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All Journal Jurnal Teknik Jurnal Inspiration JMM (Jurnal Masyarakat Mandiri) JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah Jambura Journal of Informatics Jambura Journal of Educational Management Al-Majaalis : Jurnal Dirasat Islamiyah Journal Informatic, Education and Management (JIEM) CENDEKIA : Jurnal Ilmu Pengetahuan Jurnal Al-Fawa'id : Jurnal Agama dan Bahasa Internet of Things and Artificial Intelligence Journal Jurnal Fokus Elektroda (Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Al-Usariyah: Jurnal Hukum Keluarga Islam Biosainsdik: Biologi Sains dan Kependidikan Jurnal Ilmiah Mahasiswa & Penelitian Keperawatan
Muhammad Yassir
Institut Teknologi Dan Bisnis Nobel Indonesia
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Journal : Internet of Things and Artificial Intelligence Journal

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Prediction Analysis of Package C Student Graduation at the Bollo DMansel Community Learning Activity Center (PKBM) with the Naïve Bayes Algorithm Method Yassir, Muhammad; Cahyani, Wanda
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.751

Abstract

Education plays a crucial role in improving the quality of human resources and is the key to a nation's progress. Bollo DMansel Community Learning Activity Center (PKBM) in West Papua provides a Paket C Equivalency Education program to help those who are underserved by formal education. The main challenge in this program is to increase student graduation rates. This research aims to analyze and predict the graduation of Paket C students at PKBM Bollo DMansel using the Naive Bayes algorithm method. The data used includes historical student data from 2021 to 2023, with a total of 128 students. The research steps include data collection, data pre-processing, Naive Bayes algorithm application, and prediction model evaluation. The results show that the Naive Bayes algorithm can provide graduation prediction with fairly high accuracy. The factors that most influence student graduation were identified, including attendance, test scores, and participation in activities. This research makes a real contribution to improving the quality of education at PKBM Bollo DMansel by providing a prediction tool to identify students at risk of not graduating so that timely intervention can be provided.
Co-Authors Abd. Rahman Abdulloh, Muhammad Wahid Adi, Puput Dani Prasetyo Adiprabowo, Tjahjo Agunawan, Agunawan Agusrianto Agusrianto Agustina Rantesalu Ahmad Firman Ajib Hanani Andi Ircham Hidayat Aswarita, Rika Cahaya, Lidiya Cahyani, Wanda Chanif Setiawan, Muchammad Gafur Gafur Gafur Jamada Gafur, Gafur Ganis Chandra Puspitadewi Hanum, Erlia Harry Soepandi Hasanah Nur Husaini, Akhmad Ita Fitriati Jamaluddin, Maryam Johan Ericka Wahyu Prakasa Khoirul Ahsan Kitagawa, Akio Muh. Syilfa Nooviar Muhammad Komeini Muhammad Tahir Musdalifah Muthalib, Abd. Nurdiana, Dian Nursafiah, Nursafiah Nurul Istiqamah Paula Eka Wijaya Purnamawati Saharuna Saharuna Saharuna, Saharuna Sastya Hendri Wibowo Satyawan, Arief Suryadi Sitti Suhada Zubair, Abdurrahman