Sistem Rekomendasi Produk Sparepart Motor Menggunakan Metode Knowledge Based Recommendation
-
Vony Nur Alizah
*
Universitas Merdeka Pasuruan, Pasuruan,
Indonesia
- Anang Aris Widodo Universitas Merdeka Pasuruan, Pasuruan, Indonesia
- Nanda Martyan Anggadimas Universitas Merdeka Pasuruan, Pasuruan, Indonesia (*) Corresponding Author
Abstract
The rapid growth of e-commerce has led to an increasing number of motorcycle spare part products available across various online marketplaces. This condition often makes it difficult for users to select products that match their vehicle type, needs, and budget. This study aims to develop a motorcycle spare parts recommendation system using the Knowledge-Based Recommendation method to assist users in obtaining suitable product recommendations based on their stated preferences. The system was developed using the Python programming language on the Google Colab platform and utilized a dataset consisting of 100 motorcycle spare part products with attributes including product name, motorcycle compatibility, product category, price, and rating. The recommendation process was carried out by matching user preferences with product attributes, after which each product was scored using a weighted calculation with compatibility weighted at 45%, product category at 30%, price at 15%, and rating at 10%. The system was evaluated using 10 testing scenarios by assessing the Top-1 recommendation. The evaluation results showed that all testing scenarios successfully generated recommendations that matched user requirements, achieving an accuracy of 100%. The findings indicate that the implementation of the Knowledge-Based Recommendation method, combined with a weighted attribute mechanism based on motorcycle compatibility, product category, price, and rating, is capable of producing recommendations that align with user preferences without requiring users' purchase history or rating history. Furthermore, the proposed method was found to be effective for motorcycle spare parts recommendation systems and has the potential to assist users in selecting appropriate products more quickly and accurately.
Downloads
References
A. Sukanda, “Sistem Rekomendasi Menggunakan Algoritma Apriori Pada Aplikasi E-Commerce Toko Sudirman Sport,” Jurnal Nasional Ilmu Komputer, vol. 2, no. 1, pp. 2746–1343, 2021, doi: 10.47747/jurnalnik.v2i1.523.
A. Gusti Pradana, M. Wicaksono, and Purwanto, “Sistem Rekomendasi Produk Pada E-Commerce Menggunakan Metode Simple Additive Weighting (SAW) Dan Clustering K-Means,” Journal of Data Analytics, Information, and Computer Science (JDAICS), vol. 3, no. 1, Jan. 2026, doi: doi.org/10.70248/jdaics.v3i1.3524.
E. Novalia, E. E. Awal, A. Hananto, T. Tarmuji, and P. W. Setiawan, “Perancangan Sistem Rekomendasi Produk pada E-Commerce Toko Sendal Grosir Menggunakan Algoritma Content-Based Filtering,” Journal of Information System, Applied, Management, Accounting and Research, vol. 9, no. 4, p. 1591, Nov. 2025, doi: 10.52362/jisamar.v9i4.2130.
Y. Dananjoyo and Y. Muflihah, “Sistem Rekomendasi Ukuran Baju Pada Aplikasi E-Commece Dengan Metode Naïve Bayes,” JAMASTIKA, vol. 3, no. 1, 2024, doi: https://doi.org/10.35473/jamastika.v3i1.2402.
H. S. Ziliwu, “Pengambangan Sistem Rekomendasi Produk dengan Algoritma Collaborative Filtering Dan Teknik machine Learning,” Jurnal Ilmu Ekomoni,Pendidikan,Teknik, vol. 2, no. 1, Jan. 2025, doi: https://doi.org/10.70134/identik.v2i1.302.
M. I. Syarif, M. Hannum, S. Wahyuni, and Nurbaiti, “Potensi Perkembangan E-Commerce Dalam Menunjang Bisnis di Indonesia,” Journal of Computers and Digital Business, vol. 2, no. 1, pp. 11–14, Jan. 2023, doi: 10.56427/jcbd.v2i1.30.
M. Farhan and M. I. P. Nasution, “5 Pendapat Ahli Tentang E-Commerce,” Jurnal Ilmiah Ekonomi Manajemen Bisnis dan Akuntansi, vol. 2, no. 1, pp. 458–463, Dec. 2024, doi: 10.61722/jemba.v2i1.658.
