Prediksi Perilaku Konsumtif Remaja Menggunakan Algoritma Catboost Berbasis Machine Learning

Sifa Aurahman; Umi Mahmudah

doi:10.30989/ijds.v3i2.1715

Authors

Sifa Aurahman UIN K.H. Abdurrahman Wahid Pekalongan, Indonesia
Umi Mahmudah UIN K.H. Abdurrahman Wahid Pekalongan, Indonesia

DOI:

https://doi.org/10.30989/ijds.v3i2.1715

Keywords:

perilaku konsumtif, gaya hidup, media social, machine learning, CatBoost

Abstract

Perilaku konsumtif pada remaja menjadi fenomena yang semakin marak dan berpotensi menimbulkan dampak negatif jangka panjang, seperti berkurangnya sikap hemat dan produktif. Ciri perilaku ini antara lain kebiasaan boros, pengeluaran berlebihan untuk memenuhi keinginan, serta mengikuti tren gaya hidup yang sedang berkembang. Penelitian ini bertujuan memprediksi perilaku konsumtif remaja berdasarkan faktor gaya hidup, penggunaan media sosial, dan lingkungan sosial menggunakan pendekatan machine learning. Metode yang digunakan adalah supervised learning dengan algoritma Categorical Boosting (CatBoost), yang mampu mengelola data kategorikal secara efisien dan merupakan pengembangan dari Gradient Boosting Decision Tree (GBDT). Model dibangun dengan mempelajari fitur-fitur yang relevan untuk mengklasifikasikan tingkat perilaku konsumtif. Hasil penelitian menunjukkan bahwa gaya hidup, media sosial, dan lingkungan sosial memiliki pengaruh signifikan terhadap perilaku konsumtif remaja, dengan tingkat akurasi prediksi sebesar 91,8% dan nilai AUC sebesar 0,93. Temuan ini diharapkan dapat menjadi dasar pengembangan strategi pencegahan perilaku konsumtif di kalangan remaja.

References

[1] A. N. C. Ramadhany, “Peran media sosial dalam mendorong gaya hidup konsumtif di kalangan remaja komunitas pesisir,” EDUSOS J. Edukasi dan Ilmu Sos., vol. 2, no. 01, pp. 18–25, 2025.

[2] N. L. Deutsch and E. Theodorou, “Aspiring, consuming, becoming: Youth identity in a culture of consumption,” Youth Soc., vol. 42, no. 2, pp. 229–254, 2010.

[3] J. Y. Zhu and I. Szekely, “Buying Happiness: How Influencer Marketing Affects the Identity and Purchasing Patterns of Teens,” J. Student Res., vol. 11, no. 1, Mar. 2023, doi: 10.47611/jsrhs.v11i1.2372.

[4] M. W. Rakhmatullah, “Konstruksi sosial atas konsumsi digital: Studi interpretatif tentang peran influencer dalam gaya hidup belanja online,” RIGGS J. Artif. Intell. Digit. Bus., vol. 4, no. 1, pp. 489–496, 2025.

[5] K. A. Azhar, C. A. C. Wel, and S. N. Ab Hamid, “‘They Post, I Scroll, I Envy, I Buy’—How Social Media Influencers Shape Materialistic Values and Consumer Behavior Among Young Adults in Malaysia,” J. Theor. Appl. Electron. Commer. Res., vol. 20, no. 3, p. 172, 2025.

[6] E. Wulandari, S. H. Utomo, and L. Rokhmani, “Lifestyle Pathways: How Family, Peers, and Social Media Shape Adolescent Consumer Behavior,” Din. Pendidik., vol. 20, no. 1, pp. 119–135, Jun. 2025, doi: 10.15294/dp.v20i1.24666.

[7] G. Gökçay, S. E. Eryilmaz, and F. Küçük, “The impact of social media addiction on healthy lifestyle beliefs in adolescents,” J. Pediatr. Nurs., vol. 76, pp. e85–e92, May 2024, doi: 10.1016/j.pedn.2024.01.023.

