Kismiantini's research while affiliated with Universitas Negeri Yogyakarta and other places
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Publications (31)
Studies found that the variables of student engagement at school and teacher support have a significant impact on mathematics achievement. This paper aims to examine the relationship between student engagement and teacher support on the mathematics achievement of Indonesian students. The data came from PISA 2018 Indonesia, which included 9,936 stud...
Anxiety can arise as a result of negative interactions between teachers, students, and the school environment. The purpose of this study was to identify the effect of mathematics anxiety on students' mathematics learning achievement by gender. The data for this study comes from PISA 2018 Indonesia, which includes 9,058 students from 329 schools who...
Attitude is an important factor in understanding mathematics. This study aims to investigate the relationship between attitudes and school quality to student's mathematics achievement in Indonesia using PISA 2018 data. The multilevel analysis showed that student gender, student behavior, parental role and quality school were statistically significa...
Many learning theories say that raising students' cooperative attitudes, competitive attitudes, and paying attention to the teacher's teaching style in every lesson are appropriate actions in learning that may improve the student achievement. This study uses data from the PISA 2018 Indonesia to investigate the factors that influence the mathematics...
The importance of student's perseverance and mathematic work ethic toward mathematics achievement has been acknowledged by the existing research literature. This study examined the role of student's perseverance, mathematics work ethic, and modern school qualification towards mathematics achievement in Indonesia. By applying a multilevel modeling t...
Self-efficacy is an individual's belief in the ability that a student should have. The student's self-efficacy can affect mathematics achievement. This study aims to examine relationship of self-efficacy, parental support, ability grouping, and school status on mathematics achievement in Indonesia using PISA 2018 data. A sample used in this study w...
School climate has been shown to have a positive impact on students’ mathematics achievement. The school climate plays an important role in the advancement of education. The purpose of this study is to determine the effect of school climate on the student's mathematics achievement in Indonesia. This study used PISA 2018 Indonesia data with 12,098 s...
Bidang pendidikan pada saat ini merupakan salah satu prioritas yang menjadi urgensi pemerintah. Berdasarkan Badan Pusat Statistik 2021, tercatat bahwa angka putus sekolah di Provinsi Jawa Barat menduduki peringkat pertama dari 34 provinsi di Indonesia. Fenomena ini tentunya menurunkan indeks pendidikan serta bertolak belakang dengan intensi penting...
Many different arguments have been offered in order to explain how social economic, student attitude and school size might affect mathematics achievement. This study focuses on explaining the importance of school size and student teacher ratio in affecting students’ mathematics scores in Indonesia using data from PISA 2018. Total observations used...
Many research believed that socioeconomic status and school resources were variables that played a significant role in the mathematical achievement of students. This study analyzed the relationship between socioeconomic status and school resources to mathematics achievement. Data were drawn from 9,979 students in 338 schools in Indonesia who partic...
Mathematical skills are one of the most important abilities for students, especially when faced with the Industrial Revolution 4.0 era, as well as skills in science and reading. This study aims to examine the relationship of students’ backgrounds on mathematics, science and reading achievements using PISA 2018 Indonesia for Daerah Istimewa Yogyakar...
Many factors affect students’ mathematics achievement. In addition to cognitive factors, many studies also highlight and show that non-cognitive factors of students and school resources become important factors in influencing students’ mathematical achievement. This study analyzed the relationship between students’ non-cognitive factors and school...
Researchers found that non-cognitive factors and the school of students can affect the academic achievement of students during the educational phase. The goal of this study is to examine the relationship between non-cognitive factors and school characteristics to mathematics achievement. Data used in this study were taken from 9262 students in 334...
Gender differences and psychological attributes have important role in learning process and achievement, including in mathematics fields. The psychological attributes have different effect to students’ achievement based on the gender. This study aims to compare the impact of competitiveness, fear of failure and resilience to mathematics achievement...
Prior studies showed that both girls and boys typically use their metacognitive skills in learning. The aim of this study is to examine the aspects measuring the students’ mathematical achievement in Indonesia. By applying multilevel analysis to the Programme for International Student Assesment (PISA) 2018 data for Indonesia, this study showed that...
