- P. Nadkarni and P. Nadkarni, “Core technologies: data mining and big data,” in Clinical Research Computing, Academic Press, 2016, pp. 187–204. doi: 10.1016/B978-0-12-803130-8.00010-5
- D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method,” Applied Computing and Informatics, vol. 12, no. 1, pp. 90–108, 2016. doi: 10.1016/j.aci.2014.10.001
- B. Santosa dan A. Umam, Data Mining dan Big Data Analytics, edisi 1. Yogyakarta: Penebar Media Pustaka, 2018, pp. 110–113
- S. Zhang, D. Cheng, Z. Deng, M. Zong, and X. Deng, “A novel kNN algorithm with data-driven k parameter computation,” Pattern Recognition Letters, vol. 109, pp. 44–54, 2018. doi: 10.1016/j.patrec.2017.09.036
- R. Goyal, P. Chandra, and Y. Singh, "Suitability of kNN regression in the development of interaction based software fault prediction models," IERI Procedia, vol. 6, pp. 15-21, 2014. doi: 10.1016/j.ieri.2014.03.004
- J. F. Ajao, D. O. Olawuyi, and O. O. Odejobi, "Yoruba handwritten character recognition using Freeman chain code and k-nearest neighbor classifier," Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 4, pp. 129-134, 2018. doi: 10.14710/jtsiskom.6.4.2018.129-134
- A. M. Nagy and V. Simon, "Survey on traffic prediction in smart cities," Pervasive and Mobile Computing, vol. 50, pp. 148-163, 2018. doi: 10.1016/j.pmcj.2018.07.004
- A. Priadana and A. W. Murdiyanto, "Metode SURF dan FLANN untuk identifikasi nominal uang kertas Rupiah tahun emisi 2016 pada variasi rotasi," Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 1, pp. 19-24, 2019. doi: 10.14710/jtsiskom.7.1.2019.19-24
- F. Martínez, M. P. Frías, M. D. Pérez, and A. J. Rivera, "A methodology for applying k-nearest neighbor to time series forecasting," Artificial Intelligence Review, vol. 52, no. 3, pp. 2019-2037, 2019. doi: 10.1007/s10462-017-9593-z
- V. Nguyen Thanh Le, B. Apopei, and K. Alameh, "Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods," Information Processing in Agriculture, vol. 6, no. 1, pp. 116-131, 2019. doi: 10.1016/j.inpa.2018.08.002
- M. A. Mabayoje, A. O. Balogun, H. A. Jibril, J. O. Atoyebi, H. A. Mojeed, and V. E. Adeyemo, "Parameter tuning in kNN for software defect prediction: an empirical analysis," Jurnal Teknologi dan Sistem Komputer, vol. 7, no. 4, pp. 121-126, 2019. doi: 10.14710/jtsiskom.7.4.2019.121-126
- S. Zhang, X. Li, M. Zong, X. Zhu, and D. Cheng, "Learning k for kNN classification," ACM Transactions on Intelligent Systems and Technology, vol. 8, no. 3:43, pp. 1-19, 2017. doi: 10.1145/2990508
- Y. Song, J. Liang, J. Lu, and X. Zhao, "An efficient instance selection algorithm for k nearest neighbor regression," Neurocomputing, vol. 251, pp. 26-34, 2017. doi: 10.1016/j.neucom.2017.04.018
- S. Zhang, "Cost-sensitive KNN classification," Neurocomputing, vol. 391, pp. 234-242, 2019. doi: 10.1016/j.neucom.2018.11.101
- F. Martínez, M. P. Frías, M. D. Pérez-Godoy, and A. J. Rivera, "Dealing with seasonality by narrowing the training set in time series forecasting with kNN," Expert Systems with Applications, vol. 103, pp. 38-48, 2018. doi: 10.1016/j.eswa.2018.03.005
- B. E. Flores, "A pragmatic view of accuracy measurement in forecasting," Omega, vol. 14, no. 2, pp. 93-98, 1986. doi: 10.1016/0305-0483(86)90013-7
- J. S. Armstrong, Long-range Forecasting: from crystal ball to computer, 2ed. Wiley, 1985. doi: 10.1016/0169-2070(86)90059-2
- Y. Cai, H. Huang, H. Cai, dan Y. Qi, "A K-nearest neighbor locally search regression algorithm for short-term traffic flow forecasting," in 9th International Conference on Modelling, Identification and Control, Kunming, China, Jul. 2017, pp. 624-629. doi: 10.1109/ICMIC.2017.8321530
- S. P. Mahasagara, A. Alamsyah, and B. Rikumahu, "Indonesia infrastructure and consumer stock portfolio prediction using artificial neural network backpropagation," in 5th International Conference on Information and Communication Technology (ICoIC7), Malacca City, Malaysia, May 2017, pp. 1-4. doi: 10.1109/ICoICT.2017.8074710
- J. Demšar et al., "Orange: data mining toolbox in Python," Journal of Machine Learning Research, vol. 14, pp. 2349-2353, 2013
- F. Pedregosa et al., "Scikit-learn: machine learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011
- JetBrains, "PyCharm: the Python IDE for professional developers by JetBrains," 2017. [online]. Available: https://www.jetbrains.com/pycharm/
- J. D. Hunter, "Matplotlib: A 2D graphics environment," Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007. doi: 10.1109/MCSE.2007.55
- P. Mehta et al., "A high-bias, low-variance introduction to machine learning for physicists," Physics Reports, vol. 810, pp. 1-124, 2019. doi: 10.1016/j.physrep.2019.03.001
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