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All Journal MEDIA BINA ILMIAH Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences
Arofah, Irvana
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Religion Arts Humanities Economics, Econometrics & Finance Education Social Sciences

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KLASIFIKASI KETEPATAN WAKTU KELULUSAN MAHASISWA DENGAN METODE NAÏVE BAYES Rahayu, Tias Mugi; Ningsi, Besse Arnawisuda; Isnurani, Isnurani; Arofah, Irvana
MEDIA BINA ILMIAH Vol 15, No 8: Maret 2021
Publisher : BINA PATRIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33758/mbi.v15i8.1062

Abstract

Ketepatan waktu kelulusan mahasiswa merupakan salah satu tujuan untuk menyelesaikan pendidikan mahasiswa di perguruan tinggi dengan status tepat waktu atau tamat. Butuh waktu ≤ 4 tahun untuk mencapai tujuan kelulusan tepat waktu dengan gelar sarjana, namun pada kenyataannya masih terdapat kasus mahasiswa yang tidak lulus tepat waktu. Penelitian ini bertujuan untuk mengetahui klasifikasi ketepatan waktu kelulusan mahasiswa Fakultas Ekonomi Universitas Pamulang tahun ajaran 2018/2019. Algoritma yang digunakan dalam klasifikasi data adalah klasifikasi Naïve Bayes. Klasifikasi Naive Bayesian adalah teknik klasifikasi data yang menggunakan teori dan statistik probabilitas untuk memprediksi probabilitas masa depan berdasarkan data masa lalu. Atribut yang digunakan dalam metode klasifikasi Naïve Bayes adalah ketepatan waktu kelulusan siswa, daerah asal, jenis kelamin, profesi orang tua, jenis sekolah, program studi dan predikat nilai rata-rata kumulatif (IPK). Hasil penelitian menunjukkan bahwa data kelulusan mahasiswa S1 Fakultas Ekonomi Universitas Pamulang tahun ajaran 2018/2019 sebanyak 61,9% yang mampu menyelesaikan pendidikan tepat waktu dengan tingkat akurasi sebesar 69,33%.
Basic Multiplication Knowledge Acquiring Based on Mathematical of Fingering System Junaedi, Junaedi; Wahab, Abdul; Arofah, Irvana; Nugroho, Arya Setya; Permana, Erwin Putera
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences Vol 4, No 3 (2021): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v4i3.2172

Abstract

Acquiring Basic Knowledge Multiplication is one stage of skill trainings. The activities associated with skill development are designed to acclimate students to thinking quickly and precisely about facts, concepts, formulas, and problem-solving techniques. One technique for skill coaching is to employ the Mathematical of Fingering System. These writings are a type of library research in which the author discusses the importance of basic multiplication in the education of Islamic elementary school teachers. Correspondingly, the researchers examine how the concept of the basic multiplication knowledge acquiring based on mathematical of fingering system, and the application of the education of Islamic elementary school teachers are incorporated into the basic multiplication knowledge acquiring based on mathematical of fingering system.
The Influence of Reading Interest and Study Habits against Mathematics Learning Outcomes Arofah, Irvana; Ningsi, Besse Arnawisuda
Budapest International Research and Critics Institute (BIRCI-Journal): Humanities and Social Sciences 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 (BIRCI-Journal): Humanities and Social Sciences 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 Abdul Wahab Arya Setya Nugroho Erwin Putera Permana Gantini, Sri Nevi Isnurani, Isnurani Junaedi Ningsi, Besse Arnawisuda Rahayu, Tias Mugi