Machine Learning Rekomendasi Produk dalam Penjualan Menggunakan Metode Item-Based Collaborative Filtering
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Abstract
The shift towards Industry 4.0 has pushed many companies to adopt a digital system. With the sheer amount of data available today, companies start to face difficulties with providing product recommendation to their customers. As a result, data analysis has become increasingly important in the pursuit of providing the best service (user experience) to customers. The location appointed in this research is PT. Sentral Tukang Indonesia which is engaged in the sale of building materials and carpentry tools such as: paint, plywood, aluminum, ceramics, and hpl. Machine Learning has emerged as a possible solution in the field of data analysis. The recommendation system emerged as a solution in providing product recommendation based on interactions between customers in historical sales data. The purpose of this study is to assist companies in providing product recommendation to increase sales, to make it easier for customers to find the products they need, providing the best service (user experience) to customers. The data used is customer, item, and historical sales at PT. Sentral Tukang Indonesia over a time span of 1 period.data historical sales divide to dataset training 80% and dataset testing 20%. The Item-based Collaborative Filtering method used in this study uses Cosine Similarity algorithm to calculate the level of similarity between products. Score prediction uses Weighted Sum formula while computation of error rate uses the Root Mean Squared Error formula. The result of this study shows top 10 product recommendations per customer. The products displayed are products with the highest score from the individual customer. This research can be used as a reference by companies looking to provide product recommendations needed by their customers.
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[1]Sharon, Defit, S., & Nurcahyo, G. W. (2021). Tingkat Efisiensi Penggunaan Resep Dokter Spesialis Menggunakan Metode K-Means Clustering. Jurnal Informasi Dan Teknologi, 121–127. https://doi.org/10.37034/jidt.v3i3.118
[2]Jan Wira Gotama Putra, 2020. Pengenalan Konsep Pembelajaran Mesin dan Deep Learning, Tokyo.
[3]Islamiyah, M., Subekti, P., & Andini, T. D. (2019). Utilization of CollaboratPemanfaatan Metode Based Collaborative Filtering Untuk Rekomendasi Wisata Di Kabupaten Malangive Filtering Method for Tourism Recommendations in Malang Regency. Jurnal Ilmiah Teknologi Informasi Asia, 13(2), 143. https://doi.org/10.32815/jitika.v13i2.70
[4]Setiawan, Y., Nurwanto, A., & Erlansari, A. (2019). Implementasi Item Based Collaborative Filtering Dalam Pemberian Rekomendasi Agenda Wisata Berbasis Android. Pseudocode, 6(1), 13–20. https://doi.org/10.33369/pseudocode.6.1.13-20
[5]Ramzan, B., Bajwa, I. S., Jamil, N., Amin, R. U., Ramzan, S., Mirza, F., & Sarwar, N. (2019). An Intelligent Data Analysis for Recommendation Systems Using Machine Learning. Scientific Programming, 2019, 1–20. https://doi.org/10.1155/2019/5941096
[6]Jepriana I. W., Hanief S, (2020). Analisis dan Implementasi Metode Item- Based Collaborative Filtering Untuk Sistem Rekomendasi Stmik Stikom Bali. Janapati, 9(2), 171-180. http://dx.doi.org/10.23887/janapati.v9i2.23218
[7]Prasetyo, B., Haryanto, H., Astuti, S., Astuti, E. Z., & Rahayu, Y. (2019). Implementasi Metode Item-Based Collaborative Filtering dalam Pemberian Rekomendasi Calon Pembeli Aksesoris Smartphone. Eksplora Informatika, 9(1), 17–27. https://doi.org/10.30864/eksplora.v9i1.244
[8]Putra, A. I., & Santika, R. R. (2020). Implementasi Machine Learning dalam Penentuan Rekomendasi Musik dengan Metode Content-Based Filtering. Edumatic : Jurnal Pendidikan Informatika, 4(1), 121–130. https://doi.org/10.29408/edumatic.v4i1.2162
[9]Feng, J., Fengs, X., Zhang, N., & Peng, J. (2018). An improved collaborative filtering method based on similarity. PLOS ONE, 13(9), e0204003. https://doi.org/10.1371/journal.pone.0204003
[10]Kouadria, A., Nouali, O., & Al-Shamri, M. Y. H. (2019). A Multi-criteria Collaborative Filtering Recommender System Using Learning-to-Rank and Rank Aggregation. Arabian Journal for Science and Engineering, 45(4), 2835–2845. https://doi.org/10.1007/s13369-019-04180-3
[11]Rana, A., & Deeba, K. (2019). Online Book Recommendation System using Collaborative Filtering (With Jaccard Similarity). Journal of Physics: Conference Series, 1362, 012130. https://doi.org/10.1088/1742-6596/1362/1/012130
[12]Kumar Ojha, R., & Bhagirathi Nayak, D. (2018). Application of Machine Learning in Collaborative Filtering Recommender Systems. International Journal of Engineering & Technology, 7(4.38), 213. https://doi.org/10.14419/ijet.v7i4.38.24445
[13]Lu, Q., & Xia, J. (2019). Research on the Application of Item-based Collaborative Filtering Algorithms in MOOC. Journal of Physics: Conference Series, 1302, 032020. https://doi.org/10.1088/1742-6596/1302/3/032020
[14]Sinaga, A. S. R. (2020). Machine Learning Prediksi Karakter Pengguna Hastag (#) Bahasa Generasi Milenial Di Sosial Media. Indonesian Journal of Applied Informatics, 4(2), 165. https://doi.org/10.20961/ijai.v4i2.41764
[15]Pambudi, H. K., Kusuma, P. G. A., Yulianti, F., & Julian, K. A. (2020). PREDIKSI STATUS PENGIRIMAN BARANG MENGGUNAKAN METODE MACHINE LEARNING. Jurnal Ilmiah Teknologi Infomasi Terapan, 6(2), 100–109. https://doi.org/10.33197/jitter.vol6.iss2.2020.396
[16]Sari, K. R., Suharso, W., & Azhar, Y. (2020). Pembuatan Sistem Rekomendasi Film dengan Menggunakan Metode Item Based Collaborative Filtering pada Apache Mahout. Jurnal Repositor, 2(6), 767. https://doi.org/10.22219/repositor.v2i6.936
[17]Wiputra, M. M., & Shandi, Y. J. (2021). Perancangan Sistem Rekomendasi Menggunakan Metode Collaborative Filtering dengan Studi Kasus Perancangan Website Rekomendasi Film. Media Informatika, 20(1), 1–18. https://doi.org/10.37595/mediainfo.v20i1.54
[18]Jaja, V. L., Susanto, B., & Sasongko, L. R. (2020). Penerapan Metode Item-Based Collaborative Filtering Untuk Sistem Rekomendasi Data MovieLens. d’CARTESIAN, 9(2), 78. https://doi.org/10.35799/dc.9.2.2020.28274