Implementation of the Naive Bayes Method in Looker Studio for data on the achievement of Great IDN in IDN Akhwat School

Authors

  • Yuma Akbar Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Haura Salsabila Az-Zahra Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Kiki Setiawan Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika
  • Raisah Fajri Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI:

https://doi.org/10.55324/ijoms.v3i11.981

Keywords:

Data Analytics, Google Looker Data Studio, Cloud Systems, Naive Bayes, Great Student

Abstract

The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results.

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Published

2024-08-25

Issue

Vol. 3 No. 11 (2024): Indonesian Journal of Multidisciplinary Science

Section

Articles

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Copyright (c) 2024 Yuma Akbar, Haura Salsabila Az-Zahra, Kiki Setiawan, Raisah Fajri

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