Finding The Most Desirable Car Using K-Nearest Neighbor From E-Commerce Websites
DOI:
https://doi.org/10.31961/eltikom.v5i1.221Keywords:
E-Commerce, Euclidean Distance, K Nearest Neighbors, Manhattan Distance, Minkowski DistanceAbstract
The increasing number of cars that have been released to the market makes it more difficult for buyer to choose the choice of car that fits with their desired criteria such as transmission, number of kilometers, fuel type, and the year the car was made. The method that is suitable in determining the criteria desired by the community is the K-Nearest Neighbors (KNN). This method is used to find the lowest distance from each data in a car with the criteria desired by the buyer. Euclidean, Manhattan, and Minkowski distance are used for measuring the distance. For supporting the selection of cars, we need an automatic data col-lection method by using web crawling in which the system can retrieve car data from several ecommerce websites. With the construction of the car search system, the system can help the buyer in choosing a car and Euclidean distance has the best accuracy of 94.40%.
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References
“Automotive Revolution & Perspective Towards 2030,” 2016. doi: 10.1365/s40112-016-1117-8.
A. Awalinah, S. Arifin, M. Saf. Sistem Pendukung Keputusan Pembelian Mobil dengan Membandingkan Metode Analytic Hierachy Process dan Fuzzy Associative Memory. Jurnal Teknologi dan Sistem Informasi, pp. 89-100. 2017
S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN classification with different numbers of nearest neighbors,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 5, pp. 1774–1785, 2018, doi: 10.1109/TNNLS.2017.2673241.
S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, “Learning k for kNN Classification,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 3, 2017, doi: 10.1145/2990508.
X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008.
S. Mutrofin, A. Mu'alif, R,V,H. Ginardi, and C Fatichah. Optimasi Teknik Klasifikasi Modified k Nearest Neighbor Menggunakan Algoritma Genetika. Jurnal Gamma, pp:2. 2019
J. Chen, Q. Jin, S. Zhao, S. Bao, L. Zhang, Z. Su, Y. Yu. “Boosting Recommendation in Unexplored Categories by User Price Preference,” ACM Trans. Inf. Syst, pp. 12:1-12:27. 2019
Li, H., Chan, T.N., Yiu, M.L., Mamoulis, N. “FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems,” in Proceedings of the 2017 ACM International Conference on Management of Data, pp 835-850. 2017
U. Janjarasuk, and S. Puengrusme, “Product Recommendation based on Genetic Algorithm,” in Proceedings of the 14th International Conference on Applied Sciences, and Technology (ICEAST), pp. 1-4. 2019
M. Chelliah and S. Sarkar. “Product Recommendations Enhanced with Reviews,” in Proceedings of the Eleventh ACM Conference on Recommender Systems, pp 398-399. 2017
X, W. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li “We Know What You Want to Buy: A Demographic-based System for Product Recommendation on Microblogs,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1935-1944. 2014
V. R. Prasetyo, "Searching Cheapest Product on Three Different E-Commerce Using K-Means Algorithm," 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), Bali, Indonesia, 2018, pp. 239-244, doi: 10.1109/ISITIA.2018.8711043.
Y. H. Cho, J. K. Kim, and S. H. Kim, “A personalized recommender system based on web usage mining and decision tree induction,” Expert Syst. Appl., vol. 23, no. 3, pp. 329–342, 2002, doi: https://doi.org/10.1016/S0957-4174(02)00052-0.
P. Tan, M, Steinbach and V. Kumar. Introduction to Data Mining first edition, Addison-Wesley Longman Publishing Co., Inc. 2005
T. Jinhui, H, Richang, Y. Shuicheng, C. Tat-Seng, Q Guo-Jun, J. Ramesh. “Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images” in ACM Transactions on Intelligent Systems and Technology, vol 3. 2011
J. Ding, H. D. Cheng, M. Xian, Y. Zhang, and F. Xu, “Local-weighted Citation-kNN algorithm for breast ultrasound image classification,” Optik (Stuttg)., vol. 126, no. 24, pp. 5188–5193, 2015, doi: https://doi.org/10.1016/j.ijleo.2015.09.231.
K. S. Angel Viji and D. Hevin Rajesh, “An Efficient Technique to Segment the Tumor and Abnormality Detection in the Brain MRI Images Using KNN Classifier,” Mater. Today Proc., vol. 24, pp. 1944–1954, 2020, doi: https://doi.org/10.1016/j.matpr.2020.03.622.
B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based Framework for Text Categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014, doi: https://doi.org/10.1016/j.proeng.2014.03.129.
Z. Chen, L. J. Zhou, X. Da Li, J. N. Zhang, and W. J. Huo, “The Lao Text Classification Method Based on KNN,” Procedia Comput. Sci., vol. 166, pp. 523–528, 2020, doi: https://doi.org/10.1016/j.procs.2020.02.053.
S. Tan, “An effective refinement strategy for KNN text classifier,” Expert Syst. Appl., vol. 30, no. 2, pp. 290–298, 2006, doi: https://doi.org/10.1016/j.eswa.2005.07.019.
