. Imbalanced data cause the classification system only to recognize the majority class, so it is necessary to handle imbalanced data. In this study, imbalanced data can be handled by implementing the Deep Convolution Generative Adversarial Network (DCGAN) method as the addition of synthetic images to the training data. The DCGAN method generates synthetic images with feature learning on a Convolutional Neural Network (CNN), making DCGAN more stable than the basic generative adversarial network method. Synthetic and original images were further classified using the CNN GoogleNet method, which performs well in image classification and with reasonable computation cost. Synthetic ultrasound images were generated using a tuning hyperparameter in the DCGAN method to adjust the input size on GoogleNet for imbalanced data handling. From the experiment result, the implementation of DCGAN-GoogleNet has a higher accuracy in handling imbalanced data than conventional augmentation and other previous research, with an accuracy value reaching 91.61%, which is 1% to 4% higher than the accuracy value in the previous method.

Belitung Taro (Xanthosoma sagittifolium) is a plant that can be processed into various food products. The high-water content of taro and the lack of a precise drying system made this material easy to rot. This study aims to evaluate the dehydration performance of tray dryers to reduce the water content of Belitung Taro. The independent variables used were air velocity (3-6 m/s), taro thickness (1-4 mm), and drying time (30-120 min). The results showed that the optimum drying time of taro is 30 min. The dryer airflow of 5 m/s significantly reduces the water content. The thickness of the slices positively affects the drying rate. The lowest water content was obtained in drying with a thickness of 1 mm. The ANOVA results show the effect of thickness, time, and flow rate variations on the drying rate. Three types of mathematical modeling are used to estimate moisture content: Newton, Page, and Modified Page. The Modified Page equation was preferred to detect the moisture content of the taro. From this study, the optimum condition of the tray dryer can be the best solution to dehydrate Belitung Taro effectively and efficiently