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All Journal Zeta - Math Journal Journal Focus Action of Research Mathematic (Factor M) Proceeding International Conference on Education
Ahadiyah, Kurnia
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Application of binary logistic regression analysis to factors that influence participation in the 2024 presidential election Ahadiyah, Kurnia; Dewi, Ardiana Fatma; Romadewanti, Shinta Hircatanu
Journal Focus Action of Research Mathematic (Factor M) Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024)
Publisher : Universitas Islam Negeri (UIN) Syekh Wasil Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/f_m.v7i2.3703

Abstract

Social media plays a very influential role, especially in the world of politics and elections. The 2024 elections in Indonesia show how social media can influence political dynamics. This research aims to analyze the influence of social media on the political participation of IAIN Kediri students in the 2024 Presidential Election, as well as understand how social media shapes public opinion and polarizes political views, with a focus on its impact on political participation among students. Data from the Indonesian Internet Service Providers Association (APJII) shows that the majority of the Indonesian population actively uses social media, so political candidates use platforms such as YouTube, Facebook, Instagram, and TikTok to attract support. Social media accelerates the delivery of political messages and has the potential to strengthen polarization and spread misleading information. In this research, a binary logistic regression model is used to analyze the factors that influence student participation in the 2024 presidential election. The findings show that students who actively follow news in mass media have a 7,157 times greater chance of participating in the 2024 presidential election compared to those who do not follow the news. These results emphasize the importance of social media in motivating political participation among students and provide insight into how social media can be utilized to improve the integrity and quality of democracy.
Agglomerative Hierarchy Clustering Pada Penentuan Kelompok Kabupaten/Kota di Jawa Timur Berdasarkan Indikator Pendidikan Dewi, Ardiana Fatma; Ahadiyah, Kurnia
Zeta - Math Journal Vol 7 No 2 (2022): November
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2022.7.2.57-63

Abstract

Salah satu cara agar dapat meningkatkan kondisi pendidikan di setiap kabupaten/kota di Jawa Timur adalah dengan menggunakan analisis pengelompokan sesuai dengan karakteristik pendidikannya. Analisis Klaster merupakan analisis yang termasuk ke dalam golongan analisis multivariat yang bertujuan untuk mengelompokkan objek berdasarkan karakteristik yang dimilikinya. Penelitian ini menggunakan analisis klaster tipe agglomerative dengan lima metode. Analisis klaster ini disebut dengan “agglomerative hierarchy clustering”. Tujuan penelitian ini adalah membandingkan metode analisis klaster herarki tersebut dengan mencari model terbaik melalui pencarian nilai koefisien korelasi cophenetic terbesar. Data yang digunakan pada penelitian ini merupakan data sekunder yang diperoleh dari Badan Pusat Statistik (BPS) Jawa Timur dan Kementerian Pendidikan Republik Indonesia. Pada pengelompokan Kabupaten/Kota di Provinsi Jawa Timur berdasarkan indikator pendidikan dengan menggunakan metode Agglomerative Hierarchical Clustering dihasilkan bahwa algoritma optimal yang digunakan yaitu pada algoritma average linkage dengan nilai korelasi cophenetic yang diperoleh sebesar 0,807. Berdasarkan indikator pendidikan dari 38 Kabupaten/Kota di Provinsi Jawa Timur terbagi menjadi dua cluster dimana pada cluster pertama beranggotakan 3 Kabupaten/Kota dan cluster kedua 35 Kabupaten Kota. Keywords: Agglomerative, Clustering, Hierarchical, cophenetic
Using Photomath Applications on Student Learning Outcomes in Advanced Calculus Courses Dewi, Ardiana Fatma; Ahadiyah, Kurnia
Proceeding International Conference on Education Volume 01, Agustus Tahun 2023: International Conference on Education
Publisher : Faculty of Tarbiyah, Institut Agama Islam Negeri (IAIN) Kediri, Indonesia

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Abstract

Along with the development of the times, the world of education is much influenced by advances, especially in the field of information and communication technology. Many educators use technology as a learning medium to help students learn and solve problems, especially math problems. There are several applications that students use to help them complete lecturer assignments. Photomath is one of the applications used by students in mathematics lessons. This application can be accessed via a smartphone and has features that help students solve math problems. This study aims to determine the effect of using the Photomath application on the learning outcomes of Tadris Mathematics students in semester 4 of the State Islamic Institute (IAIN) Kediri. The number of samples used in this study were 45 students, which were divided into two groups. The experimental group consisted of 24 students and the control group consisted of 21 students. The analysis used in this study is a t-test to compare whether there are differences in student learning outcomes in advanced calculus courses when solving problems using the Photomath application and not using these tools. The results obtained show that there are significant differences between students who use the help of the Photomath application and those who do not. So it can be concluded that the use of the Photomath application has an effect on student learning outcomes at IAIN Kediri's mathematics tadris in advanced calculus courses.
Analysis of Factors Affecting Mathematics Learning Outcomes With Internal Locus of Control As a Mediator Taningrum, Novita Suci Yuen; Ahadiyah, Kurnia; Septianawati, Erni
Proceeding International Conference on Education Volume 02, Agustus Tahun 2024: International Conference on Education
Publisher : Faculty of Tarbiyah, Institut Agama Islam Negeri (IAIN) Kediri, Indonesia