In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as "leave one environment out," is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accu...
The purpose of this study is to map drought-prone areas in Gunungkidul district using the fuzzy c-means method, making it easier for the government to allocate water-dropping assistance to drought-affected areas. The research variables include rainfall, soil type, infiltration, slope, and land use. The type of variables is in an ordinal scale, so t...
Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF...
In high dimensional data, Principal Component Analysis (PCA)-based Pearson correlation remains broadly employed to reduce the data dimensions and to improve the effectiveness of the clustering partitions. Besides being prone to sensitivity on non-Gaussian distributed data, in a high dimensional data analysis, this algorithm may influence the partit...
Shifting students to a growth mindset can increase their achievements. Nevertheless, only a few studies have been conducted on this topic in developing countries. This study aims to examine the relationship between growth mindset, school context, and mathematics achievement in Indonesia. Using a multilevel model on the PISA 2018 data, this study ex...
Genomic selection (GS) is revolutionizing plant breeding since the selection process is done with the help of statistical machine learning methods. A model is trained with a reference population and then it is used for predicting the candidate individuals available in the testing set. However, given that breeding phenotypic values are very noisy, n...
A farmer’s welfare classification can be performed to accommodate all significant issues that will assist policymakers, government, and scientists. This study aims to compare K-Nearest Neighbor (K-NN) and K-Means methods for clustering Indonesian farmers’ welfare using the fifth wave of Indonesia Family Life Survey (IFLS 5) data. The K-Means method...
Diarrhea becomes a severe problem for children under five years of age. A preventive action is needed to minimize the negative effect of diarrhea. Gender risk assessment may be necessary to control diarrhea transmission as different sexes have distinct healthy behaviour. We develop a collection of candidate models of Bayesian shared component rando...
Obesity is one of the major issues in many countries, including in Indonesia. In this study, the obesity category based on body mass index (BMI) is categorized into four levels, namely non-obese with BMI below 30 kg/m ² , obesity I with BMI 30 – 34.9 kg/m ² , obesity II with BMI 35 – 39.9 kg/m ² , and obesity III with BMI above 40 kg/m ² . The obje...
Introduction: this paper presents the development and validation of an instrument to measure the perceptions and attitudes about the production and consumption of the Mexican urban consumers towards genetically modified organisms (GMOs). Method: The proposed questionnaire contains 63 questions that encompassed 11 latent factors that was applied to...
Health status of a population plays an important role in developing a country. A better health can promote economic growth and foster development of the country. The aim of this study was to investigate relationships among age, sex, weight, height, smoking behavior, and blood pressure on health status of adults in Indonesia. The path analysis was c...
Happiness can be an indicator of social progress achievement in developing a country. Understanding factors affecting the level of happiness in a country become important in the study of subjective well-being. This study aims to determine factors that influence happiness in Indonesia using the fifth wave of Indonesian Family Life Survey (IFLS) data...
Having health insurance is an important decision for enjoying the security of a safe future. Health insurance can protect people from a large amount of medical costs. More Indonesian have health insurance now a days than ever before since the government is committed to supporting the universal health care. This study aims to determine factors affec...
Today, breeders perform genomic-assisted breeding to improve more than one trait. However, frequently there are several traits under study at one time, and the implementation of current genomic multiple-trait and multiple-environment models is challenging. Consequently, we propose a four-stage analysis for multiple-trait data in this paper. In the...
The Mexican Social Security Institute (IMSS) belongs to the Mexican health sector and provide health services to beneficiaries, employers, pensioners and retirees across Mexico. However, there are evidences that beneficiaries are not satisfied with the health services they receive. For this reason, with a sample of 417 out of 669 workers of the Gen...
Citations
... The ability to integrate heterogenous data into a model is a known strength of machine learning models in general, and deep learning models in particular. However, this research is one facet of a large, active research community that seeks to improve GS accuracy, using various models, through integration of types of environmental data (Costa-Neto et al., 2022;Montesinos-López et al., 2022;Putra et al., 2022;Song et al., 2022). In this survey, our aim is to provide a comprehensive overview of genomic selection process with deep learning that starts from data and ends with creating a new variety for both single and multi-environment trial. ...
