Perbandingan Klasifikasi Tipe Kesuksesan Generasi Z Menggunakan Algoritma Naïve Bayes dan Decision Tree
DOI:
https://doi.org/10.38204/tematik.v12i1.2334Keywords:
Generation Z, classification of success, CRISP-DM, Naive Bayes, Decision TreeAbstract
This study aims to classify the types of success of Generation Z using the CRISP-DM method approach and using the Naïve Bayes and Decision Tree algorithms. Generation Z who grew up in a digital environment has a unique view of the meaning of success, which is no longer limited to income or position, but also includes life balance and self-development. This study identifies several important factors such as educational background, technological skills, work experience, personal branding, and use of social media as determining variables in the classification of types of success. The classification model produces four main categories of success, namely financial, career, self-development, and life balance. The results showed that life balance was the most dominant category of success among respondents. The use of the Naïve Bayes and Decision Tree algorithms showed that Decision Tree with balancing techniques (random oversampling) provided the highest classification accuracy, which was 94%, compared to Naïve Bayes which only reached 37%. This study makes an important contribution to the development of human resource strategies, education, and policies that are relevant to the characteristics and aspirations of Generation Z in the digital era.
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References
C. T. Widiastuti, “Analisis Gaya Hidup Sebagai Variabel Mediasi Pada Perilaku Konsumtif Belanja Online,” vol. 21, no. 4, 2023.
M. I. Revilliano Trisandri, B. Aji Putra, and A. Aldo Nugroho, “PENGARUH KOMPENSASI FINANSIAL TERHADAP NIAT UNTUK KELUAR MELALUI KEPUASAN KERJA GENERASI Z SEKTOR JASA DI JAKARTA,” Jurnal Review Pendidikan dan Pengajara, vol. 7 No 4, 2024, Dec. 2024, Accessed: Mar. 20, 2025. [Online]. Available: http://journal.universitaspahlawan.ac.id/index.php/jrpp
M.-A. Lazar, A. Zbuchea, and F. Pînzaru, “The Emerging Generation Z Workforce in the Digital World: A Literature Review on Cooperation and Transformation,” Proceedings of the International Conference on Business Excellence, vol. 17, no. 1, pp. 1991–2001, Jul. 2023, doi: 10.2478/picbe-2023-0175.
D. Mavhungu, E. N. Barkhuizen, and M. Magau, “Predicting the career success of Generation Z employees: A socioeconomic perspective,” South African Journal of Economic and Management Sciences, vol. 27, no. 1, Nov. 2024, doi: 10.4102/sajems.v27i1.5788.
M. G. Herlina and D. H. Syahchari, “Soft Skill Analysis: Rasch Model on Generation Z in Greater Jakarta,” Jurnal Pendidikan Ekonomi Dan Bisnis (JPEB), vol. 10, no. 2, pp. 100–110, May 2022, doi: 10.21009/JPEB.010.2.1.
T. A. Suciati and D. Deswarta, “Pengaruh Pelatihan Kerja, Tingkat Pendidikan, dan Pengalaman Kerja terhadap Kinerja Karyawan Generasi Z di Selat Panjang,” Al Qalam: Jurnal Ilmiah Keagamaan dan Kemasyarakatan, vol. 18, no. 1, p. 58, Jan. 2024, doi: 10.35931/aq.v18i1.3008.
M. Zaid and RajendranT, “Higher Classification Accuracy of Income Class Using Decision Tree Algorithm over Naive Bayes Algorithm,” 2022. doi: 10.3233/APC220079.
S. Sang-Cheol Moon, “Syncretistic Scientism and Dissonant Plausibility Structure: An Analysis of Worldview Orientation Among Generation Zers in South Korea,” Humanities and Social Sciences, vol. 9, no. 1, p. 14, 2021, doi: 10.11648/j.hss.20210901.13.
Arya Satya Pratama, Suci Maela Sari, Maila Faiza Hj, Moh Badwi, and Mochammad Isa Anshori, “Pengaruh Artificial Intelligence, Big Data Dan Otomatisasi Terhadap Kinerja SDM Di Era Digital,” Jurnal Publikasi Ilmu Manajemen, vol. 2, no. 4, pp. 108–123, Oct. 2023, doi: 10.55606/jupiman.v2i4.2739.
D. Kurniawan and D. M. Yasir, “Optimization Sentiment Analysis Using CRISP-DM And Naïve Bayes Methods Implemented On Social Media,” Oct. 2022.
Salma Navisa, Luqman Hakim, and Aulia Nabilah, “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify Menggunakan CRISP-DM,” Jurnal Sistem Cerdas, pp. 114–125, 2021.
M. B. Ryando and S. Sutarman, “Clustering of Eligibility and Characteristics of Smart Indonesia Card Recipients for College using Agglomerative Hierarchical Clustering,” JURNAL SISFOTEK GLOBAL, vol. 14, no. 2, p. 137, Sep. 2024, doi: 10.38101/sisfotek.v14i2.15707.
A. Handijono, “Memanfaatkan Data Mining danKnowledge Management System Untuk Mengoptimalkan Strategi Pemasaran pada Bank,” 2022.
A. Pratiwi, A. T. Sasongko, and D. K. Pramudito, “Analisis Prediksi Gilingan Plastik Terlaris Menggunakan Algoritma K-Nearest Neighbor Di CV Menembus Batas,” Jurnal Informatika Teknologi dan Sains (Jinteks), vol. 5, no. 3, pp. 437–445, 2023.
R. R. Adhitya, Wina Witanti, and Rezki Yuniarti, “Perbandingan Metode CART Dan Naïve Bayes Untuk Klasifikasi Customer CHURN,” INFOTECH journal, vol. 9, no. 2, pp. 307–318, Jul. 2023, doi: 10.31949/infotech.v9i2.5641.
R. R. Rahmah, I. R. Setiawan, and F. Frazna Az-Zahra, “Prediksi Keterlambatan Mahasiswa Dalam Membayar Biaya Kuliah Menggunakan Algoritma C4.5 (Studi Kasus : Universitas Muhammadiyah Sukabumi),” 2024.
E. G. G. Sukmo, G. Firmansyah, and B. Tjahjono, “Perbandingan Prediksi Keberlanjutan Polis Asuransi Dengan Menggunakan Algoritma Naive Bayes, KNN, dan SVM,” Jurnal Bisnis dan Manajemen, vol. 4, no. 2, pp. 24–43, 2024.
A.- Husaini, I. Hariyanti, and A. R. Raharja, “Perbandingan Algoritma Decision Tree dan Naive Bayes dalam Klasifikasi Data Pengaruh Media Sosial dan Jam Tidur Terhadap Prestasi Akademik Siswa,” Technologia : Jurnal Ilmiah, vol. 15, no. 2, p. 332, Apr. 2024, doi: 10.31602/tji.v15i2.14381.
