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References
- Kementerian Kesehatan Republik Indonesia, “Peran Ditjen Kesmas Daman Pandemi COVID 19 2020-2021,” Jakarta, 2021.
- A. Syauqi, “Jalan panjang covid19 (Sebuah Refleksi Dikala Wabah Merajalela Berdampak Pada Perekonomian),” JKUBS J. Chem. Inf. Model., vol. 1, no. 1, pp. 1–19, 2020.
- N. Mamoon and G. Rasskin, “COVID-19,” 2019. [Online]. Available: https://www.covidvisualizer.com/
- M. F. Asriansyah, “Pandemi Covid 19 dan Upaya Pencegahan,” 2022. https://www.djkn.kemenkeu.go.id/artikel/baca/15799/Pandemi-Covid-19-dan-Upaya-Pencegahan.html
- E. Martínez Chamorro, A. Díez Tascón, L. Ibáñez Sanz, S. Ossaba Vélez, and S. Borruel Nacenta, “Radiologic diagnosis of patients with COVID-19,” Radiologia, vol. 63, no. 1, pp. 56–73, 2021, doi: 10.1016/j.rx.2020.11.001.
- S. Ebrahimzadeh et al., “Thoracic imaging tests for the diagnosis of COVID-19,” 2022. doi: 10.1002/14651858.CD013639.pub5.
- S. Kamthan, H. Singh, and T. Meitzler, “Hierarchical fuzzy deep learning for image classification,” Memories - Mater. Devices, Circuits Syst., vol. 2, no. June, p. 100016, 2022, doi: 10.1016/j.memori.2022.100016.
- X. Zhang, H. Xiao, R. Gao, H. Zhang, and Y. Wang, “K-nearest neighbors rule combining prototype selection and local feature weighting for classification,” Knowledge-Based Syst., vol. 243, 2022, doi: 10.1016/j.knosys.2022.108451.
- Y. Guo, S. Han, Y. Li, C. Zhang, and Y. Bai, “K-Nearest Neighbor combined with guided filter for hyperspectral image classification,” Procedia Comput. Sci., vol. 129, pp. 159–165, 2018, doi: 10.1016/j.procs.2018.03.066.
- J. Wang, P. Neskovic, and L. N. Cooper, “Improving nearest neighbor rule with a simple adaptive distance measure,” Pattern Recognit. Lett., vol. 28, no. 2, pp. 207–213, 2007, doi: 10.1016/j.patrec.2006.07.002.
- J. A. Romero-del-Castillo, M. Mendoza-Hurtado, D. Ortiz-Boyer, and N. García-Pedrajas, “Local-based k values for multi-label k-nearest neighbors rule,” Eng. Appl. Artif. Intell., vol. 116, no. June, p. 105487, 2022, doi: 10.1016/j.engappai.2022.105487.
- S. Ougiaroglou and G. Evangelidis, “Fast and accurate k-nearest neighbor classification using prototype selection by clustering,” Proc. 2012 16th Panhellenic Conf. Informatics, PCI 2012, no. i, pp. 168–173, 2012, doi: 10.1109/PCi.2012.69.
- L. Cai, Y. Song, T. Liu, and K. Zhang, “A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification,” IEEE Access, vol. 8, pp. 152183–152192, 2020, doi: 10.1109/ACCESS.2020.3017382.
- K. Li, H. Wang, W. Wang, F. Wang, and Z. Cui, “Improving artificial bee colony algorithm using modified nearest neighbor sequence,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 8807–8824, 2022, doi: 10.1016/j.jksuci.2021.10.012.
- X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008. doi: 10.1007/s10115-007-0114-2.
- G. I. Okolo, S. Katsigiannis, and N. Ramzan, “IEViT: An enhanced vision transformer architecture for chest X-ray image classification,” Comput. Methods Programs Biomed., vol. 226, p. 107141, 2022, doi: 10.1016/j.cmpb.2022.107141.
