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Motivation to learn mathematics decreased due to the inability of teachers to implement innovative learning models and techniques. Therefore, this study aimed to investigate the effects of teachers' ability on students' motivation to learn mathematics by using quantitative methods and survey approaches. There were 32 mathematics teachers and 542 students in the 24 schools within the Depok region, selected as respondents through a stratified random sampling method. The research instruments of two questionnaires of teachers' competence and students' learning motivation were distributed to the respondents. Data analysis was conducted to test the random effect of teachers' ability on students' motivation to learn mathematics by using the effect of teachers' random intercepts and competence as models 1 and 2, respectively. These two models were analyzed using the n-level Structural Equation Model (nSEM), and the result showed that model 2 was the best one to investigate the random effect of teachers' ability and students' learning motivation. The data analysis showed that the variance among teachers' ability (0,0027) was less than learning motivation among students (0.0597). These findings indicated that the motivation levels of students taught by the same teacher varied significantly, whereas the effects of the teachers were relatively homogeneous. In other words, teachers' ability was somewhat the same in increasing students' learning motivation. Based on these findings, this research work suggests teachers keep improving their teaching techniques. Hence, students will be well motivated to learn so that the learning objectives will be well achieved.

The issue of missing data is a significant problem in time series. There have been various imputation techniques recommended for handling missing data. Among them, univariate imputation methods are frequently preferred for their adaptability. Nevertheless, in the case of multivariate time series data where the variables are interrelated, these methods tend to eliminate critical information. In such cases, multivariate imputation methods, such as Vector Autoregressive-Imputation Method or more commonly known as VAR-IM, are more suitable since they can enhance imputation accuracy by leveraging information from other variables. To further improve imputation accuracy, a modified VAR-IM called VAR-IMMA was introduced. The objective of this study was to assess and compare the effectiveness of the original VAR-IM method and the newly proposed VAR-IMMA method through the use of simulation studies. The simulation was repeated 100 times at different proportions of missing data from 5% to 30%. The data used were the monthly inflation rate of 82 cities in Indonesia from January 2014 to December 2019. The performances of each methods were evaluated by measuring RMSE and MAE. The results show that VAR-IMMA performs similarly to VAR-IM in data with 5% missing data. But, for larger percentages of missing data, the VAR-IMMA performs better with lower values of RMSE and MAE. [ABSTRACT FROM AUTHOR]

This article is concerned with the density estimation of Neonatal Mortality Rate (NMR) in Central Java Province, Indonesia. Neonatal deaths contribute to 73% of infant deaths in Central Java Province. The number of neonatal deaths for 35 districts/municipalities in Central Java Province is considered as Poisson distributed surrogate with NMR as the rate of Poisson distribution. It is assumed that each number of neonatal deaths by district/municipality in Central Java Province were realizations of unobserved NMR, which come from unknown prior density. We applied the Empirical Bayes Deconvolution (EBD) method for estimating the unknown prior density of NMR based on Poisson distributed surrogate. We used secondary data from the Health Profiles of Central Java Province, Indonesia, in 2018. The density estimation of NMR by the EBD method showed that the resulting prior estimate is relatively close to the Gamma distribution based on Poisson surrogate. This is implying that the suitability of the obtained prior density estimation as a conjugate prior for Poisson distribution. [ABSTRACT FROM AUTHOR]

This research explores the relationship between cognitive writing strategies and students' writing performance. The primary aim of this paper is to explore whether or not the two variables are interrelated. It also investigates how strong are the relationship between the two variables. The target observation was students at the moderate level. The sample was drawn systematically from population of 80 students at the English department Hasanuddin University 2008-2009 Academic year. The students were divided into 3 groups based on the results of their achievement test. The results indicated that 37 students included at the moderate level. Data were mainly obtained by means of achievement test where the students were asked to write about approximately 300 words in 60 minutes. The test was evaluated based on the five writing elements, assessed by two raters. Another way of obtaining data was through strategies questionnaire (10 Questions) and data were analyzed quantitatively using Kendall's thau-c formula. Qualitative analysis was also applied. The results of the quantitative analysis through their achievement test are not significant (0.298). Its contribution is 11.4%. In contrast, in relation to questionnaire, the result shows that the correlation is significant (0.000) and its contribution is 70.3%. [ABSTRACT FROM AUTHOR]