ABSTRACT. This paper elaborates a research of the cancer patients after receiving a treatment in cencored data using Bayesian estimation under Linex Loss function for Survival Model which is assumed as an exponential distribution. By giving Gamma distribution as prior and likelihood function produces a gamma distribution as posterior distribution. The posterior distribution is used to find estimatior ̂ by using Linex approximation. After getting ̂, the estimators of hazard function ĥ and survival function ̂ can be found. Finally, we compare the result of Maximum Likelihood Estimation (MLE) and Linex approximation to find the best method for this observation by finding smaller MSE. The result shows that MSE of hazard and survival under MLE are 2.91728E-07 and 0.000309004 and by using Bayesian Linex worths 2.8727E-07 and 0.000304131, respectively. It concludes that the Bayesian Linex is better than MLE.
This paper describes the innovations in mathematics teaching for the D3 Dep. of Electrical Engineering Politeknik Negeri Semarang (Polines), Indonesia during the corona pandemic and the evaluation. The case study method is chosen to observe the qualitative and quantitative data based on three considerations, i.e. the technologies, goals and assessments. The results show three things: teaching innovations based on the processes and products and the evaluation. Processes here mean the variety on the technology use in teaching. During the pandemic, not only Whatsapp (WA) application is used to support the teaching but also two learning management systems (LMS), i.e. Elnino (Moodle-based) and Google Classroom (Google-based). These technologies are used simultantly to record the attendances as well as to deliver teaching and assignment materials. Meanwhile, Whatsapp (WA) application is then used specifically for discussions, Google Classroom for submitting assignment and Elnino for managing the grades. Beside the LMS, another technology, i.e. Youtube channel, is used to share teaching materials in the video format. Meanwhile products here mean innovations on the goals and assessments modifications. Goals are simplified into three (out of four) purposes only. While assessments only consist of the two (out of three) individual assignments and two (out of four) group works. As the evaluation given by students’ feedback, more teaching innovations are needed to balance the synchronous and asynchronous teaching approaches. Which means more technologies can be used to conduct online meeting (e.g. Zoom meeting, Google Meet) and interactive online quizzes (e.g. Kahoot, Mentimeter).
The Radar Absorption Material (RAM) method is a coating for reducing the energy of electromagnetic waves received by converting the electromagnetic waves emitted by radar into heat energy. Hemp has been studied to have the strongest and most stable tensile characteristics of 5.5 g/den and has higher heat resistance compared to other natural fibers. Combining the characteristics of hemp with alumina powder (Al2O3) and epoxy resin could provide a stealth technology system that is able to absorb radar waves more optimally, considering that alumina has light, anti-rust and conductive properties. The electromagnetic properties of absorbent coatings can be predicted using machine learning. This study classifies the reflection loss of Hemp-Alumina Composite using Random Forest, ANN, KNN, Logistic Regression, and Decision Tree. These machine learning classifiers are able to generate predictions immediately and can learn critical spectral properties across a wide energy range without the influence of data human bias. The frequency range of 2-12 GHz was used for the measurements. Hemp-Alumina composite has result that the most effective structure thickness is 5mm, used as a RAM with optimum absorption in S-Band frequencies of -15,158 dB, C-Band of -16,398 dB and X-Band of -23,135 dB. The highest and optimum reflection loss value is found in the X-Band frequency with a thickness of 5mm which is equal to -23.135 dB with an absorption bandwidth of 1000 MHz and efficiencyof 93.1%. From this result, it is proven that Hemp-Alumina Composite is very effective to be used as a RAM on X-Band frequency. Based on the results of the experiments, the Random Forest Classifier has the highest values of accuracy (0.97) and F1 score (0.98). The F1 score and accuracy of Random Forest are 0.96 and 0.97, respectively, and do not significantly differ from KNN.
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