Analisis Keterkaitan Layanan Laundry Menggunakan Market Basket Analysis dan Apriori pada One Laundry Jogja
DOI:
https://doi.org/10.30865/json.v8i1.10073Keywords:
Algoritma Apriori, Aturan Asosiasi, Laundry, Market Basket Analysis, Keterkaitan LayananAbstract
Data transaksi pada usaha jasa laundry dapat digunakan untuk mengidentifikasi keterkaitan antarlayanan, tetapi struktur label yang heterogen dapat menghasilkan interpretasi yang kurang tepat apabila seluruh label diperlakukan sebagai item yang setara. Penelitian ini bertujuan menganalisis keterkaitan layanan pada One Laundry Jogja menggunakan Market Basket Analysis dengan algoritma Apriori terhadap 6.790 transaksi periode 8 Juli–31 Desember 2025. Data dipraproses melalui kanonikalisasi label, klasifikasi semantik item, dan transformasi ke matriks transaksional biner. Kanonikalisasi memetakan 198 label mentah menjadi 138 label kanonik, dengan 114 layanan atomik untuk analisis utama. Pilihan parfum wajib, label operasional, dan konteks pelanggan dipisahkan dari basket layanan agar tidak membentuk asosiasi struktural yang keliru diinterpretasikan sebagai pola bundling. Sebanyak 603 transaksi (8,88%) memuat sedikitnya dua layanan atomik. Analisis sensitivitas menetapkan minimum support 0,005 dan minimum confidence 0,30. Dua aturan asosiasi positif diperoleh, yaitu Seprei (Sedang) → Komplit (2 Hari) dengan support 0,007216, confidence 0,340278, dan lift 1,941585, serta Seprei (Kecil) → Komplit (2 Hari) dengan support 0,005596, confidence 0,351852, dan lift 2,007625. Evaluasi antarbulan menunjukkan lift lebih besar dari 1 masing-masing pada lima dari enam dan enam dari enam bulan pengamatan. Kontribusi penelitian ini terletak pada pemisahan semantik item, analisis sensitivitas parameter, dan evaluasi kestabilan temporal. Temuan dapat digunakan sebagai dasar awal pemilihan kandidat paket layanan atau promosi silang yang selanjutnya perlu diuji secara operasional.
References
[1] S. D. Müller, M. Zaggl, R. Svangaard, and A. M. Jakobsen, Digital Transformation Toward Data-Driven Decision-Making: Theorizing Action Strategies in Response to Transformation Challenges, vol. 56. 2025. doi: 10.17705/1CAIS.05637.
[2] A. Pardeshi et al., “An Approach to Explore Consumer Behavior Patterns in Retail Markets Using Market Basket Analysis,” Appl. Stoch. Models Bus. Ind., vol. 41, no. 6, 2025, doi: 10.1002/asmb.70057.
[3] S. F. Gumelar, E. Adrianto, and A. H. Rahmatika, “Leveraging Market Basket Analysis to Develop Sales Strategies for MSMEs: a Case Study of Los in Between,” Jurnal Aplikasi Bisnis dan Manajemen, vol. 12, no. 2, p. 385, 2026, doi: 10.17358/jabm.12.2.385.
[4] M. M. Alawadh and A. M. Barnawi, “A Survey on Methods and Applications of Intelligent Market Basket Analysis Based on Association Rule,” Journal on Big Data, vol. 4, no. 1, pp. 1–25, 2022, doi: 10.32604/jbd.2022.021744.
[5] T. Stylianou and A. Pantelidou, “A machine learning approach to consumer behavior in supermarket analytics,” Decision Analytics Journal, vol. 16, no. May, p. 100600, 2025, doi: 10.1016/j.dajour.2025.100600.
[6] M. D. S. Nugraha, F. A. Al-Anshari, and F. Surya Nugraha, “Analisis Pola Transaksi di Kartika Laundry Menggunakan Metode Apriori,” Seminar Nasional Amikom Surakarta (SEMNASA), no. November, pp. 115–123, 2023.
