Implementation of the Naïve Bayes Method to Determine Student Interest in Gaming Laptops

Authors

  • Rico Fadly Nasution Universitas Labuhanbatu
  • Muhammad Halmi Dar Universitas Labuhanbatu
  • Fitri Aini Nasution Universitas Labuhanbatu

DOI:

10.33395/sinkron.v8i3.12562

Keywords:

Classification, Data Mining, Gaming Laptops, Naïve Bayes, Students

Abstract

The development of the times resulted in the development of technology to date. With the existence of technology, many people have used technology to help their daily activities. In this study, the author will discuss the technology that is often used by students to help them with their assignments, namely laptops. Laptop is a technology that has been widely used by students, teachers and the public. Having a laptop can make things easier. Until now, each laptop brand continues to develop their laptop production laptops with good specifications. Until now, almost all laptop brands have made gaming laptops that are actually intended for people-people who play games. But with good specifications, gaming laptops can also be used for daily activities. With an attractive design and good specifications, of course you can attract student and public interest in gaming laptops. Therefore the authors made a study of student interest in gaming laptops. With good design and specifications on gaming laptops, the author aims to classify the number of students who are interested and not interested in gaming laptops. The classification will be carried out using the Naïve Bayes method with the number of sample data used as many as 100 student data in data mining. The classification results obtained were 55 students (55% representation) interested in gaming laptops and 45 students (45% representation) had no interest in gaming laptops. The results show that not all students are interested in gaming laptops, even though they have laptops design and great specs.

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Author Biographies

Muhammad Halmi Dar, Universitas Labuhanbatu

 

 

Fitri Aini Nasution, Universitas Labuhanbatu

 

 

References

Agustina, N., Adrian, A., & Hermawati, M. (2021). Implementasi Algoritma Naïve Bayes Classifier untuk Mendeteksi Berita Palsu pada Sosial Media. Faktor Exacta, 14(4), 1979–276. https://doi.org/10.30998/faktorexacta.v14i4.11259

Al-Rasheed, A. (2021). Identification of important features and data mining classification techniques in predicting employee absenteeism at work. International Journal of Electrical and Computer Engineering, 11(5), 4587–4596. https://doi.org/10.11591/ijece.v11i5.pp4587-4596

Azzahra, S. A., & Wibowo, A. (2020). Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(4), 737. https://doi.org/10.25126/jtiik.2020731907

Damuri, A., Riyanto, U., Rusdianto, H., & Aminudin, M. (2021). Implementasi Data Mining dengan Algoritma Naïve Bayes Untuk Klasifikasi Kelayakan Penerima Bantuan Sembako. JURIKOM (Jurnal Riset Komputer), 8(6), 219. https://doi.org/10.30865/jurikom.v8i6.3655

Di, P., & Duan, L. (2014). New naive Bayes text classification algorithm. Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing, 29(1), 71–75. https://doi.org/10.11591/telkomnika.v12i2.4180

Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students’ academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447–459. https://doi.org/10.11591/ijeecs.v9.i2.pp447-459

Indrayuni, E. (2019). Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes. Jurnal Khatulistiwa Informatika, 7(1), 29–36. https://doi.org/10.31294/jki.v7i1.1

Murwantara, I. M., Yugopuspito, P., & Hermawan, R. (2020). Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data. Telkomnika (Telecommunication Computing Electronics and Control), 18(3), 1331–1342. https://doi.org/10.12928/TELKOMNIKA.v18i3.14756

Negara, A. B. P., Muhardi, H., & Putri, I. M. (2020). Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 599. https://doi.org/10.25126/jtiik.2020711947

Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI, 5(2), 697–711. Retrieved from http://ejurnal.tunasbangsa.ac.id/index.php/jsakti/article/view/369

Patil, R., & Tamane, S. (2018). A comparative analysis on the evaluation of classification algorithms in the prediction of diabetes. International Journal of Electrical and Computer Engineering, 8(5), 3966–3975. https://doi.org/10.11591/ijece.v8i5.pp3966-3975

Pour, E. S., Esmaeili, M., & Romoozi, M. (2022). Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification. International Journal of Electrical and Computer Engineering, 12(6), 6397–6409. https://doi.org/10.11591/ijece.v12i6.pp6397-6409

Santoso, H. A., Rachmawanto, E. H., Nugraha, A., Nugroho, A. A., Setiadi, D. R. I. M., & Basuki, R. S. (2020). Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization. Telkomnika (Telecommunication Computing Electronics and Control), 18(2), 799–806. https://doi.org/10.12928/TELKOMNIKA.V18I2.14744

Uçar, T., & Karahoca, A. (2021). Benchmarking data mining approaches for traveler segmentation. International Journal of Electrical and Computer Engineering, 11(1), 409–415. https://doi.org/10.11591/ijece.v11i1.pp409-415

Watratan, A. F., B, A. P., Moeis, D., Informasi, S., & Makassar, S. P. (2020). Implementation of the Naive Bayes Algorithm to Predict the Spread of Covid-19 in Indonesia. Journal of Applied Computer Science and Technology, 1(1), 7–14.

Yun, H. (2021). Prediction model of algal blooms using logistic regression and confusion matrix. International Journal of Electrical and Computer Engineering, 11(3), 2407–2413. https://doi.org/10.11591/ijece.v11i3.pp2407-2413

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How to Cite

Nasution, R. F., Dar, M. H. ., & Nasution, F. A. . (2023). Implementation of the Naïve Bayes Method to Determine Student Interest in Gaming Laptops. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(3), 1709-1723. https://doi.org/10.33395/sinkron.v8i3.12562

Issue

Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023

Section

Articles

License

Copyright (c) 2023 Rico Fadly Nasution, Muhammad Halmi Dar, Fitri Aini Nasution

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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