Teknik Bagging pada Ensemble Learning untuk Kategorisasi Produk E-Commerce

Faskal Churniansyah(1*), Danang Wahyu Utomo(2)
(1) Universitas Dian Nuswantoro
(2) Universitas Dian Nuswantoro
(*) Corresponding Author

https://doi.org/10.25077/TEKNOSI.v10i1.2024.92-99


Abstrak


E-commerce merupakan layanan dalam jual beli yang dijalankan secara online melalui media elektronik seperti komputer dan handphone. Adanya perkembangan teknologi informasi yang lebih canggih menjadi pendorong utama dalam meningkatkan kerja e-commerce. Peningkatan yang sering dilakukan adalah menyediakan layanan sebaik – baiknya dan semudah mungkin untuk pelanggan. Banyaknya produk e-commerce yang ditawarkan ke pelanggan menjadi isu utama dalam layanan e-commerce. Tidak sedikit pelanggan yang bingung dalam menentukan pilihan produk. Bahkan beberapa penelitian menyatakan pelanggan yang awam tentang penggunaan e-commerce bingung dalam pemilihan produk. Ada deskripsi atau ulasan produk yang berbeda terhadap produk yang sama. Penelitian ini mengusulkan kategorisasi produk pada layanan e-commerce dengan tujuan menempatkan deskripsi produk sesuai dengan kategori yang telah ditentukan. Teknik bagging adalah Teknik ensemble learning yang mampu membuat beberapa sub pohon keputusan yang nantinya dapat dicari nilai akurasi yang terbaik. Pada hasil pengujian diperoleh bahwa pada pengaturan hyperparameter n_estimators 200 menghasilkan nilai akurasi terbaik dengan nilai 93,25%., precision 93%, recall 93% dan f1-score 93%.

Kata Kunci


Kategorisasi produk, e-commerce, bagging, ensemble learning


Teks Lengkap:

PDF


Referensi


[1] H. Al Mashalah, E. Hassini, A. Gunasekaran, and D. Bhatt (Mishra), “The impact of digital transformation on supply chains through e-commerce: Literature review and a conceptual framework,” Transp Res E Logist Transp Rev, vol. 165, p. 102837, Sep. 2022, doi: 10.1016/j.tre.2022.102837.

[2] R. E. Bawack, S. F. Wamba, K. D. A. Carillo, and S. Akter, “Artificial intelligence in E-Commerce: a bibliometric study and literature review,” Electronic Markets, vol. 32, no. 1, pp. 297–338, Mar. 2022, doi: 10.1007/s12525-022-00537-z.

[3] M. Oase Ansharullah, W. Agustin, L. Lusiana, J. Junadhi, S. Erlinda, and F. Zoromi, “Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method,” JAIA-Journal Of Artificial Intelligence And Applications, vol. 2, no. 2, pp. 15–22, 2022.

[4] L. Donati, E. Iotti, G. Mordonini, and A. Prati, “Fashion Product Classification through Deep Learning and Computer Vision,” Applied Sciences, vol. 9, no. 7, p. 1385, Apr. 2019, doi: 10.3390/app9071385.

[5] S. Suci Indasari and A. Tjahyanto, “Automatic Categorization of Multi Marketplace FMCGs Products using TF-IDF and PCA Features,” Jurnal SISFOKOM ( Sistem Informasi dan Komputer ), vol. 12, pp. 198–204, 2023.

[6] P. Ristoski, P. Petrovski, P. Mika, and H. Paulheim, “A machine learning approach for product matching and categorization,” Semant Web, vol. 9, no. 5, pp. 707–728, Aug. 2018, doi: 10.3233/SW-180300.

[7] L. Tan, M. Y. Li, and S. Kok, “E-Commerce Product Categorization via Machine Translation,” in ACM Transactions on Management Information Systems, Association for Computing Machinery, Aug. 2020. doi: 10.1145/3382189.

[8] S. Jain and V. Kumar, “Garment categorization using data mining techniques,” Symmetry (Basel), vol. 12, no. 6, Jun. 2020, doi: 10.3390/SYM12060984.

[9] H. Kim, G. Joo, and H. Im, “Product Category Classification using Word Embedding and GRUs,” The Journal of Korean Institute of Information Technology, vol. 19, no. 4, pp. 11–18, Apr. 2021, doi: 10.14801/jkiit.2021.19.4.11.

[10] H. Jahanshahi et al., “Text Classification for Predicting Multi-level Product Categories,” Sep. 2021.

[11] R. Bruni and G. Bianchi, “Website categorization: A formal approach and robustness analysis in the case of e-commerce detection,” Expert Syst Appl, vol. 142, p. 113001, Mar. 2020, doi: 10.1016/j.eswa.2019.113001.

