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All Journal KADIKMA Jurnal Cendekia : Jurnal Pendidikan Matematika Data Science: Journal of Computing and Applied Informatics J-PiMat : Jurnal Pendidikan Matematika JURNAL AXIOMA : Jurnal Matematika dan Pembelajaran
Angrenani, Arin Berliana
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Computer Science & IT Education Mathematics

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The Effect of Lecture Timing on Learning Outcomes in Multivariable Calculus Using One-Way ANOVA Afifah, Ngizatul; Angrenani, Arin Berliana; Mutammam, Muhamad Badrul; Fazira, Shima Kunaza; Firmansyah, Frenza Fairuz; Salim, Abdurrahman
Kadikma Vol 15 No 3 (2024): Desember 2024
Publisher : Department of Mathematics Education , University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/kdma.v15i3.52846

Abstract

This study aims to investigate the effect of lecture timing on learning outcomes in Multivariable Calculus using One-Way ANOVA. The type of research is quantitative. The subjects of this study are students of the Mathematics Education program at Jember University from class of 2023, for the Academic Year 2024-2025, enrolled in the regular Multivariable Calculus course. The samples were purposively selected: Class A (morning class) with 35 students, Class B (afternoon class) with 37 students, and Class E (evening class) with 30 students. The sample data were free of outliers, normally distributed, and homogeneous. The results show that and p-value < 0.05, indicating that at a 95% confidence level, there is at least one pair of treatments with significantly different average learning outcomes. Based on the Post Hoc test, significant differences in average learning outcomes were found between Class A and Class B, and between Class A and Class E, while betweet Class B and Class E did not show significant differences in average learning outcomes. Keywords: learning outcomes, lecture timing, One-Way ANOVA
Aplikasi Uji Wilcoxon Signed Rank untuk Menganalisis Pengaruh COVID-19 pada Prestasi Belajar Afifah, Ngizatul; Setiawani, Susi; Prihandini, Rafiantika Megahnia; Angrenani, Arin Berliana; Putri, Inge Wiliandani Setya; Salim, Abdurrahman
Jurnal Axioma : Jurnal Matematika dan Pembelajaran Vol. 10 No. 2 (2025): Juli
Publisher : Universitas Islam Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56013/axi.v10i2.3668

Abstract

Education in Indonesia faced significant challenges during the COVID-19 pandemic, particularly with the transition from face-to-face to online learning. Many obstacles occur in this online learning system which may affect student achievement. This research aims to analyze the effect of the COVID-19 pandemic on student achievement which is reviewed statistically descriptively and inferentially. This research was conducted at SMK Ma'arif 9 Kebumen with a sample of one class X. The research method used was using a quantitative approach, namely the Wilcoxon Signed Rank Test. The analysis results show that based on a 95% confidence level, there is a significant difference between the average learning achievement of students before the pandemic and after the COVID-19 pandemic occurred, with a decrease in the average student scores of 2.98% from the average student scores before the COVID-19 pandemic. Keywords: Wilcoxon Signed Rank Test, COVID-19's Impact, Learning Achievement
Penalaran Matematis Mahasiswa dalam Konteks Logika Fuzzy: Studi terhadap Keluaran Mini Riset Mahasiswa Fazira, Shima Kunaza; Jatmiko, Dhanar Dwi Hary; Afifah, Ngizatul; Angrenani, Arin Berliana; Firmansyah, Frenza Fairuz; Mutammam, Muhamad Badrul
Jurnal Cendekia : Jurnal Pendidikan Matematika Vol 9 No 2 (2025): Jurnal Cendekia: Jurnal Pendidikan Matematika Volume 9 Nomor 2 Tahun 2025
Publisher : Mathematics Education Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cendekia.v9i2.4334

