Analisis Data Mining Pola Penggunaan Seluler dan Klasifikasi Perilaku Pengguna di Berbagai Perangkat Menggunakan Metode C4.5
- Andi Ernawati Universitas Pembangunan Pancabudi, Indonesia
-
Sri Wahyuni
*
Universitas Pembangunan Pancabudi,
Indonesia
Abstract
Along with the development of digital technology, the use of mobile devices is increasing rapidly and affects user behaviour in accessing information and interacting with digital applications. This research aims to analyse mobile device usage patterns and classify user behaviour across various devices by utilising the C4.5 data mining method. The data used in this study was obtained from the Kaggle.com platform which provides a dataset of mobile device usage patterns, including variables such as frequency of application use, duration of device use, and type of application accessed.
The research stages include data collection, data pre-processing to ensure quality, and analysis using the C4.5 algorithm. The C4.5 algorithm was chosen due to its ability to build a decision tree model that can classify user behaviour with a good level of accuracy. The results of this study show that there are certain patterns in mobile device usage that can be linked to demographic characteristics and user preferences for device types and applications. The resulting decision tree model is able to classify user behaviour with an accuracy rate of 41.71%%, and shows that social media applications and streaming applications are the most frequently used categories on mobile devices.
This research is expected to provide insights for app developers and digital marketers in understanding user behaviour and optimising mobile-based interaction strategies. In addition, the results of this study also contribute to the application of the C4.5 method for analysing mobile technology usage patterns in the context of big data.
Keywords: Data Mining, C4.5, Mobile Usage Pattern, User Behaviour Classification,Rapidminer Decision Tree...
References
T. S. W. T. Plus, “Indonesia Jumlah Pelanggan Seluler.”
M. Syaharani, “10 Negara Dengan Pengguna Smartphone Terbanyak Di Dunia, Indonesia Masuk Daftar!”
Mohamad Alparizi Sahadan, Prof. Dr. Ing. Parabelem T. D. Rompas, MT, and Cindy P. C. Munaiseche, ST, M. Eng, “Analisis Kepuasan Pelanggan Menggunakan Metode Algoritma C4.5 Berdasarkan E-Survey Kejaksaan Negeri Minahasa,” JOINTER J. Informatics Eng., vol. 4, no. 01, pp. 1–13, 2023, doi: 10.53682/jointer.v4i01.137.
N. Azwanti and E. Elisa, “Analisa Kepuasan Konsumen Menggunakan Algoritma C4.5,” Pros. Semin. Nas. Ilmu Sos. dan Teknol., no. 3, pp. 126–131, 2020.
D. S. P. Eko Setia Budi, Abdul Rahman Kadafi, Yasdi Kharismawan, Randi Fadillah, “Analisa Kepuasan Pelanggan Terhadap Layanan Aplikasi E-Commerce Menggunakan Algoritma C4.5,” vol. 4, no. 6, pp. 530–542, 2024.
R. N. Ramadhon, A. Ogi, A. P. Agung, R. Putra, S. S. Febrihartina, and U. Firdaus, “Implementasi Algoritma Decision Tree untuk Klasifikasi Pelanggan Aktif atau Tidak Aktif pada Data Bank,” Karimah Tauhid, vol. 3, no. 2, pp. 1860–1874, 2024, doi: 10.30997/karimahtauhid.v3i2.11952.
D. Telaumbanua and I. Kurniawati, “Penerapan Algoritma C4.5 Untuk Klasifikasi Kepuasan Pelanggan Pada Jasa Layanan Pengiriman,” JoMMiT J. Multi Media dan IT, vol. 6, no. 1, 2022, doi: 10.46961/jommit.v6i1.524.
J. Endardi, “Upaya Peningkatan Loyalitas Pelanggan Pada PT. Jayamandiri Gemasejati Cabang Bojong Gede Menggunakan Metode Algoritma C4.5 dan MetodeCSI,” vol. 3, no. 2, pp. 47–53, 2022.
A. Karim, S. Esabella, K. Kusmanto, M. Hidayatullah, and S. Suryadi, “Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 1001, 2024, doi: 10.30865/mib.v8i2.7445.
B. Bangun and A. K. Karim, “Pengembalian Data Yang Hilang Pada Dataset Dengan Menggunakan Algoritma K-Nearest Neighbor Imputation Data Mining,” J. Media Inform. Budidarma, vol. 8, no. 3, p. 1706, 2024, doi: 10.30865/mib.v8i3.8014.
I. Nasution, A. P. Windarto, and M. Fauzan, “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi,” Build. Informatics, Technol. Sci., vol. 2, no. 2, pp. 76–83, 2020, doi: 10.47065/bits.v2i2.492.
S. T. Bangsa and S. Utara, “Implementasi Data Mining Dalam Mengelompokkan Jumlah Penduduk Miskin Berdasarkan Provinsi Menggunakan Algoritma,” vol. 2, no. 2, pp. 125–132.
N. T. Luchia, H. Handayani, and F. S. Hamdi, “Comparison of K-Means and K-Medoids on Poor Data Clustering in Indonesia Perbandingan K-Means dan K-Medoids Pada Pengelompokan Data Miskin di Indonesia,” vol. 2, no. October, pp. 35–41, 2022.
A. P. Wibawa, M. Guntur, A. Purnama, M. Fathony Akbar, and F. A. Dwiyanto, “Metode-metode Klasifikasi,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., vol. 3, no. 1, pp. 134–138, 2018.
P. P. Haryoto, H. Okprana, and I. S. Saragih, “Algoritma C4.5 Dalam Data Mining Untuk Menentukan Klasifikasi Penerimaan Calon Mahasiswa Baru,” TIN Terap. Inform. Nusant., vol. 2, no. 5, pp. 358–364, 2021.
Nagesh Singh Chauhan, “Decision Tree Algorithm, Explained.”
Y. Mardi, “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Inform., vol. 2, no. 2, pp. 213–219, 2017, doi: 10.22202/ei.2016.v2i2.1465.
K. Aidi Saputra, J. Tata Hardinata, M. Ridwan Lubis, S. Retno Andani, and I. Syahputra Saragih, “KLIK: Kajian Ilmiah Informatika dan Komputer Klasifikasi Algoritma C4.5 Dalam Penerapan Tingkat Kepuasan Siswa Terhadap Media Pembelajaran Online,” Media Online), vol. 1, no. 3, pp. 113–118, 2020.
S. Wahyuni, “Implementation of Data Mining to Analyze Drug Cases Using C4.5 Decision Tree,” J. Phys. Conf. Ser., vol. 970, no. 1, 2018, doi: 10.1088/1742-6596/970/1/012030.
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