Jurnal Nasional Teknik Elektro dan Teknologi Informasi

The Exploration of Student Emotion Experience and Learning Experience in E-learning Platform

2024-11-21T16:11:12+07:00

Previous studies have shown that emotion is crucial in student learning. However, most studies in the e-learning environment have yet to consider emotion as part of learning that could lead to successful learning. Thus, this study explored the relationship between student emotion state, emotion sequences, and student learning experience. A preliminary data collection was conducted to explore the relationship between emotional experience and student learning experience, which involved 16 students. Students were asked to learn a programming subject in an e-learning environment. E-learning is designed to store the students' emotional experience and activity during learning. The sequential pattern mining technique was used to extract the data, exploratory data analysis was conducted to visualize the emotional trajectory during the learning process, and regression analysis was used to explain the relationship between students' emotional learning experiences. The results showed that emotional experience might affect student experience in learning. In one-sequence emotion, all emotion states contributed to the learning experience with p-values < 0.01 except for neutral and disgust with p-values < 0.05. The one-sequence emotion model shows R-squared = 0.585; Adj. R-squared = 0.734; F-statistic = 6.920; Prob (F-statistic) = 0.00702. Meanwhile, in two-sequence emotion, none of the emotion sequences contributed to the student learning experience. Lastly, three-sequence emotion models also showed that most sequences did not influence student learning experience. The only sequence of emotions that influenced the student learning experience was surprise-neutral-surprise. These results suggest that emotion should be considered in learning design as it can influence student experience.

Optimizing Solar Panel Efficiency: Integration of Dual Axis Solar Tracking and Reflectors

2024-11-22T09:41:36+07:00

Solar panels have relatively low efficiency, but their performance can be enhanced by a tracking system directing the panels perpendicular to the light source and adding reflectors to capture more sunlight. The dual-axis solar tracking method, using two linear actuators and optimized by fuzzy logic, efficiently positions solar panels for maximum sunlight exposure. This research aimed to improve the overall efficiency of solar panels by integrating reflectors with a dual-axis solar tracking system optimized by fuzzy logic. Specifically, this research tested various reflectors to determine the most significant efficiency improvement. This research consisted of two tests: a tracking test and a reflector test using a halogen lamp. The tracking test was conducted by positioning the light in four different positions. The light sensor data were obtained before and after the solar tracking, indicating that the tracking was successful. All these tests were conducted with the light source radiation of 1,168 W/m2. This research concluded that the tracking system effectively positioned the solar panels toward the light source, with the tracking time ranging from 12 to 16 s, depending on the position. Aluminum foil is the most cost-effective reflector, priced at IDR5,341 per 1% increase in efficiency, compared to mirrors at IDR20,204 per 1% and reflective tape at IDR48,034 per 1%. In conclusion, the integration of aluminum foil reflectors and a dual-axis solar tracking system, optimized by fuzzy logic, significantly improves the efficiency of solar panels, which is both cost-effective and efficient.

Improving the Android Geopositioning Accuracy Using Graham Scan Algorithm and Moment Centroid

2024-11-22T09:29:53+07:00

Geopositioning is the process of determining or estimating the geographic position of an object through the global positioning system (GPS). The calculations in geopositioning require measurements of distances or angles relative to known reference positions. In Android devices, achieving accuracy, speed, and power efficiency in geopositioning with GPS, cellular networks, and Wi-Fi can be challenging. This research aimed to improve the accuracy of the geopositioning process for cellular networks on Android devices through polygon triangulation using the Graham scan algorithm and determining a moment centroid for the improved estimation of geolocation data. The geolocation data were collected using an Android smartphone with a cellular network and disabled Wi-Fi. A filtering phase on the coordinates was established to obtain the closest distance coordinates from the other. The distances between each pair of coordinates were calculated using the haversine formula, and then the average distance of all pairs was calculated. Then, a polygon was formed by arranging the coordinates in a sequence, which was achieved using the Graham scan algorithm. After obtaining a set of triangles from the polygon triangulation results, the moment centroid of each formed triangle was determined. The centroid, as a result, was compared with another centroid calculation, the Lagrange interpolation polynomial. Based on the results obtained from quantifying the accuracy and precision using average Euclidean error (AEE) and root mean square error (RMSE), the coordinates derived from the moment centroid were more accurate and precise than the Lagrange interpolation polynomial.