- S. Suyanto, P. E. Yunanto, T. Wahyuningrum, and S. Khomsah, “A multi-voter multi-commission nearest neighbor classifier,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6292–6302, 2022, doi: 10.1016/j.jksuci.2022.01.018.
- A. Islam, S. B. Belhaouari, A. U. Rehman, and H. Bensmail, “K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation,” Softw. Impacts, vol. 12, no. February, p. 100272, 2022, doi: 10.1016/j.simpa.2022.100272.
- M. Kumar, N. K. Rath, A. Swain, and S. K. Rath, “Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor,” Procedia Comput. Sci., vol. 54, pp. 301–310, 2015, doi: 10.1016/j.procs.2015.06.035.
- P. Nair et al., “A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method,” Int. J. Sci. Technol. Res., vol. 28, no. 3, pp. 221–226, 2020, doi: 10.1016/j.imu.2021.100825.
- K. U. Syaliman, Yuliska, and N. F. Najwa, “Seleksi Fitur Menggunakan Pendekatan k-Nearest Neighbor,” J. Sist. Inf. dan Teknol. Jar., vol. 3, no. 1, pp. 8–13, 2022.
- K. U. Syaliman, A. Labellapansa, and A. Yulianti, “Improving the Accuracy of Features Weighted k-Nearest Neighbor using Distance Weight,” no. ICoSET 2019, pp. 326–330, 2020, doi: 10.5220/0009390903260330.
- P. A. Charde and S. D. Lokhande, “Classification Using K Nearest Neighbor for Brain Image Retrieval,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 8, pp. 760–765, 2013, doi: 10.25126/jtiik.2020722608.
- L. Farokhah, “Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1129, 2020, doi: 10.25126/jtiik.2020722608.
- Z. Pan, Y. Wang, and W. Ku, “A new k-harmonic nearest neighbor classifier based on the multi-local means,” Expert Syst. Appl., vol. 67, pp. 115–125, 2017, doi: 10.1016/j.eswa.2016.09.031.
- J. Gou, H. Ma, W. Ou, S. Zeng, Y. Rao, and H. Yang, “A generalized mean distance-based k-nearest neighbor classifier,” Expert Syst. Appl., vol. 115, pp. 356–372, 2019, doi: 10.1016/j.eswa.2018.08.021.
- K. U. Syaliman, E. B. Nababan, and O. S. Sitompul, “Improving the accuracy of k-nearest neighbor using local mean based and distance weight,” J. Phys. Conf. Ser., vol. 978, no. 1, pp. 1–6, 2018, doi: 10.1088/1742-6596/978/1/012047.
- Y. Mitani and Y. Hamamoto, “A local mean-based nonparametric classifier,” Pattern Recognit. Lett., vol. 27, no. 10, pp. 1151–1159, 2006, doi: 10.1016/j.patrec.2005.12.016.
- S. A. Dudani, “The Distance-Weighted k-Nearest-Neighbor Rule,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. SMC-8, no. 4, pp. 311–313, 1978, doi: 10.1109/tsmc.1978.4309958.
- N. García-Pedrajas and D. Ortiz-Boyer, “Boosting k-nearest neighbor classifier by means of input space projection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10570–10582, 2009, doi: 10.1016/j.eswa.2009.02.065.
- A. Kataria and M. D. Singh, “A Review of Data Classification Using K-Nearest Neighbour Algorithm,” Int. J. Emerg. Technol. Adv. Eng., vol. 3, no. 6, pp. 354–360, 2013.
- Z. Lei, S. Wang, and D. Xu, “Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier,” Proc. - 2016 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2016, no. 3, pp. 1871–1876, 2017, doi: 10.1109/CISP-BMEI.2016.7853022.
References
Kementerian Kesehatan Republik Indonesia, “Peran Ditjen Kesmas Daman Pandemi COVID 19 2020-2021,” Jakarta, 2021.
