IMPLEMENTATION OF DATA MINING ALGORITHM FOR PREDICTING POPULARITY OF PLAYSTORE GAMES IN THE PANDEMIC PERIOD OF COVID-19

  • Daning Nur Sulistyowati (1*) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Norma Yunita (2) Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri
  • Siti Fauziah (3)
  • Risca Lusiana Pratiwi (4)

  • (*) Corresponding Author
Keywords: C4.5 Algorithm, Naive Bayes Algorithm, Game popularity, Prediction, Playstore Games

Abstract

The existence of the COVID-19 virus makes everyone fill their time at home by doing various activities, one of them playing games on the phone. For the game to develop continuously, it needs an assessment that comes from the community and especially the game lovers themselves. This assessment is used to find out what category of game you want. Therefore the analysis is needed to determine the interests of game lovers by analyzing the popularity of a game. This research was conducted to predict the level of popularity of games in PlayStore applications to find out how many popular and unpopular games and the accuracy obtained with the C4.5 algorithm and Naive Bayes algorithm. The results obtained using the C4.5 algorithm showed 73 popular games and 12 unpopular games with an accuracy value of 85.83% with a precision of 85.83% and a recall of 100% and Naive Bayes showed 23 popular games and 62 unpopular games with an accuracy value of 80% with a precision of 96.11% and a recall of 81.01%. The evaluation results with the ROC curve show the AUC value using the Naive Bayes model of 0.776 and the C4.5 model of 0.500. Of the two models used, one of them is included in the classification of Good classification, namely the Naive Bayes algorithm model, because it has an AUC value between 0.80-0.90. While the C4.5 algorithm model is included in the Fair classification, has an AUC value between 0.70 - 0.80.

References

M. Mustofa, “Penerapan Algoritma K-Means Clustering pada Karakter Permainan Multiplayer Online Battle Arena,” J. Inform., vol. 6, no. 2, pp. 246–254, 2019.

I. D. Wijaya, R. A. Asmara, and M. Mentari, “Penerapan Algoritma A* Untuk Penentuan Jalur Pendakian Terbaik Pada Game Petualangan 3D,” JOINTECS (Journal Inf. Technol. Comput. Sci., vol. 3, no. 3, pp. 135–142, 2018.

M. S. R. Mustofa, Arina Selawati, Kurani Mega Asteroid, “Implementasi Algoritma Apriori untuk Analisa Pemilihan Tipe Karakter pada Permainan Mobile Legend,” J. AKRAB JUARA, vol. 3, no. 1, pp. 130–141, 2017.

F. Fujiati and S. L. Rahayu, “Implementasi Algoritma Fisher Yate Shuffle Pada Game Edukasi Sebagai Media Pembelajaran.,” CogITo Smart J., vol. 6, no. 1, p. 1, 2020.

R. A. Krisdiawan, “Implementasi Model Pengembangan Sistem Gdlc Dan Algoritma Linear Congruential Generator Pada Game Puzzle,” Nuansa Inform., vol. 12, no. 2, pp. 1–9, 2018.

R. Setiyawan, “Penerapan Algoritma Decision Tree C4 . 5 dalam Pemilihan Leader Warrior pada Game Android Power Ranger Legacy Wars,” vol. 6, no. 1, pp. 7–10, 2019.

A. H. Annazili and A. Qoiriah, “Implementasi Algoritma Fisher-Yates Shuffle Dan Fuzzy Tsukamoto Pada Game Petualangan Si Thole Berbasis Android Menggunakan Game Engine Unity,” vol. 01, pp. 188–199, 2020.

William, R. Giovanno, and D. Udjulawa, “Penerapan Algoritma Negamax dan Alpha Beta Pruning pada Permainan Othello,” Jatisi, vol. 2, no. 2, pp. 181–190, 2016.

M. R. Alimansyah, E. C. Djamal, R. Yuniarti, and A. Arif, “Game Evaluasi Gerakan Pasien Rehabilitasi Cedera Bahu Berbasis Kinect menggunakan Kalman Filter,” Semin. Nas. Apl. Teknol. Inf., pp. 32–36, 2018.

W. Muhammad, C. Perdana, and A. Qoiriah, “Game Edukatif Simulasi Pembuatan SIM Menggunakan Neural Network Backpropagation Sebagai Rekomendasi Penentu Kelulusan,” vol. 01, pp. 217–227, 2020.

J. T. Informasi, I. Sujai, P. Sarjana, T. Informatika, and U. Dian, “SISWA SEKOLAH MENENGAH ATAS DENGAN,” vol. 12, no. April, pp. 42–53, 2016.

L. Ariyani, “KAJIAN PENERAPAN MODEL C45 , SUPPORT VECTOR MACHINE ( SVM ), DAN NEURAL NETWORK DALAM PREDIKSI,” vol. 9, no. 1, pp. 72–86, 2016.

M. Siddik and Y. Desnelita, “Penerapan Naïve Bayes untuk Memprediksi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademis,” vol. 2, no. 4, pp. 2–6, 2019.

Y. H. Hui et al., “PENERAPAN ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI JUMLAH PRODUKSI BARANG BERDASARKAN DATA PERSEDIAAN DAN JUMLAH PEMESANAN PADA CV. PAPADAN MAMA PASTRIES. Volume 1.,” J. Mantik Penusa, vol. 1, no. 2, pp. 16–21, 2017.

Published
2020-08-01
How to Cite
Sulistyowati, D., Yunita, N., Fauziah, S., & Pratiwi, R. (2020). IMPLEMENTATION OF DATA MINING ALGORITHM FOR PREDICTING POPULARITY OF PLAYSTORE GAMES IN THE PANDEMIC PERIOD OF COVID-19. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 6(1), 95-100. https://doi.org/10.33480/jitk.v6i1.1425
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