Upaya Meningkatkan Hasil Belajar Siswa pada Mata Pelajaran Sejarah Kebudayaan Islam melalui Strategi Inferencing

This research is motivated by the level of completeness of the learning outcomes of class III students of MI Rijalul Hikam Jatinagara, Jatinagara District, Ciamis Regency, which is still low in the SKI subject. The average student learning outcomes in the SKI subject obtained by students are still below the minimum completeness criteria of 75. However, the reality shows that 30% of 17 students have not reached the KKM while 70% of 17 students have reached the KKM. This is because the learning process is more teacher-centered and monotonous. Learning activities are mostly used to take notes so that students become bored. This situation makes students less understanding of the material being taught and easy to forget so that it affects the low student learning outcomes. The research method used was Classroom Action Research (CAR) with Kurt Lewin's model. The sequence of activities in Kurt Lewin's model is planning, acting, observing and reflecting. The data collection techniques used in this research were test, observation, interview and descriptive analysis techniques. Data analysis data selection, data correction and data weighting. After conducting the research, the following data can be obtained: 1) the ability of the teacher in preparing RPP SKI in the first cycle reaches an average of 82.61 (Good); in cycle II reached 86.5 (Good); and in the third cycle reached an average of 93.14 (Very good), 2) the ability of teachers to carry out the learning process of the IEC learning process in the first cycle reached an average of 79.02 (good); in cycle II reached an average value of 86.5 (good); and in cycle III reached an average value of 93.33 (very good). 3) student learning outcomes in the first cycle reached 83.05 (good) with 70.5% completeness in the calculation of 12 students completed, 5 students have not completed. In cycle II it reaches an average of 88.23 (good) with completeness 88.23% on the calculation of 15 students complete, 2 students have not completed and in cycle III reaches an average value of 92.88 (good) with 100% completeness in the calculation all students (22 students) completed. This proves that the Inferencing strategy (concluding) can improve student learning outcomes.

A Link between Machine Learning and Optimization in Ground-Motion Model Development: Weighted Mixed-Effects Regression with Data-Driven Probabilistic Earthquake Classification

ABSTRACT The steady increase of ground-motion data not only allows new possibilities but also comes with new challenges in the development of ground-motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed-effects model. We address this issue by introducing an extension of the mixed-effects model to incorporate data weighting. The parameter estimation of the mixed-effects model, that is, fixed-effects coefficients of the GMMs and the random-effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert-driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation–maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface- and intraslab-type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground-motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground-motion models using large databases with automated classification and processing of earthquake and ground-motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong-motion flat file of Bastías and Montalva (2016) with ∼2400 records from 319 events in the Chilean subduction zone. Our GMM with the data-driven classification is comparable to the expert-classification-based model. Furthermore, the model shows temporal variations of the between-event residuals before and after large earthquakes in the region.

Estimating a Finite Population Mean Using Transformed Data in Presence of Random Nonresponse

Developing finite population estimators of parameters such as mean, variance, and asymptotic mean squared error has been one of the core objectives of sample survey theory and practice. Sample survey practitioners need to assess the properties of these estimators so that better ones can be adopted. In survey sampling, the occurrence of nonresponse affects inference and optimality of the estimators of finite population parameters. It introduces bias and may cause samples to deviate from the distributions obtained by the original sampling technique. To compensate for random nonresponse, imputation methods have been proposed by various researchers. However, the asymptotic bias and variance of the finite population mean estimators are still high under this technique. In this paper, transformation of data weighting technique is suggested. The proposed estimator is observed to be asymptotically consistent under mild assumptions. Simulated data show that the estimator proposed is much better than its rival estimators for all the different mean functions simulated.

Comparing the performance of three data-weighting methods when allowing for time-varying selectivity

How to properly weight composition data is an important ongoing research topic for fisheries stock assessments, and multiple methods for weighting composition data have been developed. Although several studies indicated that properly accounting for time-varying selectivity can reduce estimation biases in population biomass and management-related quantities, no study to date has compared the performance of widely used data-weighting methods when allowing for time-varying selectivity. Here, we conducted four simulation experiments on this topic, aiming to provide guidance on weighting age-composition data given time-varying selectivity. The first simulation experiment showed that over-weighting should be avoided in general and even when estimating time-varying selectivity. The second simulation experiment compared three data-weighting methods (McAllister–Ianelli, Francis, and Dirichlet-multinomial), within which the Dirichlet-multinomial method outperformed the other two methods when selectivity is time-varying. The third and fourth simulation experiments further showed that given time-varying selectivity, the Dirichlet-multinomial method still performed well when age-composition data were over-dispersed and when the level of selectivity variation needed to be simultaneously estimated. Our simulation results support using the Dirichlet-multinomial method when estimating time-varying fishery selectivity. Also, the simulations suggest that improving stock assessments by accounting for time-varying selectivity requires simultaneously addressing data weighting and time-varying selectivity.

DEFINING THE IDEAL COMPETENCY MODEL FOR THE UNITED NATIONS MILITARY OBSERVERS (STUDY ON THE INDONESIAN MILITARY OBSERVERS)

<p>A military observer is a military officer from a certain country who is deployed as part of a United Nations Peacekeeping Mission. The purpose of this study is to define an ideal competency model for the UN military observers of the Indonesian military by considering certain characteristics embedded in superior military observers that distinguish them from the non-superior ones. This study applied several data collecting techniques such as behavioral event interviews, observation, and library study. Two types of analysis, namely: content analysis and discriminant analysis are utilized to create data weighting and to discover the differentiating characteristics. From this study, it can be concluded that there is a very close and significant relationship between the competency and performance of the UN military observers from Indonesia. Those in the superior category do have three strong differentiating competencies, namely: initiative, relationship building, and impacts and influence production. And then this study also found an ideal competency model for a UN military observer, where the model consists of the three competencies plus eight other competencies, namely: team working, self-controlling, local language skill, local culture knowledge, vehicle driving skill, military knowledge and skills, diplomacy and negotiation skills, and administrative skills.</p><p>Keywords: Competency, Competency model, Military observer, Recruitment, UN Peacekeeping</p>

Penerapan Metode Giving Question and Getting Answer untuk Meningkatkan Hasil Belajar Peserta Didik pada Mata Pelajaran Pendidikan Agama Islam

This class action research aims to improve the quality of student learning outcomes in Islamic Religious Education subjects. The alternative offered to achieve that goal is to introduce the giving question and getting answer method. This study uses the Classroom Action Research (CAR) method of Kurt Lewin's model. Data collection techniques used were observation and tests. Classroom Action Research in class VIII H, SMP Negeri 1 Baregbeg, Ciamis Regency. The steps of data analysis are as follows: data selection, data correction and data weighting. The results of this study prove that the giving question and getting answer method has succeeded in improving the quality of student learning outcomes in Islamic Religious Education subjects at the Baregbeg State Middle School in Ciamis Regency.

Trends in the atmospheric water vapour estimated from GPS data for different elevation cutoff angles

We have processed 20 years of GPS data from 8 sites in Sweden and 5 sites in Finland, using two different elevation cutoff angles 10° and 25°, to estimate the atmospheric integrated water vapour (IWV). We have also tested three additional elevation-angle-dependent parameters in the GPS data processing, i.e., (1) two different mapping functions, (2) with or without second order corrections for ionospheric effects, and (3) with or without elevation dependent data weighting. The results show that all these three parameters have insignificant impacts on the resulting linear IWV trends. We compared the GPS-derived IWV trends to the corresponding trends from radiosonde data at 7 nearby (