A. Syauqi, “Jalan panjang covid19 (Sebuah Refleksi Dikala Wabah Merajalela Berdampak Pada Perekonomian),” JKUBS J. Chem. Inf. Model., vol. 1, no. 1, pp. 1–19, 2020.
N. Mamoon and G. Rasskin, “COVID-19,” 2019. [Online]. Available: https://www.covidvisualizer.com/
M. F. Asriansyah, “Pandemi Covid 19 dan Upaya Pencegahan,” 2022. https://www.djkn.kemenkeu.go.id/artikel/baca/15799/Pandemi-Covid-19-dan-Upaya-Pencegahan.html
E. Martínez Chamorro, A. Díez Tascón, L. Ibáñez Sanz, S. Ossaba Vélez, and S. Borruel Nacenta, “Radiologic diagnosis of patients with COVID-19,” Radiologia, vol. 63, no. 1, pp. 56–73, 2021, doi: 10.1016/j.rx.2020.11.001.
S. Ebrahimzadeh et al., “Thoracic imaging tests for the diagnosis of COVID-19,” 2022. doi: 10.1002/14651858.CD013639.pub5.
S. Kamthan, H. Singh, and T. Meitzler, “Hierarchical fuzzy deep learning for image classification,” Memories - Mater. Devices, Circuits Syst., vol. 2, no. June, p. 100016, 2022, doi: 10.1016/j.memori.2022.100016.
X. Zhang, H. Xiao, R. Gao, H. Zhang, and Y. Wang, “K-nearest neighbors rule combining prototype selection and local feature weighting for classification,” Knowledge-Based Syst., vol. 243, 2022, doi: 10.1016/j.knosys.2022.108451.
Y. Guo, S. Han, Y. Li, C. Zhang, and Y. Bai, “K-Nearest Neighbor combined with guided filter for hyperspectral image classification,” Procedia Comput. Sci., vol. 129, pp. 159–165, 2018, doi: 10.1016/j.procs.2018.03.066.
J. Wang, P. Neskovic, and L. N. Cooper, “Improving nearest neighbor rule with a simple adaptive distance measure,” Pattern Recognit. Lett., vol. 28, no. 2, pp. 207–213, 2007, doi: 10.1016/j.patrec.2006.07.002.
J. A. Romero-del-Castillo, M. Mendoza-Hurtado, D. Ortiz-Boyer, and N. García-Pedrajas, “Local-based k values for multi-label k-nearest neighbors rule,” Eng. Appl. Artif. Intell., vol. 116, no. June, p. 105487, 2022, doi: 10.1016/j.engappai.2022.105487.
S. Ougiaroglou and G. Evangelidis, “Fast and accurate k-nearest neighbor classification using prototype selection by clustering,” Proc. 2012 16th Panhellenic Conf. Informatics, PCI 2012, no. i, pp. 168–173, 2012, doi: 10.1109/PCi.2012.69.
L. Cai, Y. Song, T. Liu, and K. Zhang, “A Hybrid BERT Model That Incorporates Label Semantics via Adjustive Attention for Multi-Label Text Classification,” IEEE Access, vol. 8, pp. 152183–152192, 2020, doi: 10.1109/ACCESS.2020.3017382.
K. Li, H. Wang, W. Wang, F. Wang, and Z. Cui, “Improving artificial bee colony algorithm using modified nearest neighbor sequence,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 8807–8824, 2022, doi: 10.1016/j.jksuci.2021.10.012.
X. Wu et al., Top 10 algorithms in data mining, vol. 14, no. 1. 2008. doi: 10.1007/s10115-007-0114-2.
G. I. Okolo, S. Katsigiannis, and N. Ramzan, “IEViT: An enhanced vision transformer architecture for chest X-ray image classification,” Comput. Methods Programs Biomed., vol. 226, p. 107141, 2022, doi: 10.1016/j.cmpb.2022.107141.
