Klasifikasi Produk Iphone dengan Menggunakan Algoritma XGBoost
DOI:
https://doi.org/10.47065/jimat.v5i3.649Keywords:
Iphone; Consumer Preferences; Product Classification; Transaction Data; XGBoostAbstract
The iPhone has become a symbol of advanced technology and a modern lifestyle that is highly sought after by the global community, including in Indonesia. Known for its stable and exclusive iOS operating system, this product offers seamless cross-device integration, consistent system updates, and high performance through the support of the latest generation of processors. The iPhone also has a visual appeal through a minimalist and elegant design, as well as superior features such as professional camera quality, high-level data security, and power efficiency. The high popularity of the iPhone makes it one of the most competitive products in the smartphone market. However, the diversity of models, features, and prices of each iPhone series causes user preferences to be diverse and complex. In an effort to understand these preferences, an accurate classification method is needed to group products according to consumer appeal. This study adopts the XGBoost algorithm which is known to be effective in handling complex and large data. By utilizing iPhone product sales transaction data in the Indonesian market, this model is designed to identify purchasing patterns and user segmentation. The classification results are expected to provide deeper insights for manufacturers and marketers in formulating more targeted data-based marketing strategies.
Downloads
References
M. Wolff, W. J. F. A. Tumbuan, and D. C. A. Lintong, “Pengaruh Gaya Hidup, Harga, Dan Citra Merek Terhadap Keputusan Pembelian Produk Smartphone Merek Iphone Pada Kaum Perempuan Milenial Di Kecamatan Tahuna,” Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi, vol. 10, no. 1, pp. 1671–1681, 2022.
A. F. Abdul Kadir, A. Habibi Lashkari, and M. Daghmehchi Firoozjaei, “iPhone Operating System (iOS),” in Understanding Cybersecurity on Smartphones: Challenges, Strategies, and Trends, Springer, 2024, pp. 43–55.
S. Biswas, I. Ali, R. K. Chakrabortty, H. H. Turan, S. Elsawah, and M. J. Ryan, “Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: a study of iPhone evolution,” Technological Forecasting and Social Change, vol. 178, p. 121549, 2022.
M. A. B. N. Hidayah, U. Soebiantoro, and Z. Zawawi, “Pengaruh Gaya Hidup, Citra Merek, dan Kualitas Produk terhadap Minat Beli Smartphone Iphone,” Al-Kharaj: Jurnal Ekonomi, Keuangan & Bisnis Syariah, vol. 6, no. 2, pp. 1993–2003, 2024.
M. Wiens, A. Verone-Boyle, N. Henscheid, J. T. Podichetty, and J. Burton, “A tutorial and use case example of the eXtreme gradient boosting (XGBoost) artificial intelligence algorithm for drug development applications,” Clinical and Translational Science, vol. 18, no. 3, p. e70172, 2025.
F. M. Hidayat, H. Sanjaya, and others, “ANALISIS SENTIMEN PUBLIK TERHADAP PENJUALAN IPHONE 16 DAN KEBIJAKAN TKDN DI INDONESIA,” INFOTECH journal, vol. 11, no. 1, pp. 74–80, 2025.
A. Sayuti and others, “Perbandingan Evaluasi Kernel Support Vector Machine Untuk Klasifikasi Sentimen Apple Vision Pro Pada Sosial Media X,” Universitas Muhammadiyah Malang, 2025.
D. Pradana, M. L. Alghifari, M. F. Juna, and D. Palaguna, “Klasifikasi Penyakit Jantung Menggunakan Metode Artificial Neural Network,” Indonesian Journal of Data and Science, vol. 3, no. 2, pp. 55–60, 2022.
P. Putra, A. M. H. Pardede, and S. Syahputra, “Analisis Metode K-Nearest Neighbour (Knn) Dalam Klasifikasi Data Iris Bunga,” JTIK (Jurnal Teknik Informatika Kaputama), vol. 6, no. 1, pp. 297–305, 2022.
N. N. Sari, T. T. Anisah, and R. Fitriani, “Implementasi Machine Learning Untuk Prediksi Harga Laptop Menggunakan Algoritma Regresi Linear Berganda,” Jurnal Manajemen Informatika (JAMIKA), vol. 14, no. 2, pp. 162–177, 2024.
X. Zhang, F. Guo, T. Chen, L. Pan, G. Beliakov, and J. Wu, “A brief survey of machine learning and deep learning techniques for e-commerce research,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 18, no. 4, pp. 2188–2216, 2023.
B. Hamdikatama, “BEYOND ALGORITHMS: AN INTEGRATED APPROACH TO FAKE NEWS DETECTION USING MACHINE LEARNING TECHNIQUES”.
R. K. Pratama and F. Piliang, “Rancang Bangun Aplikasi Penyewaan Lapangan Futsal Berbasis Web,” Jurnal Sistem Informasi dan Sains Teknologi, vol. 1, no. 2, pp. 144–157, 2019, doi: 10.31326/sistek.v1i2.676.
T. P. P. Sinawang, “Klasifikasi Sentimen Mengenai Rekomendasi HP terbaik Awal Tahun 2024 menggunakan Ekstraksi Fitur TF-IDF dan Algoritma Na{"i}ve Bayes,” 2024.
M. Al Faroby and H. Zajuli, “EXTREME GRADIENT BOOSTING UNTUK PENCARIAN PROTEIN YANG BERPENGARUH TERHADAP PRODUKSI INSULIN BERDASARKAN INTERAKSI PROTEIN-PROTEIN,” Institut Teknologi Sepuluh Nopember, 2020.
T. N. Muthmainnah and A. Voutama, “Pendekatan Data Science Untuk Menemukan Customer Churn Pada Perusahaan Fashion Dengan Metode Machine Learning,” Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD, vol. 6, no. 2, pp. 463–471, 2023.
G. A. Prabowo, B. Rahmat, and H. E. Wahanani, “Aspect-Based Sentiment Analysis Iphone 14 Pro Menggunakan Algoritma XGBoost,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, pp. 3947–3952, 2023.
R. G. Gunawan, E. S. Handika, and E. Ismanto, “Pendekatan Machine Learning Dengan Menggunakan Algoritma Xgboost (Extreme Gradient Boosting) Untuk Peningkatan Kinerja Klasifikasi Serangan Syn,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 3, pp. 453–463, 2022.
C. Hafidz Ardana, A. A. A. A. A. Khoyum, and M. Faisal, “Segmentasi pelanggan penjualan online menggunakan Metode K-Means Clustering,” JISKA (Jurnal Informatika Sunan Kalijaga), vol. 9, no. 1, pp. 1–9, 2024.
A. M. K. Putri and A. F. Rozi, “IMPLEMENTASI CONVUTIONAL NEURAL NETWORK DALAM MENENTUKAN TINGKAT KEMATANGAN MENTIMUN DAN TOMAT BERDASARKAN WARNA KULIT,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 5, pp. 10388–10394, 2024.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Klasifikasi Produk Iphone dengan Menggunakan Algoritma XGBoost
ARTICLE HISTORY
Issue
Section
Copyright (c) 2025 Stevi Freshia Sihombing, Josua Prayuda Pakpahan, Andre Hasudungan Lubis
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
