Analysis Of Public Interest In Smartfren SIM Cards Using The K-Nearest Neighbors Method

Sri Tawanta Natalia Sembiring; Volvo  Sihombing; Deci  Irmayani

doi:10.46729/ijstm.v5i5.1177

Sri Tawanta Natalia Sembiring Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.
Volvo Sihombing Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.
Deci Irmayani Faculty of Science and Technology, Universitas Labuhanbatu, Sumatera Utara Indonesia.

DOI: https://doi.org/10.46729/ijstm.v5i5.1177

Keywords: Confusion Matrix, K-Nearest Neighbors, Public Interest, SIM Cards, and Smartfren.

Abstract

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Downloads

Download data is not yet available.

References

N. Anwar, I. Riadi, and A. Luthfi, “Analisis SIM Card Cloning Terhadap Algoritma Random Number Generator,” J. Buana Inform., vol. 7, no. 2, pp. 143–150, 2016.

E. M. Sari Rochman, I. Imamah, and A. Rachmad, “Desain Model Peramalan Jumlah Kerusakan SIM CARD,” Multitek Indones., vol. 12, no. 1, p. 20, 2018, doi: 10.24269/mtkind.v12i1.640.

L. N. Rani, “Klasifikasi Nasabah Menggunakan Algoritma C4.5 Sebagai Dasar Pemberian Kredit,” INOVTEK Polbeng - Seri Inform., vol. 1, no. 2, p. 126, 2016, doi: 10.35314/isi.v1i2.131.

Suwati, R. Yesputra, and A. Sapta, “Prediksi Kelancaran Pembayaran Angsuran Pada Koperasi Dengan Metode Naive Bayes Classifier,” Indones. J. Comput. Sci., vol. 11, no. 2, pp. 635–644, 2022, doi: 10.33022/ijcs.v11i2.3080.

Muttaqin et al., Pengenalan Data Mining, 1st ed., no. July. Yayasan Kita Menulis, 2023.

A. Yudhistira, A. A. Aldino, and D. Darwis, “Analisis Klasterisasi Penilaian Kinerja Pegawai Menggunakan Metode Fuzzy C-Means (Studi Kasus : Pengadilan Tinggi Agama bandar lampung),” J. Ilm. Edutic Pendidik. dan Inform., vol. 9, no. 1, pp. 77–82, 2022, doi: 10.21107/edutic.v9i1.17134.

D. Firdaus, “Penggunaan Data Mining dalam Kegiatan Sistem Pembelajaran Berbantuan Komputer,” J. Format, vol. 6, no. 2, pp. 91–97, 2017.

P. Primadona and R. Fauzi, “Penerapan Data Mining Pada Penjualan Produk Elektronik,” Comput. Sci. Ind. Eng., vol. 9, no. 4, 2023, doi: 10.33884/comasiejournal.v9i4.7712.

B. Hssina, A. Grota, and M. Erritali, “Recommendation system using the k-nearest neighbors and singular value decomposition algorithms,” Int. J. Electr. Comput. Eng., vol. 11, no. 6, pp. 5541–5548, 2021, doi: 10.11591/ijece.v11i6.pp5541-5548.

A. S. Abdelmagid and A. I. M. Qahmash, “Utilizing the Educational Data Mining Techniques Orange Technology ‘for Detecting Patterns and Predicting Academic Performance of University Students,’” Inf. Sci. Lett., vol. 12, no. 3, pp. 1415–1431, 2023, doi: 10.18576/isl/120330.

E. Y. Boateng, J. Otoo, and D. A. Abaye, “Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review,” J. Data Anal. Inf. Process., vol. 08, no. 04, pp. 341–357, 2020, doi: 10.4236/jdaip.2020.84020.

R. Puspita and A. Widodo, “Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 646, 2021, doi: 10.32493/informatika.v5i4.7622.

S. Sumarlin, “Implementasi Algoritma K-Nearest Neighbor Sebagai Pendukung Keputusan Klasifikasi Penerima Beasiswa PPA dan BBM,” J. Sist. Inf. Bisnis, vol. 5, no. 1, pp. 52–62, 2015, doi: 10.21456/vol5iss1pp52-62.

S. R. Cholil, T. Handayani, R. Prathivi, and T. Ardianita, “Implementasi Algoritma Klasifikasi K-Nearest Neighbor (KNN) Untuk Klasifikasi Seleksi Penerima Beasiswa,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 6, no. 2, pp. 118–127, 2021.

H. A. Pratama, G. J. Yanris, and M. N. S. Hasibuan, “Implementation of Data Mining for Data Classification of Visitor Satisfaction Levels,” Sink. J. dan Penelit. Tek. Inform., vol. 8, no. 3, pp. 1832–1851, 2023, doi: 10.33395/sinkron.v8i3.12674.