The internet of things technology has become one technology that plays a role in the industrial revolution 4.0. Not only limited to the data communication between devices over internet protocol communication data networks, but on the IoT device is also need the proper interface to visualize the sensor data which has received by the server node, and with that result, the user can understand easily and get insight from those data which has an exchange or communicate each other on the system. In the previous research by Yuliantoro, et. al., the IoT device was designed only to communicate to other devices, where the web server as a server node was designed to receive the sensor data. The design of the visualize system in this research, designed by the PHP script and web server which is connected to the NodeMCU in a wireless local area network or WLAN. The sensor data from NodeMCU sent to the server, then the result will be shown visually by the website interface. Based on the results of this study, the visualized data has been shown the full sensor data, with the delay is following the delay of the NodeMCU, 60 seconds.
Purpose: This paper compares five supervised learning algorithms (support vector machines, k-nearest neighbor, decision tree, random forest, and AdaBoost) for predicting heart disease and examines the impact of normalization and GridSearch hyper-parameter tuning on model performance. Methods: The study utilizes the Cleveland database from the University of California-Irvine (UCI) repository, comprising data on 918 instances of heart disease patients with 12 attributes. Eleven attributes serve as predictors, while one attribute represents the target class. Models are built and tested using this dataset. Results: Comparing the algorithm performances with existing literature, accuracies range from 89.13–91.85%. AdaBoost exhibits the highest performance, whereas the decision tree performs the least effectively. Results surpass those reported in the literature. Normalization improves prediction performance by 17% for Support Vector Machines (SVM) and 14% for k-nearest neighbor (kNN). SVM does not benefit from GridSearch, while GridSearch enhances the decision tree and AdaBoost by 7% and 4% respectively. Normalization combined with GridSearch improves kNN and random forest by 2–3%. Conclusion: This study compares supervised learning algorithms for heart disease prediction. AdaBoost emerges as the top-performing algorithm, while the decision tree performs relatively poorly. The findings surpass those in the literature. Normalization significantly improves performance for SVM and kNN, while GridSearch enhances the decision tree and AdaBoost. Combined, normalization and GridSearch yield performance improvements for kNN and random forest. These results contribute to the field of heart disease prediction, offering valuable insights for algorithm selection and guiding future research.
This paper discusses the simulation of automation control and hull manufacturing on a fast boat. This high-speed boat that will be carried out for fish mapping. This study uses a numerical simulation to predict its motion. The experiment was carried out 3 times in forward motion, and 3 times in turn motion. The ship’s time and movement in ideal conditions will be recorded for each experiment. With the PID value for forward overshoot with values such as: 8.94%, 30.86%, and 40.60%, while turning overshoot is obtained, among others: 25.77%, 49.82%, and 61.15% Then the advanced settling time obtained includes: 0.91 seconds, 3.84 seconds, and 5.31 seconds. The settling time for turns is: 1,022 seconds, 0.85 seconds, and 0.84 seconds These values are obtained from the simulation graph of the three-dof formulas for surge, sway, yaw.
Solar radiation could be used as alternative energy by using solar panel as a transducer to convert solar radiation energy to electricity energy. Jamal SSFarm, a family-owned business in decorative fish cultivation had problem in electricity energy limitation for their operational works. Researcher and team offered solutions to install and implement solar energy as an alternative energy by using solar panels. This community service works have done by two steps, the first step was surveying and buying stuffs and the second step was installment and testing at the partner location. In our activities, the alternative energy system works well. The testing device showed 1.25 ampere of its current, which shows that the device works well. That matter are in line with our partner expectation.
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