Abstract

Artikel ini bertujuan untuk mendeskripsikan kemampuan penalaran matematis mahasiswa yang tergambar melalui hasil mini riset pada mata kuliah Logika Fuzzy. Penelitian ini menggunakan metode deskriptif kualitatif untuk mendeskripsikan kemampuan penalaran matematis mahasiswa dalam konteks Logika Fuzzy, yang tergambar melalui hasil mini riset pada mata kuliah Logika Fuzzy. Hasil penelitian menunjukkan bahwa sebagian besar mahasiswa mampu mengjukan dugaan, manipulasi matematika, menyusun bukti, dan menarik kesimpulan yang relevan. Namun, beberapa laporan mini riset mahasiswa menunjukkan kesulitan dalam memverifikasi argumen dan menarik kesimpulan yang lebih akurat. Dari penelitian ini dapat disimpulkan bahwa mahasiswa menunjukkan kemampuan penalaran matematis yang baik dalam konteks Logika Fuzzy, meskipun beberapa aspek masih perlu ditingkatkan.
Preventing recession through GDP growth prediction: A classical and machine learning classification approach Saputri, Prilyandari Dina; Angrenani, Arin Berliana; Fitriana, Ika Nur Laily
Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 2 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v7.i2-10507

Abstract

Classification methods are a popular method applied in many various fields of science. To represent the effect of predictor factors on categorical response variables, different machine learning classification algorithms are used, namely logistic regression, neural network (NN), random forest, support vector machine (SVM), and bayesian model averaging (BMA). Every classifier has its unique characteristic, performing well in certain datasets but not in others. Hence, it is always a quest to find the best classifier to use for a certain dataset. Economic growth, most commonly using a gross regional domestic product, is experiencing a recession or acceleration, especially before and during the COVID-19 pandemic. This research proposed a comparison of classification methods using regional GDP data for 2019-2020, before and during the COVID-19 pandemic, by predictor variables; percentage of workers, foreign direct investment (PMA), regional revenue (PAD), general allocation fund (DAU), revenue sharing fund (DBH), and the dummy of COVID-19. The results are that all selected machine learning models can classify the regional GDP growth perfectly for the training data, but, NN model outperforms the other methods with an accuracy of 100% in training and testing data. COVID-19 and the PMA are the most significant variables predicting regional GDP growth for all models. Further research relating to interpretable machine learning, such as feature interaction, global surrogate, and Shapley values, is also necessary to predict regional GDP growth using machine learning methods.
Analisis Faktor-Faktor yang Mempengaruhi Nilai Ujian Mahasiswa Pada Mata Kuliah Kombinatorika Angrenani, Arin Berliana
J-PiMat : Jurnal Pendidikan Matematika Vol 7, No 1 (2025): J-PiMat
Publisher : Prodi Pendidikan Matematika STKIP Persada Khatu;istiwa Sintang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31932/j-pimat.v7i1.4704

Abstract

Keberhasilan mahasiswa dalam menempuh suatu mata kuliah dipengaruhi oleh berbagai faktor, baik yang bersifat internal maupun eksternal. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi nilai ujian mahasiswa pada mata kuliah Kombinatorika. Faktorfaktor yang diteliti meliputi lama belajar, banyaknya latihan soal, gaya belajar (visual, auditori, kinestetik), dan waktu kedatangan ke tempat ujian. Metode penelitian yang digunakan adalah kuantitatif eksplanatif dengan teknik analisis regresi linier berganda. Data dikumpulkan melalui survei menggunakan Google Form dan hasil koreksi ujian mahasiswa FKIP Pendidikan Matematika UNEJ. Hasil penelitian menunjukkan bahwa lama belajar, banyaknya latihan soal, dan waktu kedatangan memiliki korelasi signifikan dengan nilai ujian. Gaya belajar juga berpengaruh, di mana mahasiswa dengan gaya belajar visual cenderung memiliki nilai lebih tinggi dibandingkan auditori dan kinestetik. Model regresi menunjukkan bahwa variabilitas data yang dapat dijelaskan oleh model adalah 81,24%. Kata Kunci: Kombinatorika, Nilai Ujian, Lama Belajar, Latihan Soal, Gaya Belajar.Rasakan keseruan bermain di om88 dengan peluang menang yang lebih besar. Nikmati pengalaman bermain yang seru, aman, dan penuh tantangan. Segera coba dan raih kemenangan besarmu!
Co-Authors Abdurrahman Salim Afifah, Ngizatul Fazira, Shima Kunaza Firmansyah, Frenza Fairuz Fitriana, Ika Nur Laily Jatmiko, Dhanar Dwi Hary Mutammam, Muhamad Badrul Putri, Inge Wiliandani Setya Rafiantika Megahnia Prihandini Saputri, Prilyandari Dina Susi Setiawani