[8] J. Tuominen, E. Rantala, H. Reinikainen, and T.-A. Wilska, “Modern-day socialization agents: The connection between social media influencers, materialism, and purchase intentions of Finnish young people,” J. Soc. Media Soc., vol. 12, no. 1, pp. 21–48, 2023.

[9] H. Ho and K. Ito, “Consumption-oriented engagement in social network sites: Undesirable influence on personal well-being,” Eur. J. Mark., vol. 53, no. 7, pp. 1355–1377, 2019.

[10] E. G. Ellis, “The psychological impact of seeing YouTubers spend millions,” Wired. ISSN, pp. 1028–1059, 2019.

[11] L. Soares and S. Moniz, “Overconsumption and the effects on Mental Health and Well-Being: A review,” Curr. Res. Diabetes Obes. J., vol. 17, no. 2, pp. 1–5, 2023.

[12] H. W. Sari, “HUBUNGAN ANTARA KONTROL DIRI DAN PERILAKU KONSUMTIF DENGAN MINAT TERHADAP PINJAMAN ONLINE PADA MAHASISWA DI SEMARANG.” Universitas Islam Sultan Agung Semarang, 2024.

[13] G. Maulani et al., Machine Learning. Mega Press Nusantara, 2025.

[14] A. Wilson and M. R. Anwar, “The future of adaptive machine learning algorithms in high-dimensional data processing,” Int. Trans. Artif. Intell., vol. 3, no. 1, pp. 97–107, 2024.

[15] L. Zhou, S. Pan, J. Wang, and A. V Vasilakos, “Machine learning on big data: Opportunities and challenges,” Neurocomputing, vol. 237, pp. 350–361, 2017.

[16] E. L. Salvador, “Use of Boosting Algorithms in Household-Level Poverty Measurement: A Machine Learning Approach to Predict and Classify Household Wealth Quintiles in the Philippines,” arXiv Prepr. arXiv2407.13061, 2024.

[17] I. D. Mienye, Y. Sun, and Z. Wang, “Prediction performance of improved decision tree-based algorithms: a review,” Procedia Manuf., vol. 35, pp. 698–703, 2019.

[18] V. G. Costa and C. E. Pedreira, “Recent advances in decision trees: an updated survey,” Artif. Intell. Rev., vol. 56, no. 5, pp. 4765–4800, 2023.

[19] A. BANERJEE, R. RAONIAR, and A. K. MAURYA, “Study of Factors Impacting Safety-Security and Mobility Friction on the Choice of Pedestrians in Using Skywalk Facilities through Soft Computing Approaches,” in Proceedings of the Eastern Asia Society for Transportation Studies, 2021, vol. 13.

[20] S. R. Nath, “Leveraging User and Entity Behavioral Analysis and Machine Learning for Log-Based Anomaly Detection,” Digit. Repos. Theses-SSBM Geneva, 2024.

[21] O. Pahlevi, D. A. N. Wulandari, L. K. Rahayu, H. Leidiyana, and Y. Handrianto, “Model Klasifikasi Risiko Stunting Pada Balita Menggunakan Algoritma CatBoost Classifier,” Bull. Comput. Sci. Res., vol. 4, no. 6, pp. 414–421, 2024.

[22] N. K. Dewi, “Deteksi fake follower instagram menggunakan catboost classifer.” Fakultas Sains Dan Teknologi UIN Syarif Hidayatullah Jakarta, 2021.

[23] J. Lin, “Application of machine learning in predicting consumer behavior and precision marketing,” PLoS One, vol. 20, no. 5, p. e0321854, 2025.

[24] A. L. Puspanagara, “Penerapan Explainable AI untuk Prediksi Performa Akademik Mahasiswa Menggunakan Random Forest dan SHAP,” Infoman’s J. Ilmu-ilmu Inform. dan Manaj., vol. 19, no. 1, 2025.

[25] M. T. Syamkalla, S. Khomsah, and Y. S. R. Nur, “Implementasi Algoritma Catboost dan Shapley Additive Explanations (SHAP) Dalam Memprediksi Popularitas Game Indie Pada Platform Steam,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 4, pp. 777–786, 2024.