Journal of Advanced Technology and Multidiscipline

THE DETECTOR METHODS OF COLOR CHANGING TO NON-INVASIVE AND ECONOMICAL NANOFILLER COMPOSITE RESIN BASED ON OPTICAL IMAGING

2024-07-02T18:50:22+00:00

It has been developed the detector method of color changing to non-invasive and economical nanofiller composite resin based on optical imaging. The method is chosen due to the easiness of information in images form to be understood. The color changing is represented by the changing of brightness intensity laser which transmitted by samples. The light source uses green pointer laser with 532 nm of wavelength of and webcam sensor which can be obtained in the local market. Fraunhofer diffraction principle is used to utilize set up and test material treatment. By utilizing IC LM 317, it is made a series of regulators so that the laser pointer can be the input voltage from voltage source (AC). The light source of laser pointer is exposed to the test materials for detecting the intensity of transmission. Samples are made as thin as possible in order to transmit light and are given treatment in form of immersion in tea and coffee solution. Immersion is done for 1 week for 4 hours per day. The transmission intensity of samples captured by webcam and processed using the Delphi program. The data collections in form of transmission intensity are in pixel scale. The results indicate that the longer time immersion used affect the transmission intensity of samples decrease. These results can be seen from graph of the relation between transmission intensity with longer time of immersion. This detector can be used to help characterization of color's stability determination on the material which is portable gear.

Application of Negative Binomial Regression Model in West Java Tourism

2024-05-04T17:38:39+00:00

Tourism is the most important sector for potential areas. One province in Indonesia that has quite potential is West Java. To be able to increase this potential, it is necessary to increase the number of tourists. This research aims to increase the number of visitors through several factors that are thought to be influential, namely the number of star hotels, number of stalls, and number of restaurants. The method used to determine the relationship between the number of visitors and these factors is regression. Because the research data is in the form of count data, it uses the Poisson distribution. If the data indicates overdispersion, it can be modeled using a Negative Binomial regression model as a comparison. The results obtained state that the Negative Binomial regression model is better than the Poisson regression model. Using the Negative Binomial regression model, it is found that increasing the number of tourists can be achieved by increasing the number of star hotels and number of restaurants. However, the number of stalls must be reduced so that visitors can increase. Thus, the aspect that must be addressed is to disband illegal stalls and increase the number of hotels and restaurants.

Student's Behavior Clustering based on Ubiquitous Learning Log Data using Unsupervised Machine Learning

2024-03-13T09:44:44+00:00

Online learning is the source of data generation related to learner's learning behaviors, which is valuable for knowledge discovery. Existing research emphasized more on an understanding of student's performance and achievement from learning log data. In this study, we presented data-driven learning behavior clustering in authentic learning context to understand students' behavior while participating in the learning process. The objective of the study is to distinguish students according to their learning behavior characteristics and identify clusters of students at risk of unsuccessful learning achievement. Learning log data were collected from ubiquitous learning applications before conducting Exploratory Data Analysis (EDA) and cluster analysis. We used partitional clustering using K-means algorithm and hierarchical clustering based on the agglomerative method to improve clustering strategies. The result of this study revealed three different clusters of students supported by data visualization techniques. Cluster 1 comprised more students with active learning behavior based on the total logs, total problems posed, and the total attempts in fraction operation and simplification. Students in clusters 2 and 3 had a higher attempt at problem-solving instead of problem-posing. Both clusters also focused on fraction's conceptual understanding. Knowledge discovery of this study used real data generated from ubiquitous learning application namely U-Fraction. We combined two different types of clustering method for delivering more accurate portrait of a student's hidden learning behaviors. The outcome of this study can be a basis for educational stakeholders to provide preventive learning strategies tailored to a different cluster of students.

High performance of straight and U-shaped probe microfiber sensors for sucrose solution detection applications

2024-05-19T19:10:40+00:00

A low cost, highly sensitive sensor with easy fabrication has been successfully developed to detect variations in the concentration of sucrose solutions using a microfiber probe sensor. The microfiber probe was fabricated using a flame brushing mixture of butane and oxygen with single-mode optical fiber material and pulled on both sides to achieve a size of 16.48 µm. These microfiber probes were characterized into two sensor probe shapes: straight and u-shaped, to measure variations in the sucrose solution concentration. The results for both probe shapes showed a decrease in peak output intensity and a shift in peak wavelength as the sucrose concentration increased from 0.5% to 3%. The straight shape exhibited a sensitivity of 0.241 dBm/% with a slope linearity of 99.5% and a resolution of 0.0415%, while the U-shape had a sensitivity of 2.692 dBm/% with a slope linearity of 90.6% and a resolution of 0.0030%. The measurement spectra results indicated significant differences in u-shape at each concentration. In conclusion, both microfiber sensor probe shapes exhibited excellent performance and are suitable for use as chemical sensors to measure variations in solutions.

OPTIMAL CONTROL DESIGN FOR FREQUENCY REGULATION IN ELECTRIC POWER SYSTEM WITH LOW INERTIA

2024-07-09T10:54:28+00:00

Electricity is a very important element in this era because almost all aspects of modern life depend on electricity. Therefore, electricity plays a very important role in improving people's quality of life and maintaining an efficient and productive life. An efficient and reliable electrical system is essential to ensure adequate electricity availability and maintain system reliability. Therefore, planning, designing and operating electrical systems must be carried out carefully to ensure stability, reliability and efficiency. However, a decrease in frequency in the electrical system sometimes occurs when there is a sudden change in load. This can affect system stability. Therefore, Load Frequency Control (LFC) and Linear Quadratic Regulator (LQR) analysis is needed to maintain the stability of the electrical system frequency. The combination of these two techniques, namely LFC with LQR modeling, provides a better solution for maintaining frequency stability and optimizing electrical system performance. LFC analysis regulates power generation settings automatically to compensate for fluctuations in load demand and maintain a stable frequency, while LQR is a control technique used to minimize system errors and optimize system performance. Therefore, LFC with LQR results in system performance increasing very significantly with a faster response, undershoot that can be reduced to 0.001 and a better settling time of 300s in area-1 and 450s in area-2 and rise time reaching 270s in area-1 and 405s in area-2 as well as the use of LQR can maintain the system frequency at its nominal limit and the presence of New Renewable Energy (EBT) has an effect in the form of a greater undershoot level than without EBT.