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... Aside from traditional cluster algorithms, the usage of PCA has proven to be effective in identifying the number of clusters and improving cluster accuracy. Previous studies [23,24] have demonstrated the capability and applicability of PCA in identifying the number of clusters for rainfall patterns. However, the classical PCA can be insensitive to outliers, as it assigns equal weights to each set of observations [25]. ...
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... The data used in this study is PISA 2018 data. PISA is a three-year survey of 15-year-old students who have acquired the main knowledge and skills to participate in society [21]. The 2018 PISA data set in Indonesia covers 12,098 students and 397 schools. ...
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... Recent studies have applied machine/deep learning to integrate HTP and environmental and genetic data for selecting varieties under field trial conditions. For example, a study on wheat used generalized Poisson regression, a statistical machine learning method, to merge hyperspectral images with environmental and genetic data to predict count phenotypes for GS [97]. Another study used canopy temperature and vegetation indices for the GS of wheat, highlighting that adding the HTP features increased the yield prediction by 70% in genomic models [98]. ...
Reference: Book-PlantGenotyping-MethodsProtocols-2023
... Idealnya, petani yang mengelola lahan lebih luas akan semakin meningkat pendapatannya. Dengan menggunakan Metode K-Means dan K-NN, Pawa & Kismiantini (2020) government, and scientists. This study aims to compare K-Nearest Neighbor (K-NN melakukan penelitian tentang pengelompokan kesejahteraan petani di Indonesia. ...
... Thus, this study adopted used a binary logit model to estimate how the total sales of HNEs are influenced by credit access in Nigeria. Since the total sales of HNEs is a measurement variable with two categories (dichotomous), this study followed the modelling approach of Astari and Kismiantini (2019). In the binary logit model used for this study, π is used to denote the total sales of HNEs, while X i is used to denote the set of independent variables. ...
... In the area of well-being research, the demand for ordinal response data analysis is increasing tremendously (Arvidsson, 2019;Krys et al., 2019;Schmidt, Clouth, Haggenmüller, Naber, & Reitberger, 2006;Winkelmann, 2005), and therefore, it is crucial for researchers to understand the principle for analysing ordinal response variables. There are a limited number of studies on the application of ordinal regression for modelling the level of family well-being, particularly with covariates involving the demographic and social characteristics of the respondents (Arvidsson, 2019;Pratiwi & Kismiantini, 2019;Soukiazis & Ramos, 2016;Winkelmann, 2005). The demographic factors considered are ethnicity, locality, family type, education level and household income. ...
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... The main assumptions that need to be tested in path analysis, according to Nurmawati and Kismiantini [9] are based on the premise of multiple linear regression, namely, the residual values are normally distributed, the residual values between exogenous variables are not correlated, and the residual values of exogenous variables have the same variance. Testing the path analysis residual assumptions in this study was carried out by utilizing the R program. ...
... Two types of MT models were implemented. Firstly, by performing single value decomposition (SVD) of the matrix of all phenotypes, as proposed by Montesinos-López et al. (2019a), whereby each of the decomposed and uncorrelated vectors from all the traits were predicted as traits themselves using the same genomic models as for ST predictions. The predictions of vectors were then back-transformed to the original trait scales to derive the MT predictions pertrait. ...
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... El CFA es una técnica estadística utilizada para evaluar modelos de medición que representa una hipótesis sobre las relaciones entre los reactivos y los factores (Geiser et al., 2014;Kline, 2011). Supone que los factores latentes son los causantes de los puntajes observados en los reactivos (Kismiantini et al., 2014). En los modelos de ecuaciones estructurales (SEM, por sus siglas en inglés) el modelo multigrupos y el modelo de múltiples indicadores y múltiples causas (MIMIC, por sus siglas en inglés) para examinar modelos de dos o más grupos. ...