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Abstract

Mathematics learning outcomes are optimal when an individual reaches a level of achievement that aligns with their potential and abilities. The factors that influence student learning outcomes come from two main aspects: external factors and internal factors. External factors include the influence of family, school, and the community environment, while internal factors encompass psychological dimensions, physical conditions, and the student's level of fatigue. This research analyzes the factors influencing mathematics learning outcomes through the internal locus of control as a mediator. The type of research is a survey with a quantitative approach. The research location was carried out at SMAN 2 Kediri City. The population of this study were all class XI students of SMAN 2 Kediri City. The sampling technique is simple random sampling, with a sample size of 100 SMAN 2 Kediri City students. Data collection techniques use family socioeconomic status questionnaires, psychological distress questionnaires, internal locus of control questionnaires, and third-semester student report cards. Data analysis was carried out using descriptive statistical analysis and inferential statistics. The research results show that (1) family socioeconomic status influences mathematics learning outcomes, (2) psychological distress influences mathematics learning outcomes, (3) internal locus of control influences mathematics learning outcomes, (4) internal locus of control cannot mediate between family socioeconomic status and mathematics learning outcomes, (5) internal locus of control can mediate between psychological distress and mathematics learning outcomes, and (6) the model in the research is acceptable.
Perbandingan Model Generalized Ammi (Gammi) dengan Row Column Interaction Model pada Interaksi Genotipe dan Lingkungan Ahadiyah, Kurnia; Dewi, Ardiana Fatma
Journal Focus Action of Research Mathematic (Factor M) Vol. 4 No. 2 (2022)
Publisher : Universitas Islam Negeri (UIN) Syekh Wasil Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/factor_m.v4i2.4189

Abstract

Model Generalized AMMI (GAMMI) merupakan perluasan dari model AMMI (Additive Main Effect and Multiplicative Interaction). Model GAMMI melibatkan konsep Generalized Linear Model (GLM) pada variabel responnya. Pada penelitian ini, model GAMMI digunakan untuk data interaksi antara genotipe dan lingkungan yang mempunyai distribusi poisson. Sama halnya dengan model AMMI, model GAMMI juga digunakan untuk menganalisis kestabilan genotipe pada lingkungan yang beragam dengan pengaruh utama perlakuan dimodelkan dengan model aditif sedangkan pengaruh interaksi dimodelkan dengan model multiplikatif (bilinier). Metode lain yang memiliki kemiripan dengan model GAMMI adalah Row Column Interaction Model (RCIM). Model ini juga dapat digunakan untuk data yang berdistribusi poisson. Kedua model ini akan dibandingkan nilai analisis devian dan biplotnya. Interpretasi kedua model ditunjukkan melalui biplot dengan penguraian Singular Value Decompotition (SVD) pada matriks interaksi. Data yang digunakan untuk membandingkan kedua metode tersebut adalah data hama kedelai yang berisi empat genotipe dan lima jenis hama kedelai. Penelitian ini lebih ditekankan pada perbandingan hasil pemodelan dengan cara yang berbeda. Kedua metode menunjukan nilai peluang yang hampir sama yaitu untuk model GAMMI dengan regresi bolak-balik sebesar 0,0541, sedangkan model RCIM sebesar 0,0548. Keduanya sama-sama signifikan pada model GAMMI2 karena nilai peluang <0,06.   Generalized AMMI (GAMMI) model is a development of the AMMI (Additive Main Effect and Multiplicative Interaction) model. Model GAMMI involves the concept of Generalized Linear Model (GLM) on the response variable. In this research, GAMMI model used for interaction of genotype and environment data that have poisson distribution. Similar to the AMMI model, GAMMI model also used to analyze the stability of the genotype in any different environment with the main effect of treatment is modeled by additive model, while the effect of the interaction is modeled by multiplicative model (bilinear). Another method which is similar to GAMMI model is Row Column Interaction Model (RCIM). This model also can used for the data that have poisson distribution. These two models will be compared with the analysis value of the deviance and biplot. Interpretation of the model is shown through the biplot with Singular Value Decompotition (SVD) toward interaction matrix. The data used to compare the two methods is soybean pest data which contains four genotypes and five of soybean pests. This research emphasizes on comparing the results of modeling in different ways. The results of the analysis of the two methods show that the probability value is almost the same, for the GAMMI model with alternating regression is 0.0541, while the RCIM model is 0.0548. Both are equally significant in the GAMMI2 model because the probability value is <0.06.
Co-Authors Dewi, Ardiana Fatma Romadewanti, Shinta Hircatanu Septianawati, Erni Taningrum, Novita Suci Yuen