APPLICATION OF LAPTOP RECOMMENDED DATA MINING FOR STUDENTS USING APRIORI METHOD

Fachira Ayu Rahma; Titin Prihatin

doi:10.52362/jisicom.v7i2.1247

Fachira Ayu Rahma Universitas Nusa Mandiri, Jakarta
Titin Prihatin Universitas Nusa Mandiri, Jakarta

DOI: https://doi.org/10.52362/jisicom.v7i2.1247

Abstract

This study aims to analyze laptop recommendations for students so that students can buy laptops according to affordable prices and desired specifications. The method used is the Apriori Algorithm Method with a minimum support of 15% and a minimum confidence of 50%. This research was conducted to find out laptops with good performance and affordable prices that can be used as recommendations, especially students who work part-time. The data obtained for this study, namely data downloaded from the Kaggle platform for 1 year and looking for supporting references (such as books and journals). The results of this study show that there are laptops that are recommended by laptop sellers so that they are purchased by consumers significantly. Based on a priori analysis, laptops were found that can be used for recommendations using association rules, namely Apple with a support value of 35%, HP with a support value of 26%, Dell and Razer with a support value of 19%. If Apple and HP are purchased simultaneously it has a confidence value of 45%.

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References

[1] R. Saputra and A. J. P. Sibarani, “Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Meningkatkan Pola Penjualan Obat,” 2020. [Online]. Available: http://jurnal.mdp.ac.id
[2] N. Ratna, S. Purba, and F. Riandari, “Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Analisis Keranjang Belanja Transaksi Penjualan Pada PT Madu Kembang Joyo,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 4, no. 1, 2021.
[3] B. Audi Najib, N. Suryani, S. Nusa Mandiri Jakarta Jl Damai No, W. Jati Barat, P. Minggu, and J. Selatan, “Penerapan Data Mining Terhadap Data Penjualan Lapis Bogor Sangkuriang Dengan Metode Algoritma Apriori,” 2019, doi: 10.31294/jtk.v4i2.
[4] N. Ratna, S. Purba, and F. Riandari, “Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Analisis Keranjang Belanja Transaksi Penjualan Pada PT Madu Kembang Joyo,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 4, no. 1, 2021.
[5] C, Dennis Aprillia, Donny Aji Baskoro, dkk. Belajar Data Mining dengan RapidMiner.
[6] Muslim, Much Aziz, Budi Prasetiyo, dkk. DATA MINING ALGORITMA C.45 Disertai Contoh Kasus dan Penerapannya Dengan Program Komputer. 2019.
[7] Wanto, Anjar, M. Noor Hasan Siregar Agus Perdana Windarto, dkk. Data Mining : Algoritma dan Implementasi , vol. 1. Yayasan Kita Menulis, 2020.
[8] Ginantra, Ni Luh Wiwik Sri Rahayu, Fatimah Nur Arifah, dkk. Data Mining dan Penerapan Algoritma , vol. 1. Yayasan Kita Menulis, 2021.
[9] A. Wicak et al., “Bulletin of Information Technology (BIT) Desain User Interface Website Pemetaan Tanaman Obat Dan Langka Di Kabupaten Kediri Dengan Menggunakan Figma,” vol. 3, no. 4, pp. 281–288, 2022, doi: 10.47065/bit.v3i1.
[10] M. Agus Muhyidin, M. A. Sulhan, and A. Sevtiana, “PERANCANGAN UI/UX APLIKASI MY CIC LAYANAN INFORMASI AKADEMIK MAHASISWA MENGGUNAKAN APLIKASI FIGMA,” 2020. [Online]. Available: https://my.cic.ac.id/.