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All Journal JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Cendekia : Jurnal Pendidikan Matematika Abdi Laksana : Jurnal Pengabdian Kepada Masyarakat Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal of Business, Law, and Education International Journal of Business, Economics, and Social Development Malcom: Indonesian Journal of Machine Learning and Computer Science International Education Trend Issues Tesseract: International Journal of Geometry and Applied Mathematics
Ningsi, Besse Arnawisuda
Program Studi Matematika FMIPA Universitas Pamulang
Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Neuroscience Social Sciences Other

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The Influence of Reading Interest and Study Habits against Mathematics Learning Outcomes Arofah, Irvana; Ningsi, Besse Arnawisuda
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 4, No 4 (2021): Budapest International Research and Critics Institute November
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i4.3179

Abstract

This study aims to determine the effect of reading interest and study habits on mathematics learning outcomes. The results showed that the variables of reading interest and study habits simultaneously (simultaneously) had an influence on mathematics learning outcomes which were expressed by the following regression equation model: Y = 49,014 + 0,479X1 – 0,212X2. From the results of the analysis, it can be concluded that the variables of reading interest and study habits together have a significant influence on mathematics learning outcomes. The two independent variables contributed to the learning outcomes of mathematics by 17%. While partially, reading interest and learning habits each have a significant influence on mathematics learning outcomes.
Classification Analysis of Student Graduation Timeliness Using Decision Tree and Naïve Bayes Methods Gantini, Sri Nevi; Ningsi, Besse Arnawisuda; Arofah, Irvana
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 4, No 4 (2021): Budapest International Research and Critics Institute November
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i4.3182

Abstract

This study aims to determine the classification of student graduation timeliness by using the Decision Tree and Naïve Bayes methods. This study uses a quantitative method, where the approach used is the classification of various attributes that affect the timeliness of student graduation. The independent variables in the classification are mostly called attributes; In this study, the attributes of school of origin, gender, area of origin, profession of parents, study program and Grade Point Average (GPA) were used. While the dependent variable or in the classification is usually called a label, in this study the label used as a decision attribute is the timeliness of student graduation. In this study, two methods were used, namely using the nave Bayes method and a decision tree (decision tree) to determine the classification of the timeliness of student graduation and to determine the level of classification accuracy. Based on the results of the analysis, it can be concluded that the classification using the nave Bayes method obtained 36 predicted data according to the actual data and 7 different predicted data from the actual data. Meanwhile, in the 42 decision tree method, the predicted data is in accordance with the actual data and there is only 1 predicted data that is different from the actual data. Decision Tree method has a lower classification error rate than the Naïve Bayes method. The level of accuracy of prediction results using the Decision Tree method is higher than the Naïve Bayes method.
Co-Authors Arofah, Irvana Baniyah Baniyah, Baniyah Dakhi, Yustina Lentiana Eni Suryani Gantini, Sri Nevi Ilmadi, Ilmadi Isnurani, Isnurani Permatasari, Yuni Putri, Dinda Novianda Putri, Reisya Junita Rahayu, Tias Mugi Rahman, Andi Nur Saefullah, Rifki Safitri, Adinda Sari, Deka Nirmala Sessu, Andi Waryanto, Hendro