Apriori Algorithm Analysis of Mattress Material Usage Data for Enhanced Production Optimization
DOI:
https://doi.org/10.35870/ijsecs.v5i2.4362Keywords:
Material, Mattress, Optimization, Data Mining, Apriori AlgorithmAbstract
Production is a value-adding process that transforms raw materials into finished products to meet manufacturing requirements. Association rule analysis serves as a methodological approach to identify relationships between items, particularly in transactional datasets. This analytical method has proven effective in processing exchange data patterns. Analysis of production material usage patterns revealed that when items A and B are utilized, there exists a 50% probability of concurrent item C usage - a significant pattern emerging from transactional data analysis. The study generated association rules for each operational process. Empirical testing through RapidMiner Studio yielded consistent results, demonstrating linear relationships proportional to the modeled scenarios, thereby validating the model's applicability as a decision-making reference. The evaluation of generated association rules through RapidMiner Studio revealed a Lift Ratio value of 1. These results indicate that combinations meeting or exceeding a Lift Ratio threshold of 1 demonstrate statistical validity and practical utility.
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References
Sutrisno, S. (2020). Penerapan Algoritma Apriori Untuk Mencari Pola Penjualan Produk Dana Pada Pt Bank Rakyat Indonesia (Persero) Tbk Kanca Jakarta Pasar Minggu. Jurnal Sistem Informasi Dan Informatika (Simika), 3(1), 12-26. https://doi.org/10.47080/Simika.V3i1.834
Yudha, R. S., Auliasari, K., & Prasetya, R. P. (2020). Penerapan Algoritma Apriori Untuk Menghasilkan Pola Penjualan Produk Bangunan. Jati (Jurnal Mahasiswa Teknik Informatika), 4(1), 154-161.
Tana, M. P., Marisa, F., & Wijaya, I. D. (2018). Penerapan metode data mining market basket analysis terhadap data penjualan produk pada toko oase menggunakan algoritma apriori. JIMP-J. Inform. Merdeka Pasuruan, 3(2), 17-22. https://doi.org/10.37438/Jimp.V3i2.167
Vulandari, R. T. (2017). Data mining. Gava Media.
Suyanto. (2017). Data mining. Informatika.
Nofriansyah, D., & Widi, G. (2015). Algoritma data mining dan pengujian. CV Budi Utama.
Agung, M. T., & Nurhadiyana, B. (2015). Penerapan Data Mining Pada Data Transaksi Penjualan Untuk Mengatur Penempatan Barang Menggunakan Algoritma Apriori. Jurnal (Online) Fakultas Ilmu Komputer.
Haris, A. (2016). Algoritma apriori. Medium.com, 5(1), 42-46.
Najib, B. A., & Suryani, N. (2020). Penerapan data mining terhadap data penjualan Lapis Bogor Sangkuriang dengan metode algoritma apriori. Jurnal Teknik Komputer, 6(1), 61-70. https://doi.org/10.31294/Jtk.V4i2
Silalahi, D. K., Murfi, H., & Satria, Y. (2017). Studi Perbandingan Pemilihan Fitur untuk Support Vector Machine pada Klasifikasi Penilaian Risiko Kredit. EduMatSains: Jurnal Pendidikan, Matematika dan Sains, 1(2), 119-136.
Kurniadi, A., & Novianto, Y. (2020). Penerapan metode regresi linier untuk memprediksi kebiasaan pelanggan studi kasus: PT. Mensa Binasukses. Jurnal Ilmiah Mahasiswa Teknik Informatika, 2(2), 107.
Susatyono, J. D., Febryantahanuji, F., Kusumo, H., & Rakasiwi, S. (2024). Implementasi metode trend moment untuk prediksi penjualan barang. E-Bisnis: Jurnal Ilmiah Ekonomi dan Bisnis, 17(1), 250-264. https://doi.org/10.51903/e-bisnis.v17i1.1912.
Panggabean, D. S. O., Buulolo, E., & Silalahi, N. (2020). Penerapan data mining untuk memprediksi pemesanan bibit pohon dengan regresi linier berganda. JURIKOM (Jurnal Riset Komputer), 7(1), 56. https://doi.org/10.30865/jurikom.v7i1.1947
Rizky, F., Syahra, Y., & Mariami, I. (2019). Implementasi Data Mining Untuk Memprediksi Target Pemakaian Stok Barang Menggunakan Metode Regresi Linier Berganda. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer), 18(2), 167-175. https://doi.org/10.53513/jis.v18i2.156.
Gaol, I. L. L., Sinurat, S., & Siagian, E. R. (2019). Implementasi Data Mining Dengan Metode Regresi Linear Berganda Untuk Memprediksi Data Persediaan Buku Pada Pt. Yudhistira Ghalia Indonesia Area Sumatera Utara. KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), 3(1), 130-133. https://doi.org/10.30865/komik.v3i1.1579
Wibisono, A., Rofik, M., & Purwanto, E. (2019). Penerapan Analisis Regresi Linier Berganda dalam Penyelesaian Skripsi Mahasiswa. Jurnal ABDINUS: Jurnal Pengabdian Nusantara, 3(1), 30-35. https://doi.org/10.29407/ja.v3i1.13512
Purwadi, P., Ramadhan, P. S., & Safitri, N. (2019). Penerapan Data Mining Untuk Mengestimasi Laju Pertumbuhan Penduduk Menggunakan Metode Regresi Linier Berganda Pada BPS Deli Serdang. Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika Dan Komputer), 18(1), 55-61. https://doi.org/10.53513/jis.v18i1.104.
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Copyright (c) 2025 Niko Suwaryo, Santoso Santoso, Masgo Masgo, Tugiman Tugiman, Sandy Gunarso Wijoyo, Nugraha Nugraha
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