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All Journal BIMASTER Journal on Mathematics Education (JME) JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Jurnal Berdaya Mandiri Journal on Mathematics Education
Dwipa, Nendra Mursetya Somasih
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Journal : JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA

 1 
Identifikasi Model I-Garch (Integrated Generalized Autoregressive Conditionally Heterocedastic) Untuk Peramalan Value At Risk Dwipa, Nendra Mursetya Somasih
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol 3, No 1 (2016): Jurnal Derivat (Juli 2016)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (377.233 KB) | DOI: 10.31316/j.derivat.v3i1.626

Abstract

A stock returns data are one of type time series data who has a high volatility and different variance in every point of time. Such data are volatile, seting up a pattern of asymmetrical, having a nonstationary model, and that does not have a constant residual variance (heteroscedasticity). A time series ARCH and GARCH model can explain the heterocedasticity of data, but they are not always able to fully capture the asymmetric property of high frequency. Integrated Generalized Autoregresive Heteroskedascticity (IGARCH) model overcome GARCH weaknesses in capturing unit root. Furthermore IGARCH models were used to estimate the value of VaR as the maximum loss that will be obtained during a certain period at a certain confidence level. The aim of this study was to determine the best forecasting model of Jakarta Composite Index (JSI). The model had used in this study are ARCH, GARCH, and IGARCH. From the case studies were carried out, the result of forecasting volatility of stock index by using IGARCH(1,1) obtained log likelihood values that 3857,979 to the information criteria AIC = -6,3180; BIC = -6,3013; SIC = -6,3180; dan HQIC = -6,3117. Value of VaR movement of the JCI if it becomes greater the investment is Rp.500,000,000.00 with a confidence level of 95% on the date of July 2, 2015 using a model IGARCH (1,1) is Rp7.166.315,00.
Meta-Analisis Optimalisasi Kualitas Pembelajaran Matematika Dengan Integrasi STEM Dwipa, Nendra Mursetya Somasih
Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika Vol. 9 No. 2 (2022): Jurnal Derivat (Desember 2022)
Publisher : Pendidikan Matematika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jderivat.v9i2.4232

Abstract

Meta-analysis is a technique that uses certain measures to show the strength of the relationship between similar variables in a number of different research results. The unit of analysis in this study is the thesis reports of UPY mathematics education students taking the topic of integrating STEM (science, technology, engineering, mathematics) in learning mathematics. This study provides an introduction to the description of the implementation of STEM in students' mathematics learning, specifically aimed at knowing the improvement of the quality of the learning. The analytical technique used is library analysis on research reports taken purposively based on the research objectives. Quantitative analysis was carried out on percentage and nominal data, while qualitative analysis was carried out on study descriptions related to STEM implementation. The results of this study indicate that (1) in the aspect of mathematics learning outcomes, learning with STEM integration is able to describe students' abilities in the cognitive, affective, and psychomotor domains, (2) STEM learning is able to improve the quality of students' mathematics learning  Keywords: Meta-Analysis, STEM, Quality, Learning
Co-Authors Anggraeni, Ganung Laela Sagita Niken Wahyu Utami Rudha Widagsa