Implementasi Algoritma Apriori Dalam Analisa Penjualan Sparepart Motor
Abstract
Abstract— In 2020, Indonesia's population is dominated by Gen Z (born 1997-2012) and Millennials (born 1981-1996). ). Gen Z accounts for 27.94% of the total population and millennials reach 25.87%. Most of these two generations are in the working age group, which could be an opportunity to accelerate economic growth. The government needs to encourage generations z and millennials to become entrepreneurs. The government calls on millennials to start a business. Technology development allows people to become entrepreneurs and self-employed. Changes in the usage of this technology will affect the human resource management of the. The gig economy enhances the flexibility and ability to hire skilled professionals from different parts of the world across time zones and geographic boundaries. This dynamics presents us with new challenges. Gig worker represents the balance between immediate working life and the need to increase income. Today's workers want flexibility in their work. The research method used is qualitative to primary data and secondary data. In addition, the data source was explained qualitatively. The survey results were performed by UBSI Jakarta Z- and 130 student repondents. The respondent has knowledge of entrepreneurship 60.8% and 39.2% of respondents can not understand. The type of work of GIG economy and generation z Gig era was awarded 47.7% itself, and then the work in society was determined by 28.4% free lancer. Reasons for choosing a job type show that 40% receive unlimited income, 25.4% are flexible, 26.2% of respondents are rewarded according to their job, and 8.5% choose others a job.
Keywords: Entrepreneur, Gen Z, Millennials, GIG economy
References
Aritonang, J., & Saragih, S. P. (2017). Implementasi Data Mining pada Penjualan Menggunakan Algoritma Apriori di PT. Selatan Indobatam Mandiri. Comasie, 2(2), 31–39.
Kristania, Y. M., & Listanto, S. (2022). Implementasi Data Mining Terhadap Data Penjualan Dengan Algoritma Apriori Pada Pt. Duta Kencana Swaguna. Jurnal Teknoinfo, 16(2), 364. https://doi.org/10.33365/jti.v16i2.1973
Nizaela F, A. F., Susyanto, T., & Vulandari, R. T. (2022). Implementasi Algoritma Apriori pada Tata Letak Kategori Buku di Perpustakaan. Jurnal Ilmiah SINUS, 20(1), 23. https://doi.org/10.30646/sinus.v20i1.566
Purwaningtias, F., Defiyanti, N. E., & Apriori, A. (2022). Penggunaan algoritma apriori untuk penjualan sparepart. 08(02), 11–18.
Ramadhan, M., Hutagalung, J., Dahria, M., Zulkarnain, I., & Jaya, H. (2023). Prediksi Penjualan Spare Part Mobil Daihatsu Menggunakan Algoritma Apriori. Techno.Com, 22(1), 156–166. https://doi.org/10.33633/tc.v22i1.7192
Subakti, G. P., & Nataliani, Y. (2022). Analisis Data Transaksi untuk Penempatan Produk Prioritas Oli Motor Menggunakan Algoritma Apriori. INOVTEK Polbeng - Seri Informatika, 7(2), 243. https://doi.org/10.35314/isi.v7i2.2684
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