S. Suyanto, P. E. Yunanto, T. Wahyuningrum, and S. Khomsah, “A multi-voter multi-commission nearest neighbor classifier,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 6292–6302, 2022, doi: 10.1016/j.jksuci.2022.01.018.
A. Islam, S. B. Belhaouari, A. U. Rehman, and H. Bensmail, “K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation,” Softw. Impacts, vol. 12, no. February, p. 100272, 2022, doi: 10.1016/j.simpa.2022.100272.
M. Kumar, N. K. Rath, A. Swain, and S. K. Rath, “Feature Selection and Classification of Microarray Data using MapReduce based ANOVA and K-Nearest Neighbor,” Procedia Comput. Sci., vol. 54, pp. 301–310, 2015, doi: 10.1016/j.procs.2015.06.035.
P. Nair et al., “A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method,” Int. J. Sci. Technol. Res., vol. 28, no. 3, pp. 221–226, 2020, doi: 10.1016/j.imu.2021.100825.
K. U. Syaliman, Yuliska, and N. F. Najwa, “Seleksi Fitur Menggunakan Pendekatan k-Nearest Neighbor,” J. Sist. Inf. dan Teknol. Jar., vol. 3, no. 1, pp. 8–13, 2022.
K. U. Syaliman, A. Labellapansa, and A. Yulianti, “Improving the Accuracy of Features Weighted k-Nearest Neighbor using Distance Weight,” no. ICoSET 2019, pp. 326–330, 2020, doi: 10.5220/0009390903260330.
P. A. Charde and S. D. Lokhande, “Classification Using K Nearest Neighbor for Brain Image Retrieval,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 8, pp. 760–765, 2013, doi: 10.25126/jtiik.2020722608.
L. Farokhah, “Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1129, 2020, doi: 10.25126/jtiik.2020722608.
Z. Pan, Y. Wang, and W. Ku, “A new k-harmonic nearest neighbor classifier based on the multi-local means,” Expert Syst. Appl., vol. 67, pp. 115–125, 2017, doi: 10.1016/j.eswa.2016.09.031.
J. Gou, H. Ma, W. Ou, S. Zeng, Y. Rao, and H. Yang, “A generalized mean distance-based k-nearest neighbor classifier,” Expert Syst. Appl., vol. 115, pp. 356–372, 2019, doi: 10.1016/j.eswa.2018.08.021.
K. U. Syaliman, E. B. Nababan, and O. S. Sitompul, “Improving the accuracy of k-nearest neighbor using local mean based and distance weight,” J. Phys. Conf. Ser., vol. 978, no. 1, pp. 1–6, 2018, doi: 10.1088/1742-6596/978/1/012047.
Y. Mitani and Y. Hamamoto, “A local mean-based nonparametric classifier,” Pattern Recognit. Lett., vol. 27, no. 10, pp. 1151–1159, 2006, doi: 10.1016/j.patrec.2005.12.016.
S. A. Dudani, “The Distance-Weighted k-Nearest-Neighbor Rule,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. SMC-8, no. 4, pp. 311–313, 1978, doi: 10.1109/tsmc.1978.4309958.
N. García-Pedrajas and D. Ortiz-Boyer, “Boosting k-nearest neighbor classifier by means of input space projection,” Expert Syst. Appl., vol. 36, no. 7, pp. 10570–10582, 2009, doi: 10.1016/j.eswa.2009.02.065.
A. Kataria and M. D. Singh, “A Review of Data Classification Using K-Nearest Neighbour Algorithm,” Int. J. Emerg. Technol. Adv. Eng., vol. 3, no. 6, pp. 354–360, 2013.
Z. Lei, S. Wang, and D. Xu, “Protein sub-cellular localization based on noise-intensity-weighted linear discriminant analysis and an improved k-nearest-neighbor classifier,” Proc. - 2016 9th Int. Congr. Image Signal Process. Biomed. Eng. Informatics, CISP-BMEI 2016, no. 3, pp. 1871–1876, 2017, doi: 10.1109/CISP-BMEI.2016.7853022.