Analysis of Public Interest in Telkomsel Cards Using the Decision Tree Method
DOI:
10.33395/sinkron.v8i2.12371Keywords:
Classification, Confusion Matrix, Data Mining, Decision Tree, SIM Card.Abstract
SIM card (Subscriber Identification Module) card is a physical electronic device that is the integrated circuit of the internet. Sim cards are used by the public as a place to store quotas for internet, phone calls and SMS. There are many types of SIM cards that are used by the public, such as Telkomsel cards, XL cards, Exis cards and Smartfren cards. There are some people who are interested and use Telkomsel cards, because the network is good. But there are some people who don't use Telkomsel cards, because the quota price is quite expensive. Therefore, the Penlus will make research about people's interest in Telkomsel cards. This study aims to determine the amount of public interest in the Telkomsel card. To conduct this research, the authors used 42 community data which would be classified using the decision tree method. The data used by the author was obtained by distributing a questionnaire to the public. After classifying using the decision tree method, the result is that the people who are interested in the Telkomsel card are 33 people who are interested in the Telkomsel card (for the representation results it is 78.5%) and the results obtained are that the people who are not interested in the Telkomsel card are 9 people (for its representation results of 21.4%). From the results of the study, many people are interested in Telkomsel cards, even though the internet, call and SMS quota prices are quite expensive.
Downloads
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
Ali, R., Yusro, M. M., Hitam, M. S., & Ikhwanuddin, M. (2021). Machine Learning With Multistage Classifiers For Identification Of Of Ectoparasite Infected Mud Crab Genus Scylla. Telkomnika (Telecommunication Computing Electronics and Control), 19(2), 406–413. https://doi.org/10.12928/TELKOMNIKA.v19i2.16724
Alsaadi, E. M. T. A., Khlebus, S. F., & Alabaichi, A. (2022). Identification of human resource analytics using machine learning algorithms. Telkomnika (Telecommunication Computing Electronics and Control), 20(5), 1004–1015. https://doi.org/10.12928/TELKOMNIKA.v20i5.21818
Dhina Nur Fitriana, & Yuliant Sibaroni. (2020). Sentiment Analysis on KAI Twitter Post Using Multiclass Support Vector Machine (SVM). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(5), 846–853. https://doi.org/10.29207/resti.v4i5.2231
Dirjen, S. K., Riset, P., Pengembangan, D., Dikti, R., Yaumi, A. S., Zulfiqkar, Z., & Nugroho, A. (2018). Terakreditasi SINTA Peringkat 4 Klasterisasi Karakter Konsumen Terhadap Kecenderungan Pemilihan Produk Menggunakan K-Means. 3(1), 195–202.
Elmannai, H., & Al-Garni, A. D. (2021). Classification using semantic feature and machine learning: Land-use case application. Telkomnika (Telecommunication Computing Electronics and Control), 19(4), 1242–1250. https://doi.org/10.12928/TELKOMNIKA.v19i4.18359
Ghaedi, H., Farizani, S. R. K. T., & Ghaemi, R. (2021). Improving power theft detection using efficient clustering and ensemble classification. International Journal of Electrical and Computer Engineering, 11(5), 3704–3717. https://doi.org/10.11591/ijece.v11i5.pp3704-3717
Nasrudin, F. K., & Latumahina, R. E. (2022). Perlindungan Hukum Terhadap Konsumen Kartu Sim Yang Mengalami Kebocoran Data Akibat Peretasan. Bureaucracy Journal : Indonesia Journal of Law and Social-Political Governance, 2(1), 331–343. https://doi.org/10.53363/bureau.v2i1.137
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
Pattnaik, G., & Parvathi, K. (2022). Machine learning-based approaches for tomato pest classification. Telkomnika (Telecommunication Computing Electronics and Control), 20(2), 321–328. https://doi.org/10.12928/TELKOMNIKA.v20i2.19740
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
Waliyansyah, R. R., & Fitriyah, C. (2019). Perbandingan Akurasi Klasifikasi Citra Kayu Jati Menggunakan Metode Naive Bayes dan k-Nearest Neighbor (k-NN). Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 5(2), 157. https://doi.org/10.26418/jp.v5i2.32473
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.
Yassir, A. H., Mohammed, A. A., Alkhazraji, A. A. J., Hameed, M. E., Talib, M. S., & Ali, M. F. (2020). Sentimental classification analysis of polarity multi-view textual data using data mining techniques. International Journal of Electrical and Computer Engineering, 10(5), 5526–5534. https://doi.org/10.11591/IJECE.V10I5.PP5526-5534
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
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2023 Putri Talia Cantika, Gomal Juni Yanris, Mila Nirmala Sari Hasibuan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.