Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16
DOI:
10.33395/sinkron.v8i4.12936Keywords:
Cosine Similarity, Deep Learning, mAP, Recommendation System, VGG16Abstract
The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Downloads
References
Agarwal, P., Vempati, S., & Borar, S. (2018). Personalizing Similar Product Recommendations in Fashion E-commerce. http://arxiv.org/abs/1806.11371
Badriyah, T., Fernando, R., Syarif, I., Elektronika, P., Surabaya, N., Arief, J., & Hakim, R. (2018). Konferensi Nasional Sistem Informasi 2018 STMIK Atma Luhur Pangkalpinang.
Buvana, M., Muthumayil, K., Kumar, S. S., Nebhen, J., Alshamrani, S. S., & Ali, I. (2021). Deep optimal VGG16 based COVID-19 diagnosis model. Computers, Materials and Continua, 70(1), 43–58. https://doi.org/10.32604/cmc.2022.019331
Dzikri, M., Ilyasa, H., & Yamasari, Y. (2023). Perbandingan Cosine Similarity Dan Euclidean Distance Pada Model Rekomendasi Buku Dengan Metode Item-Based Collaborative Filtering. Journal of Informatics and Computer Science, 04.
Eka Budiyanta, N., Mulyadi, M., Tanudjaja, H., Jaya, A., Jend Sudirman No, J., Semanggi, K., Setiabudi, K., Jakarta Selatan, K., & Jakarta, D. (2021). Sistem Deteksi Kemurnian Beras berbasis Computer Vision dengan Pendekatan Algoritma YOLO. 6(1).
Farhan Hasrul, A., Sembiring, R., & Korespondensi, P. (2020). Analisis pengaruh online customer Review dan rating terhadap minat beli produk elektronik di Tokopedia (Vol. 2).
Fawwaz, I., Candra, T., Marpaung, D. A. M., Dinis, A., & Fachrozi, M. R. (2022). Classification of beetle type using the Convolutional Neural Network algorithm. Sinkron, 7(4), 2340–2348. https://doi.org/10.33395/sinkron.v7i4.11673
Februariyanti, H., Dwi Laksono, A., Sasongko Wibowo, J., & Siswo Utomo, M. (2021). Implementasi Metode Collaborative Filtering Untuk Sistem Rekomendasi Penjualan Pada Toko Mebel. www.unisbank.ac.id
Gunawan, D., Sembiring, C. A., & Budiman, M. A. (2018). The Implementation of Cosine Similarity to Calculate Text Relevance between Two Documents. Journal of Physics: Conference Series, 978(1). https://doi.org/10.1088/1742-6596/978/1/012120
Halim, J., & Fajar, A. N. (2023). INFORMASI (Jurnal Informatika dan Sistem Informasi) Klasifikasi Pisang Berbasis Algoritma VGG16 Melalui Metode CNN Deep Learning.
Halim, P. B., Herwindiati, D. E., & Sitorus, M. (2022). Sistem Rekomendasi Makanan Tio Ciu Menggunakan Collaborative Filtering. In Computatio: Journal of Computer Science and Information Systems (Vol. 6, Issue 2). https://medium.com/analytics-vidhya/overview-of-
Jiang, Z. P., Liu, Y. Y., Shao, Z. E., & Huang, K. W. (2021). An improved VGG16 model for pneumonia image classification. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311185
Khufa Rahmada Aula, B., Fatichah, C., & Purwitasari, D. (2021). Sistem Rekomendasi pada Forum Kesehatan Dengan Pemeringkatan Pertanyaan Serupa Menggunakan Pendekatan Deep Learning. www.dokter.id
Mandal, S. (2022). VGG-16 Convolutional Neural Networks For Brain Tumour Detection. 05(01). www.shodhsamagam.com
Muhiban, A., & Putri, E. K. (2022). Pengaruh Tampilan Produk dan Electronic Word of Mouth Terhadap Keputusan Pembelian Pada E-commerce Shopee (Studi Kasus Konsumen Shopee di PT. Gucci Ratu Textile Kota Cimahi). Jurnal EMT KITA, 6(2), 249–266. https://doi.org/10.35870/emt.v6i2.633
Pradana, D. R., Sa’adah, S., & Nurjanah, D. (2022). Sistem Rekomendasi Sepatu Lokal Menggunakan Metode Collaborative Filtering Pada Toko Sepatu Tarsius Store. E-Proceeding of Engineering, 9, 2216–2176.
Resta, O. A., Aditya, A., & Purwiantono, F. E. (2021). Plagiarism Detection in Students’ Theses Using The Cosine Similarity Method. SinkrOn, 5(2), 305–313. https://doi.org/10.33395/sinkron.v5i2.10909
Rifai, M. A., & Anugrah, I. G. (2021). Semantic Search for Scientific Articles by Language Using Cosine Similarity Algorithm and Weighted Tree Similarity. Journal of Development Research, 5(2), 106–114. https://doi.org/10.28926/jdr.v5i2.150
Saeed, A. A. M., & Taqa, A. Y. (2022). A proposed approach for plagiarism detection in Article documents. SinkrOn, 7(2), 568–578. https://doi.org/10.33395/sinkron.v7i2.11381
Saleh, A., Dharshinni, N., Perangin-Angin, D., Azmi, F., & Sarif, M. I. (2023). Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm. Sinkron, 8(1), 256–267. https://doi.org/10.33395/sinkron.v8i1.11954
Singh, R. H., Maurya, S., Tripathi, T., Narula, T., & Srivastav, G. (2020). Movie Recommendation System using Cosine Similarity and KNN. International Journal of Engineering and Advanced Technology, 9(5), 556–559. https://doi.org/10.35940/ijeat.E9666.069520
Solihat, M., & Sandika, D. (2022). E-commerce di Industri 4.0. https://doi.org/10.32812/jibeka.v16i2.967
Sujasman, Mb., & Syazili, A. (2020). Implementasi Metode Cosine Similarity Untuk Rekomendasi Produk Pada Aplikasi Penjualan Berbasis Mobile. Bina Darma Conference on Computer Science.
Syachrul Maulana Nizal, M., & Dwiati Wismarini, T. (2022). Jurnal Teknik Informatika Rekomendasi Produk Pakaian Menggunakan Metode Item Based Collaborative Filtering Pada Toko Online Di Jawa Tengah Berbasis Web. JURNAL TEKNIK INFORMATIKA. https://doi.org/10.51530/jutekin.v10i2.632
Universitas Gadjah Mada, Sathāban Thēknōlōyī Phra Čhō̜mklao Čhaokhun Thahān Lātkrabang, Institute of Electrical and Electronics Engineers. Indonesia Section., & Institute of Electrical and Electronics Engineers. (2018). Proceedings of 2018 the 10th International Conference on Information Technology and Electrical Engineering : “Smarter Technology for Better Society” : Ramada Bintang Bali Resort, 24th-26th July 2018, Kuta, Bali.
Yunanda, G., Nurjanah, D., & Meliana, S. (2022). Recommendation System from Microsoft News Data using TF-IDF and Cosine Similarity Methods. Building of Informatics, Technology and Science (BITS), 4(1). https://doi.org/10.47065/bits.v4i1.1670
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2023 Irfan Rasyid, Muhammad Resa Arif Yudianto, Maimunah, Tuessi Ari Purnomo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.