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Data Mining Untuk Klasifikasi Tingkat Prokrastinasi Akademik Bagi Mahasiswa Di Kota Pekanbaru Menggunakan Metode NaÏve Bayes

Julius, Zuandrew Richi (2023) Data Mining Untuk Klasifikasi Tingkat Prokrastinasi Akademik Bagi Mahasiswa Di Kota Pekanbaru Menggunakan Metode NaÏve Bayes. Other thesis, Universitas Islam Riau.

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Abstract

Students as part of educational institutions are required to be able to develop their various potentials optimally, they are always faced with tasks, both academic and non-academic. Students are required to be able to fulfill their assignments, but in reality, when facing these assignments, students often feel reluctant or lazy to do them. This feeling of reluctance comes from the psychological condition he experiences and encourages him to avoid and postpone doing tasks that should be done. Symptoms of this behavior can be called academic procrastination. Procrastination has made students addicted, because students think that by doing procrastination, their coursework will still be completed. Therefore, it is necessary to build an application to determine the level of academic procrastination of students in the city of Pekanbaru. This application was built using the Naïve Bayes method to classify the level of academic procrastination of students in the city of Pekanbaru. Testing of the applications that were built went as expected with high levels of precision, recall and accuracy, namely with values of 95.75% for precision, 96.64% for recall and 95.52% for accuracy with 266 training data and 67 testing data, so that classification of the level of academic procrastination for students in the city of Pekanbaru is feasible to implement

Item Type: Thesis (Other)
Contributors:
Contribution
Contributors
NIDN/NIDK
Sponsor
Syafitri, Nesi
9088102
Uncontrolled Keywords: Data mining, academic procrastination, naïve Bayes method
Subjects: L Education > L Education (General)
T Technology > TS Manufactures
Divisions: > Teknik Informatika
Depositing User: Yolla Afrina Afrina
Date Deposited: 09 Sep 2025 09:19
Last Modified: 09 Sep 2025 09:19
URI: https://repository.uir.ac.id/id/eprint/28456

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