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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!
Membangun Kesadaran Siswa terhadap Dampak Psikologis Bullying melalui Literasi Sekolah di MI Nurul Jadid Bondowoso Wahyuni, Dwi; Suwito, Surya Yudhisthira; Hendratno, Yustisia Ramadhani; Istian, Lala Bunga; Putri, Aulia Nur Maharani; Fadhilah, Arifa Nur; Lestari, Kiki Ayu; Puspito, Agung Nugroro; Angrenani, Arin Berliana; Fauziyah, Mailulah Ely
Jurnal Pengabdian Nasional (JPN) Indonesia Vol. 7 No. 2 (2026): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jpni.v7i2.1721

Abstract

This community service activity aims to build students' awareness of the psychological impact of bullying through a school literacy approach at MI Nurul Jadid Bondowoso. The main problem is the low level of student understanding regarding the forms and impacts of bullying, which are often considered normal. The program was carried out by seven students through three meetings on September 20, October 4, and October 19, 2025, using a participatory literacy approach involving discussions, video analysis, mini-dramas, and reflections. Evaluation results showed a 91% increase in student understanding based on a comparison of pre-test and post-test results. The activity concluded with the distribution of snacks and an educational trip to the lake behind the school as a means of applying the values of empathy and togetherness. Teachers responded positively as the activity helped students understand the dangers of bullying and fostered an attitude of mutual respect. The program succeeded in changing student behavior to become more caring and is expected to create a school environment free from bullying.
Effectiveness of Digital Simulation-Based Learning Approach in Optimizing Students’ Understanding of Queueing Models Using Real-Life Data Angrenani, Arin Berliana; Kurniati, Dian; Setiawani, Susi; Prihandini, Rafiantika Megahnia; Afifah, Ngizatul
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 11 No. 2 (2025)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v11i2.8850

Abstract

This study examines indications of the effectiveness of a digital simulation-based learning approach in supporting students’ understanding of queueing models using real-life data. Aquasi-experimental one-group pretest–posttest design, supported by qualitative interview data, was conducted with 31 undergraduate mathematics education students at the University of Jember. The ExtendSim software was used to create interactive queueing simulations that allowed students to explore parameters such as arrival rate, service rate, and waiting time. Validity and reliability tests were conducted using item–total (Pearson) correlations and Cronbach’s alpha, with results indicating high validity (r > 0.5, p < 0.05) and high internal consistency (a > 0.80). A paired ttest showed a statistically significant increase in scores within this sample (t = 8.89, p < 0.001). Students’ perceptions of the simulation were highly positive, with an average Likert score of 3.23 (very high). Qualitative interviews further indicated that the simulations helped students visualize queue dynamics and relate theoretical concepts to real-life contexts. There were also indications of increased motivation, engagement, and computational thinking skills; however, these findings are limited by the single-site sample and the one-group study design.