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All Journal AlphaMath: Journal of Mathematics Education Generation Journal
Nur'aini Muhassanah
Universitas Nahdlatul Ulama Purwokerto
Computer Science & IT Mathematics

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The Effectiveness of Mathematics Learning Using Online Media During the Covid-19 Pandemic Nur'aini Muhassanah; Afifah Hayati; Ambar Winarni
AlphaMath : Journal of Mathematics Education Alphamath: Vol. 8, No. 2, November 2022
Publisher : Department of Mathematics Education, Universitas Muhammadiyah Purwokerto, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/alphamath.v8i2.13540

Abstract

The Covid-19 pandemic has caused the learning process to be hampered and cannot be carried out face-to-face. Therefore, a solution is needed for this problem. One alternative to overcome this problem is online learning. This study aims to describe the effectiveness of online learning using online media during the Covid-19 pandemic in Mathematics courses. The subjects of this study were 51 students in the Faculty of Science and Technology, Nahdlatul Ulama University, Purwokerto. They were from five different study programs, namely Agrotechnology, Mathematics, Biology, Fisheries Science, and Agricultural Biosystems Engineering, who had taken mathematics courses. This study was carried out in June – August 2021. This study used closed, semi-closed, and open-ended questionnaires as research instruments. Then, the data collection procedure was done through Google Forms. The data obtained were then selected using purposive sampling. To get valid and reliable data, triangulation was applied in this study. Triangulation was carried out through interviews with five students whose data were selected randomly. The researchers used descriptive statistics as the data analysis. The study found that the activities most often carried out by students during Study From Home (SFH) are playing on cellphones or laptops, doing assignments, working, and sleeping. Then, related to online learning, students prefer to use WhatsApp Group media and lectures. The preferred assignments or assessments are individual assignments and quizzes. Furthermore, the learning model most favored by students is the face-to-face learning model because students consider online learning ineffective. The results of this study can be used as a reference in choosing the suitable model, method, and learning media. Therefore, online learning can be held effectively and easily accepted by students during the Covid-19 pandemic.
Classification of Diabetes Mellitus (DM) Using the Naïve Bayes Method with Chi-Square Variable Selection Farhan Arizal Ginanjar Ginanjar; Ambar Winarni; Nur'aini Muhassanah
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.27878

Abstract

Diabetes mellitus (DM) is a chronic disease that can cause serious complications, making early detection essential. Technological advances enable the use of data mining techniques, particularly the Naïve Bayes classification method, to support early diabetes detection. Although Chi-Square variable selection is known to improve Naïve Bayes accuracy, studies examining the impact of different significance levels remain limited. Therefore, this study applies the Naïve Bayes method with and without Chi-Square variable selection at three significance levels (α = 0.05, α = 0.01, and α = 0.001) to evaluate their effects on classification performance and identify the optimal significance level. The results show that Naïve Bayes without variable selection achieved an accuracy of 87.50%, precision of 93.01%, and recall of 86.21%. After applying Chi-Square selection, performance improved across all significance levels. At α = 0.05, the accuracy reached 87.88%, with precision of 93.06% and recall of 86.85%. At α = 0.01, accuracy increased to 88.46%, precision to 94.25%, and recall to 86.53%. The best performance was obtained at α = 0.001, achieving an accuracy of 88.65%, precision of 94.19%, and recall of 86.86%. These findings indicate that Chi-Square variable selection effectively enhances the performance of the Naïve Bayes algorithm for diabetes classification  
Co-Authors Afifah Hayati Ambar Winarni Farhan Arizal Ginanjar Ginanjar