[12] V. Gomero-Fanny, A. Ruiz Bengy, and L. Andrade-Arenas, “Prototype of Web System for Organizations Dedicated to e-Commerce under the SCRUM Methodology,” 2021. [Online]. Available: www.ijacsa.thesai.org

[13] D. M. Alghazzawi, A. G. A. Alquraishee, S. K. Badri, and S. H. Hasan, “ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review,” Sustainability (Switzerland), vol. 15, no. 9, May 2023, doi: 10.3390/su15097076.

[14] P. Kalaivani, “Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application,” Indian J Sci Technol, vol. 13, no. 28, pp. 2849–2857, Jul. 2020, doi: 10.17485/IJST/v13i28.927.

[15] M. Pawłowski, “Machine Learning Based Product Classification for eCommerce,” Journal of Computer Information Systems, vol. 62, no. 4, pp. 730–739, 2022, doi: 10.1080/08874417.2021.1910880.

[16] K. POTHUGANTI, “Open-World Classification Algorithm to Product Identification,” SSRN Electronic Journal, 2019, doi: 10.2139/ssrn.3719055.

[17] B. Sun, H. Chen, J. Wang, and H. Xie, “Evolutionary under-sampling based bagging ensemble method for imbalanced data classification,” Front Comput Sci, vol. 12, no. 2, pp. 331–350, Apr. 2018, doi: 10.1007/s11704-016-5306-z.

[18] X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, “A survey on ensemble learning,” Front Comput Sci, vol. 14, no. 2, pp. 241–258, Apr. 2020, doi: 10.1007/s11704-019-8208-z.

[19] Q.-F. Li and Z.-M. Song, “High-performance concrete strength prediction based on ensemble learning,” Constr Build Mater, vol. 324, p. 126694, Mar. 2022, doi: 10.1016/j.conbuildmat.2022.126694.

[20] Y. Liu, “High-Performance Concrete Strength Prediction Based on Machine Learning,” Comput Intell Neurosci, vol. 2022, pp. 1–7, May 2022, doi: 10.1155/2022/5802217.

[21] G. Ngo, R. Beard, and R. Chandra, “Evolutionary bagging for ensemble learning,” Neurocomputing, vol. 510, pp. 1–14, Oct. 2022, doi: 10.1016/j.neucom.2022.08.055.

[22] Q.-F. Li and Z.-M. Song, “High-performance concrete strength prediction based on ensemble learning,” Constr Build Mater, vol. 324, p. 126694, Mar. 2022, doi: 10.1016/j.conbuildmat.2022.126694.

[23] B. Sun, H. Chen, J. Wang, and H. Xie, “Evolutionary under-sampling based bagging ensemble method for imbalanced data classification,” Front Comput Sci, vol. 12, no. 2, pp. 331–350, Apr. 2018, doi: 10.1007/s11704-016-5306-z.

[24] P. D. Caie, N. Dimitriou, and O. Arandjelović, “Precision medicine in digital pathology via image analysis and machine learning,” in Artificial Intelligence and Deep Learning in Pathology, Elsevier, 2021, pp. 149–173. doi: 10.1016/B978-0-323-67538-3.00008-7.

[25] S. Hong and H. S. Lynn, “Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction,” BMC Med Res Methodol, vol. 20, no. 1, p. 199, Dec. 2020, doi: 10.1186/s12874-020-01080-1.

[26] S. Amien, P. Perdana, T. Bharata Aji, and R. Ferdiana, “Aspect CategoryClassification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia (Aspect Category Classification with Machine Learning Approach Using Indonesian Language Dataset),” 2021.

[27] S. Jain and V. Kumar, “Garment categorization using data mining techniques,” Symmetry (Basel), vol. 12, no. 6, Jun. 2020, doi: 10.3390/SYM12060984.

[28] M. Oase Ansharullah, W. Agustin, L. Lusiana, J. Junadhi, S. Erlinda, and F. Zoromi, “Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method,” JAIA-Journal Of Artificial Intelligence And Applications, vol. 2, no. 2, pp. 15–22, 2022.


Artikel Statistik

Abstrak telah dilihat : 8 kali
PDF telah dilihat : 4 kali

Refbacks

  • Saat ini tidak ada refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Alamat Redaksi :
Departemen Sistem Informasi, Fakultas Teknologi Informasi
Universitas Andalas
Kampus Limau Manis, Padang 25163, Sumatera Barat

email: teknosi@fti.unand.ac.id

  Jumlah Pengunjung :

 

Creative Commons License
This work by JSI-Unand and licensed under a CC BY-SA 4.0 International License.