M. Han, K. P. Jian. Data Mining Concepts and Techniques Third Edition, Morgan Kaufmann Publishers is an imprint of Elsevier. 225 Wyman Street, Waltham, MA 02451, USA, ISBN 978-0- 12-381479-1. 2012
A. Awalinah, S. Arifin, M. Saf. Sistem Pendukung Keputusan Pembelian Mobil dengan Membandingkan Metode Analytic Hierachy Process dan Fuzzy Associative Memory. Jurnal Teknologi dan Sistem Informasi, pp. 89-100. 2017
S. Zhang, X. Li, M. Zong, X. Zhu, and R. Wang, “Efficient kNN classification with different numbers of nearest neighbors,” IEEE Trans. Neural Networks Learn. Syst., vol. 29, no. 5, pp. 1774–1785, 2018, doi: 10.1109/TNNLS.2017.2673241.
S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, “Learning k for kNN Classification,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 3, 2017, doi: 10.1145/2990508.
X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008.
S. Mutrofin, A. Mu'alif, R,V,H. Ginardi, and C Fatichah. Optimasi Teknik Klasifikasi Modified k Nearest Neighbor Menggunakan Algoritma Genetika. Jurnal Gamma, pp:2. 2019
J. Chen, Q. Jin, S. Zhao, S. Bao, L. Zhang, Z. Su, Y. Yu. “Boosting Recommendation in Unexplored Categories by User Price Preference,” ACM Trans. Inf. Syst, pp. 12:1-12:27. 2019
Li, H., Chan, T.N., Yiu, M.L., Mamoulis, N. “FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems,” in Proceedings of the 2017 ACM International Conference on Management of Data, pp 835-850. 2017
U. Janjarasuk, and S. Puengrusme, “Product Recommendation based on Genetic Algorithm,” in Proceedings of the 14th International Conference on Applied Sciences, and Technology (ICEAST), pp. 1-4. 2019
M. Chelliah and S. Sarkar. “Product Recommendations Enhanced with Reviews,” in Proceedings of the Eleventh ACM Conference on Recommender Systems, pp 398-399. 2017
X, W. Zhao, Y. Guo, Y. He, H. Jiang, Y. Wu, and X. Li “We Know What You Want to Buy: A Demographic-based System for Product Recommendation on Microblogs,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1935-1944. 2014
V. R. Prasetyo, "Searching Cheapest Product on Three Different E-Commerce Using K-Means Algorithm," 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), Bali, Indonesia, 2018, pp. 239-244, doi: 10.1109/ISITIA.2018.8711043.
Y. H. Cho, J. K. Kim, and S. H. Kim, “A personalized recommender system based on web usage mining and decision tree induction,” Expert Syst. Appl., vol. 23, no. 3, pp. 329–342, 2002, doi: https://doi.org/10.1016/S0957-4174(02)00052-0.
P. Tan, M, Steinbach and V. Kumar. Introduction to Data Mining first edition, Addison-Wesley Longman Publishing Co., Inc. 2005
T. Jinhui, H, Richang, Y. Shuicheng, C. Tat-Seng, Q Guo-Jun, J. Ramesh. “Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images” in ACM Transactions on Intelligent Systems and Technology, vol 3. 2011
J. Ding, H. D. Cheng, M. Xian, Y. Zhang, and F. Xu, “Local-weighted Citation-kNN algorithm for breast ultrasound image classification,” Optik (Stuttg)., vol. 126, no. 24, pp. 5188–5193, 2015, doi: https://doi.org/10.1016/j.ijleo.2015.09.231.
K. S. Angel Viji and D. Hevin Rajesh, “An Efficient Technique to Segment the Tumor and Abnormality Detection in the Brain MRI Images Using KNN Classifier,” Mater. Today Proc., vol. 24, pp. 1944–1954, 2020, doi: https://doi.org/10.1016/j.matpr.2020.03.622.
B. Trstenjak, S. Mikac, and D. Donko, “KNN with TF-IDF based Framework for Text Categorization,” Procedia Eng., vol. 69, pp. 1356–1364, 2014, doi: https://doi.org/10.1016/j.proeng.2014.03.129.
Z. Chen, L. J. Zhou, X. Da Li, J. N. Zhang, and W. J. Huo, “The Lao Text Classification Method Based on KNN,” Procedia Comput. Sci., vol. 166, pp. 523–528, 2020, doi: https://doi.org/10.1016/j.procs.2020.02.053.
S. Tan, “An effective refinement strategy for KNN text classifier,” Expert Syst. Appl., vol. 30, no. 2, pp. 290–298, 2006, doi: https://doi.org/10.1016/j.eswa.2005.07.019.
M. Han, K. P. Jian. Data Mining Concepts and Techniques Third Edition, Morgan Kaufmann Publishers is an imprint of Elsevier. 225 Wyman Street, Waltham, MA 02451, USA, ISBN 978-0- 12-381479-1. 2012
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Published
15-05-2022
How to Cite
[1]
Naufal, M.F. and Wibisono, Y.R. 2022. Finding The Most Desirable Car Using K-Nearest Neighbor From E-Commerce Websites. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer. 5, 1 (May 2022), 25–31. DOI:https://doi.org/10.31961/eltikom.v5i1.221.
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