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All Journal JIPM (Jurnal Ilmiah Pendidikan Matematika) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Kunti Robiatul Mahmudah
Universitas Ahmad Dahlan
Decision Sciences, Operations Research & Management Education Mathematics

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ESTIMASI PARAMETER PADA MODEL SELEKSI SAMPEL HECKMAN DENGAN KOVARIAT ENDOGEN MENGGUNAKAN PENDEKATAN KEMUNGKINAN MAKSIMUM INFORMASI PENUH Kunti Robiatul Mahmudah
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i1.558

Abstract

The linear regression model is a statistical tool used to model the causal relationship of a dependent variable based on one or several independent or explanatory variables. In scenarios where the dependent variable is a censored variable and there is potential to exist sample selection, the sample selection model can be an alternative in analyzing this relationship. In the Heckman sample selection model, independent variables have the possibility of having an endogeneity effect, where they should be treated as endogenous variables in both the outcome equation and the selection equation instead of as exogenous variables. In result, by including endogenous covariates in the Heckman sample selection model, the sample selection model equation will have more than one equation and makes it a simultaneous equation. To estimate simultaneous equations, simple estimation methods such as the maximum likelihood estimator method are no longer appropriate. In this study, we will discuss the estimation of sample selection models with endogenous covariates utilizing the full information maximum estimator (FIML) approach. The sample selection model with endogenous covariates was then applied to the women labor supply data of Tomas Mroz's research and compared with several models. Based on the MSE and SSE values obtained from the linear regression model, Tobit regression model, Heckman sample selection model, and sample selection model with endogenous covariates, it was concluded that the Heckman sample selection model is the best model that fit the dataset since it yields the best results with the smallest MSE and SSE values
Utilization of ASGeoTri Media to Support the Development of Computational Thinking Skills in SMK Students Fauziah Sumarno; Suparman; Kunti Robiatul Mahmudah
JIPM (Jurnal Ilmiah Pendidikan Matematika) Vol. 14 No. 2 (2026): Article in Press
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/jipm.v14i2.23221

Abstract

Keterbatasan media pembelajaran berbasis teknologi masih menjadi kendala dalam pembelajaran perbandingan trigonometri di sekolah kejuruan, sehingga diperlukan inovasi media yang mampu mendukung pembelajaran interaktif sekaligus mengembangkan keterampilan berpikir komputasional (computational thinking) siswa. Penelitian ini bertujuan untuk mengembangkan media pembelajaran matematika ASGeoTri berbantuan Articulate Storyline 3 yang terintegrasi dengan GeoGebra serta menguji kevalidan, kepraktisan, dan efektivitasnya. Penelitian menggunakan metode research and development dengan model ADDIE yang meliputi tahap analisis, desain, pengembangan, implementasi, dan evaluasi. Subjek penelitian terdiri atas 25 siswa kelas X jurusan PPLG di SMKS Muhammadiyah Bitung. Data dikumpulkan melalui validasi ahli, angket respons siswa, tes hasil belajar, dan lembar analisis keterampilan berpikir komputasional. Implementasi pembelajaran dilakukan dalam tiga pertemuan dengan menggunakan model Problem-Based Learning. Hasil penelitian menunjukkan media ASGeoTri valid dengan skor 4,27 (ahli media) dan 4,03 (ahli materi), serta sangat praktis dengan respons siswa sebesar 85,47%. Sebanyak 80% siswa mencapai ketuntasan belajar. Analisis deskriptif menunjukkan peningkatan keterampilan berpikir komputasional pada aspek dekomposisi, pengenalan pola, abstraksi, dan pemikiran algoritma. Temuan ini menunjukkan bahwa media ASGeoTri layak digunakan sebagai media pembelajaran interaktif dan berpotensi mendukung pengembangan keterampilan berpikir komputasional siswa pada materi perbandingan trigonometri.   The limitations of technology-based learning media continue to pose challenges in the teaching of trigonometric comparisons in vocational schools. Therefore, there is a need for innovative media that facilitate interactive learning and enhance students' computational thinking (CT) skills. This research aims to develop the ASGeoTri mathematics learning media, which is supported by Articulate Storyline 3 and integrated with GeoGebra, and to evaluate its validity, practicality, and effectiveness. The study employs a research and development approach based on the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The subjects of the research consist of 25 students from class X of the PPLG program at SMKS Muhammadiyah Bitung. Data was collected through expert validation, student response questionnaires, learning outcome tests, and analysis sheets for CT skills. The learning process was implemented over three sessions using the Problem-Based Learning model. The results indicate that the ASGeoTri media is valid, with scores of 4.27 (media expert) and 4.03 (content expert), and is highly practical, receiving a student response rate of 85.47%. Furthermore, 80% of students achieved complete learning. Descriptive analysis reveals an improvement in CT skills in decomposition, pattern recognition, abstraction, and algorithmic thinking. These findings suggest that ASGeoTri media is suitable for use as an interactive learning tool and has the potential to support the development of students' CT skills in the context of trigonometric comparisons.
Co-Authors Fauziah Sumarno Suparman