[7] S. Hidayatuloh, C. Dwi Novita Sari, and T. Ghofar, “Strategi Penempatan Produk Berdasarkan Analisis Market Basket dengan Algoritma Apriori di Swalayan ABC Purwokerto,” Jurnal TRINISTIK: Jurnal Teknik Industri, Bisnis Digital, dan Teknik Logistik, vol. 4, no. 1, pp. 1–8, 2025, doi: 10.20895/trinistik.v4i1.1822.
[8] M. Trifena, K. Hamidah, Y. Umaidah, and A. Voutama, “Implementasi Algoritma Apriori untuk Menentukan Paket Bundel dalam Penjualan Toko Swalayan XYZ,” Journal Sensi, vol. 9, no. 2, pp. 187–197, 2023, doi: 10.33050/sensi.v9i2.2912.
[9] S. R. Bagaskara and D. H. Bangkalang, “Analisis dan Implementasi Market Basket Analysis (MBA) Menggunakan Algoritma Apriori dengan Dukungan Visualisasi Data,” Jurnal Sistem Komputer dan Informatika (JSON), vol. 4, no. 4, p. 612, 2023, doi: 10.30865/json.v4i4.6351.
[10] R. Bintang Samasto and E. Mailoa, “Market Basket Analysis Menggunakan Algoritma Apriori Untuk Mendukung Strategi Promosi Produk,” Jurnal Teknologi Informasi Dan Komunikasi, vol. 16, no. 2, pp. 17–25, 2025, doi: 10.51903/jtikp.v16i2.1011.
[11] M. Ogedengbe, S. Junaidu, and D. Kana, “Adaptive Minimum Support Threshold for Association Rule Mining,” Indonesian Journal of Data and Science, vol. 5, no. 2, pp. 101–108, 2024, doi: 10.56705/ijodas.v5i2.134.
[12] B. Sowan, L. Zhang, N. Matar, J. Zraqou, F. Omar, and A. Alnatsheh, “A novel lift adjustment methodology for improving association rule interpretation,” Decision Analytics Journal, vol. 15, no. April, p. 100582, 2025, doi: 10.1016/j.dajour.2025.100582.
[13] K. Singha, P. Parthanadee, A. Kessuvan, and J. Buddhakulsomsiri, “Market Basket Analysis of a Health Food Store in Thailand: A Case Study,” International Journal of Knowledge and Systems Science, vol. 15, no. 1, pp. 1–14, 2024, doi: 10.4018/IJKSS.333617.
[14] R. Shankar and A. Yip, “Transforming Patient Feedback Into Actionable Insights Through Natural Language Processing: Knowledge Discovery and Action Research Study,” JMIR Form. Res., vol. 9, pp. 1–14, 2025, doi: 10.2196/69699.
[15] A. Wijaya, A. Faqih, D. Solihudin, C. L. Rohmat, and S. Eka Permana, “Penerapan Association Rules Menggunakan Algoritma Apriori Untuk Identifikasi Pola Pembelian,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 6, pp. 3871–3878, 2024, doi: 10.36040/jati.v7i6.8270.
[16] Jiawei Han ; Jian Pei ; Hanghang Tong, Data Mining: Concepts and Techniques, 4th ed. Cambridge, MA: Morgan Kaufmann, 2022. doi: 10.1016/C2013-0-18660-6.
[17] D. Fan, W. Jiabin, and L. Sheng, “Optimization of frequent item set mining parallelization algorithm based on spark platform,” Discover Computing, vol. 27, no. 1, 2024, doi: 10.1007/s10791-024-09470-5.
[18] D. L. Olson and G. Lauhoff, Computational Risk Management Descriptive Data Mining. 2021. [Online]. Available: http://www.springer.com/series/8827
[19] B. A. Alrahwan and M. Farouk, “ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure,” International Journal of Intelligent Systems, vol. 2024, 2024, doi: 10.1155/2024/8781318.
[20] M. Dehghani and Z. Yazdanparast, “Discovering the symptom patterns of COVID-19 from recovered and deceased patients using Apriori association rule mining,” Inform. Med. Unlocked, vol. 42, no. September, p. 101351, 2023, doi: 10.1016/j.imu.2023.101351.
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