Perbandingan Algoritma Apriori dan Fp-Growth dalam Pengaplikasian Market Basket Analysis untuk Strategi Bisnis Retail
-
Rizky Amelia
*
Institut Teknologi Kalimantan, Balikpapan,
Indonesia
- Darmansyah Darmansyah Institut Teknologi Kalimantan, Balikpapan, Indonesia
- Ahmad Maulana Rismadin Institut Teknologi Kalimantan, Balikpapan, Indonesia (*) Corresponding Author
Abstract
The advancement of information technology drives businesses to strategize to remain competitive. Retail businesses face challenges from changing consumer behaviors that prioritize convenience and speed in shopping, potentially reducing revenue if businesses fail to adapt quickly. An effective approach involves reanalyzing sales transaction data to identify consumer purchasing patterns, providing guidance for strategic decision-making. One effective technique is data mining using Market Basket Analysis models to analyze shopping baskets and identify correlations between items purchased. This model utilizes algorithms like Apriori and FP-Growth to generate association rules. Preprocessing the dataset to derive frequent itemsets, followed by applying association rules, helps identify significant correlations or patterns within the dataset. The Apriori and FP-Growth algorithms are applied with predefined minimum support and minimum confidence levels. Interpretation involves testing and verifying the discovered patterns against previous facts or hypotheses. The application of these algorithms shows their impact on the dataset size, as the chosen minimum support and confidence levels affect the number of association rules generated. Experiments with FP-Growth and Apriori algorithms indicate that using a minimum support of 0.02 requires longer execution time compared to 0.06. Using a minimum support of 0.02 and minimum confidence of 0.1 yields similar rules across different data divisions (30%, 40%, 50%, and 55% splits) of datasets containing 1200 and 2364 records out of 100% data. Both algorithms achieve 100% accuracy, demonstrating reliability and validity in discovering significant patterns within the same dataset
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References
J. Elektronika and D. Komputer, “INDONESIA Pola Asosiasi Untuk Rekomendasi Penataan Display Barang Menggunakan Algoritma Apriori dan FP-Growth (Study Kasus Gamefantasia Ada Swalayan Pati),” Elkom : Jurnal Elektronika dan Komputer, vol. 16, no. 1, pp. 109–20, Jul. 2023, doi: 10.51903/ELKOM.V16I1.999.
N. Nurlelah and M. Ryansyah, “IMPLEMENTASI DATA MINING ANALISIS TERHADAP DATA PENJUALAN PRODUK HERBAL DENGAN METODE ALGORITMA APRIORI DAN FP-GROWTH,” JIKO (Jurnal Informatika dan Komputer), vol. 5, no. 3, pp. 249–253, Dec. 2022, doi: 10.33387/JIKO.V5I3.5438.
C. R. Artsitella, A. R. Apriliani, and S. Ashari, “Penerapan Association Rules - Market Basket Analysis untuk Mencari Frequent Itemset dengan Algoritma FP-Growth,” JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI, vol. 6, no. 2, pp. 61–69, Sep. 2021, doi: 10.36722/SST.V6I2.661.
A. Untuk et al., “ANALISIS UNTUK MEMPREDIKSI STOK ASET DI PT. XYZ MENGGUNAKAN ALGORITMA APRIORI,” JURNAL ILMIAH INFORMATIKA, vol. 11, no. 02, pp. 106–112, Sep. 2023, doi: 10.33884/JIF.V11I02.7354.
I. Rosmayati, W. Wahyuningsih, E. F. Harahap, and H. S. Hanifah, “Implementasi Data Mining pada Penjualan Kopi Menggunakan Algoritma Apriori,” Jurnal Algoritma, vol. 20, no. 1, pp. 99–107, May 2023, doi: 10.33364/ALGORITMA/V.20-1.1259.
J. Dongga, A. Sarungallo, N. Koru, G. Lante, and R. Artikel, “Implementasi Data Mining Menggunakan Algoritma Apriori Dalam Menentukan Persediaan Barang (Studi Kasus: Toko Swapen Jaya Manokwari),” G-Tech: Jurnal Teknologi Terapan, vol. 7, no. 1, pp. 119–126, Jan. 2023, doi: 10.33379/GTECH.V7I1.1938.
T. Kurniana, A. Lestari, and E. D. Oktaviyani, “Penerapan Algoritma Apriori untuk Mencari Pola Transaksi Penjualan Berbasis Web pada Cafe Sakuyan Side,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 3, no. 1, pp. 13–23, Jun. 2023, doi: 10.24002/KONSTELASI.V3I1.7005.
R. Kurniawan, R. Yusuf, and D. Wacana, “PENERAPAN METODE ASOSIASI DENGAN ALGORITMA APRIORI UNTUK MENDUKUNG STRATEGI PROMOSI,” EDUSAINTEK: Jurnal Pendidikan, Sains dan Teknologi, vol. 10, no. 2, pp. 514–528, Feb. 2023, doi: 10.47668/EDUSAINTEK.V10I2.786.