A. A. Rifa’i, H. Dhika, S. Fuadah, and A. A. Rifa’, “Sistem Rekomendasi E-Commerce Produk Makanan Dengan Metode Collaborative Filtering,” JIPETIK, vol. 4, no. 1, pp. 20–29, 2026, [Online]. Available: https://ejournal.lppinpest.org/index.php/jipetik/index
S. W. C. Ngangi, C. A. Jeffta Soewoeh, E. Alfonsius, D. Lapihu, and I. G. N. Aditya Putra, “Sistem Informasi Penjualan Sparepart Motor Berbasis Website (Studi Kasus Pada Bengkel Motorindo),” Journal of Information Technology, Software Engineering and Computer Science (ITSECS), vol. 1, no. 2, pp. 75–83, Apr. 2023, doi: 10.58602/itsecs.v1i2.33.
E. Daniati, R. Indriati, and D. Syahrul, “Sistem Rekomendasi Pembelian Spare Part Sepeda Motor,” SEMNAS INOTEK, vol. 7, no. 3, pp. 2549–7952, Aug. 2023, doi: https://doi.org/10.29407/inotek.v7i3.3691.
Y. Suharya and Y. Herdiana, “Sistem Rekomendasi Untuk Toko Online Kecil Dan Menengah,” Jurnal Teknologi Informasi Komunikasi (e-Journal), vol. 8, no. 2, Dec. 2021, doi: https://doi.org/10.38204/tematik.v8i2.683.
E. Anugerah Rahayu Kasim, Statiswaty, N. Ransi, and Isnawaty, “Sistem Rekomendasi Produk Umkm Menggunakan Algoritma User-Based Collaborative Filtering Berbasis Website Website-Based Msme Product Recommendation System Using User-Based Collaborative Filtering Algorithm,” jurnal sisfotenika, vol. 14, no. 2, 2024, doi: 10.30700/sisfotenika.v14i2.447.
S. I. Adam and W. Grivin, “Implementasi Sistem Rekomendasi Produk E-Commerce Menggunakan Content-Based Filtering Berbasis Cosine Similarity,” Jurnal Sistem Informasi dan Teknik Komputer, vol. 10, no. 2, 2025, doi: https://doi.org/10.51876/simtek.v10i2.1665.
N. Azizah and A. F. Rozi, “Sistem Rekomendasi Produk Somethinc Menggunakan Metode Content-based Filtering,” Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 3, pp. 461–468, Jul. 2024, doi: 10.47233/jteksis.v6i3.1411.
A. I. Hanafi, A. Srirahayu, and A. Farida, “Sistem Rekomendasi Produk Konveksi Pada Deem Clothing Dengan Metode Knowledge Based,” SMATIKA JURNAL, vol. 14, no. 02, pp. 270–283, Dec. 2024, doi: 10.32664/smatika.v14i02.1338.
A. D. Safitri, A. Sulami, and J. Safitri, “Perancangan Sistem Rekomendasi Produk Sepatu Menggunakan Metode Knowledge Base Rekomendation,” Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI), vol. 04, no. 3, 2023, doi: https://doi.org/10.30998/jrami.v4i03.8767.
A. A. P. Adnyana, I. D. M. B. A. Darmawan, and I. G. A. Wibawa, “Perancangan Website E-Commerce Dengan Sistem Rekomendasi Berbasis Collaborative Filtering,” Jurnal Pengabdian Informatika, vol. 1, no. 2, pp. 463–468, Feb. 2023.
A. D. Ulhaq, D. Hartanti, and A. A. Sari, “Sistem Rekomendasi Pemilihan Jenis Lensa Kacamata Menggunakan Metode Knowledge Based Recommendation (Studi Kasus :Optik Wiratama Kacamata 2),” Satuan Tulisan Riset dan Inovasi Teknologi, vol. 10, no. 1, Aug. 2025, doi: http://dx.doi.org/10.30998/string.v10i1.28938.
N. D. Rahmawati and A. P. Utomo, “Sistem Rekomendasi Pakaian Muslim Toko Al-Fath Semarang Dengan Collaborative Filtering,” Jurnal Ilmiah Elektronika Dan Komputer, vol. 15, no. 1, pp. 45–51, 2022, doi: https://doi.org/10.51903/elkom.v15i1.645.
H. K. Rahmatika and Dedi Gunawan, “Sistem Rekomendasi Menu untuk Coffee Shop Menggunakan Algoritma Association Rule Mining,” The Indonesian Journal of Computer Science, vol. 14, no. 1, Feb. 2025, doi: 10.33022/ijcs.v14i1.4598.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Sistem Rekomendasi Produk Sparepart Motor Menggunakan Metode Knowledge Based Recommendation
Pages: 1305-1313
Copyright (c) 2026 Vony Nur Alizah, Anang Aris Widodo, Nanda Martyan Anggadimas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