A. Penelitian Penerapan Algoritma Apriori Pada Transaksi Penjualan Untuk Rekomendasi Menu Makanan Dan Minuman Nanda Nurisya Merliani, N. Isnaeni Khoerida, N. Tri Widiawati, L. Adi Triana, and P. Subarkah, “Penerapan Algoritma Apriori Pada Transaksi Penjualan Untuk Rekomendasi Menu Makanan Dan Minuman,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 8, no. 1, pp. 9–16, May 2022, doi: 10.25077/TEKNOSI.V8I1.2022.9-16.
G. Soepriyono, “Penerapan Market Basket Analysis Data Mining Pada Penjualan Batik dengan Menerapkan Algoritma Apriori,” Journal of Computer System and Informatics (JoSYC), vol. 5, no. 3, pp. 516–525, May 2024, doi: 10.47065/JOSYC.V5I3.5198.
M. Hossain, A. H. M. S. Sattar, and M. K. Paul, “Market basket analysis using apriori and FP growth algorithm,” 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, Dec. 2019, doi: 10.1109/ICCIT48885.2019.9038197.
E. I. Sihombing, M. Yetri, and R. I. Ginting, “Penerapan Market Basket Analysis Terhadap Data Penjualan Produk Pupuk Cair Dengan Menggunakan Metode Algoritma Apriori Dan Frequent pattern growth (Fp Growth) Di Ud. Anugerah Tani Saribudolok,” Jurnal Cyber Tech, vol. 3, no. 2, Feb. 2020, doi: 10.53513/JCT.V3I2.3130.
Bekri Murdianto and Arief Jananto, “INDONESIA Pola Asosiasi Untuk Rekomendasi Penataan Display Barang Menggunakan Algoritma Apriori dan FP-Growth (Study Kasus Gamefantasia Ada Swalayan Pati),” Elkom : Jurnal Elektronika dan Komputer, vol. 16, no. 1, pp. 109–20, Jul. 2023, doi: 10.51903/ELKOM.V16I1.999.
R. D. Widyarini, A. Suharso, A. Solehudin, J. H. S. Ronggowaluyo, and T. T. Karawang, “ASSOCIATION RULE PENGOLAHAN DATA TRANSAKSI TOKO BUNGA MENGGUNAKAN ALGORITMA APRIORI DAN FP-GROWTH UNTUK MENENTUKAN PROMOSI PAKET BUNGA:,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 3, pp. 1461–1466, Oct. 2023, doi: 10.36040/JATI.V7I3.7037.
N. Ramadhani, A. W. Syahroni, A. Supikar, and W. Zumam, “Penerapan Market Basket Analysis Menggunakan Metode Multilevel Association Rules dan Algoritma ML_T2L1 Pada Data Order PT. Unirama,” InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan, vol. 4, no. 2, pp. 261–274, Mar. 2020, doi: 10.30743/INFOTEKJAR.V4I2.2405.
H. Wu, “Data association rules mining method based on improved apriori algorithm,” ACM International Conference Proceeding Series, pp. 12–17, Nov. 2020, doi: 10.1145/3445945.3445948.
M. Soleh, N. Hidayati, F. Dwi Muji Krisdian, P. Studi Teknik Industri, S. Tinggi Teknik Wiworotomo, and J. Semingkir No, “PENERAPAN METODE ASSOCIATION RULE - MARKET BASKET ANALYSIS UNTUK MENINGKATKAN DAYA SAING TOKO SWALAYAN KECIL,” Jurnal Aplikasi Ilmu Teknik Industri (JAPTI), vol. 2, no. 1, pp. 1–9, Jul. 2021, doi: 10.32585/JAPTI.V2I1.1469.
M. Hossain, A. H. M. S. Sattar, and M. K. Paul, “Market basket analysis using apriori and FP growth algorithm,” 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019, Dec. 2019, doi: 10.1109/ICCIT48885.2019.9038197.
D. Alcan, K. Ozdemir, B. Ozkan, A. Y. Mucan, and T. Ozcan, “A Comparative Analysis of Apriori and FP-Growth Algorithms for Market Basket Analysis Using Multi-level Association Rule Mining,” pp. 128–137, 2023, doi: 10.1007/978-3-031-25847-3_13.
M. N. H. S. Anjar Wanto, N. L. W. S. R. G. Dedy Hartama, E. S. N. Darmawan Napitupulu, and C. P. Sarini Vita Dewi, “Data Mining : Algoritma dan Implementasi,” 2020.
B. Almahenzar and A. W. Wijayanto, “Analisis Intensitas Hujan Provinsi Jawa Barat Tahun 2020 Menggunakan Association Rule Apriori dan FP-Growth,” Journal of System and Computer Engineering (JSCE), vol. 3, no. 2, pp. 258–271, Jul. 2022, doi: 10.47650/JSCE.V3I2.397.
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