Accuracy of K-Nearest Neighbors Algorithm Classification For Archiving Research Publications
DOI:
https://doi.org/10.30812/matrik.v23i3.3915Keywords:
Accuracy, Algorithm Classification, K-Nearest NeighborsAbstract
The Archives and Research Publication Information System plays an important role in managing academic research and scientific publications efficiently. With the increasing volume of research and publications carried out each year by university researchers, the Research Archives and Publications Information System is essential for organizing and processing these materials. However, managing large amounts of data poses challenges, including the need to accurately classify a researcher's field of study. To overcome these challenges, this research focuses on implementing the K-Nearest Neighbors classification algorithm in the Archives and Research Publications Information System application. This research aims to improve the accuracy of classification systems and facilitate better decision-making in the management of academic research. This research method is systematic involving data acquisition, pre-processing, algorithm implementation, and evaluation. The results of this research show that integrating Chi-Square feature selection significantly improves K-Nearest Neighbors performance, achieving 86% precision, 84.3% recall, 89.2% F1 Score, and 93.3% accuracy. This research contributes to increasing the efficiency of the Archives and Research Publication Information System in managing research and academic publications.
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References
[1] A. Wibowo, C. Adhi Hartanto, and P. Wisnu Wirawan, “Android skin cancer detection and classification based on
MobileNet v2 model,†International Journal of Advances in Intelligent Informatics, vol. 6, no. 2, pp. 135–148, Jul. 2020,
https://doi.org/10.26555/ijain.v6i2.492.
[2] J. Setiabudi, I. G. A. A. E. Indira, and N. M. D. Puspawati, “Profil Pra Kanker dan Kanker Kulit di RSUP Sanglah Periode
2015-2018,†E-Jurnal Medika Udayana, vol. 10, no. 3, pp. 83–88, Mar. 2021, https://doi.org/10.24843/MU.2021.V10.i3.P13.
[3] M. Miftahurrohmah, S. Fatimah, and I. Subarkah, “Metode Al-Miftah Lil ’Ulum sebagai Upaya Meningkatkan Motivasi
dan Kemampuan Siswa dalam Membaca Kitab Kuning di SMP Ar-Raudhah,†Social, Humanities, and Educational Studies
(SHES): Conference Series, vol. 6, no. 1, pp. 169–176, Feb. 2023, https://doi.org/10.20961/shes.v6i1.71074.
[4] A. Hayati, “The Use of Digital Guessing Game to Improve Students Speaking Ability,†Journal of English Education and
Teaching, vol. 4, no. 1, pp. 115–126, Mar. 2020, https://doi.org/10.33369/jeet.4.1.115-126.
[5] M. Z. Katili, L. N. Amali, and M. S. Tuloli, “Implementasi Metode AHP-TOPSIS dalam Sistem Pendukung
Rekomendasi Mahasiswa Berprestasi,†Jambura Journal of Informatics, vol. 3, no. 1, pp. 1–10, Apr. 2021,
https://doi.org/10.37905/jji.v3i1.10246.
[6] W. Widyaningsih, I. I. Tritoasmoro, and N. C. Kumalasari, “Perbandingan Klasifikasi Kematangan Buah Kopi Menggunakan
Metode Fuzzy Logic Dan K-Nearest Neighbor Dengan Ekstraksi Ciri Gray Level Co-Occurrrence Matrix,†eProceedings of
Engineering, vol. 7, no. 2, pp. 40–60, Aug. 2020.
[7] P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network
(CNN) pada Ekspresi Manusia,†ALGOR, vol. 2, no. 1, pp. 12–20, Nov. 2020.
[8] E. Goodarzi, M. Sohrabivafa, H. A. Adineh, L. Moayed, and Z. Khazaei, “Geographical distribution global incidence and
mortality of lung cancer and its relationship with the Human Development Index (HDI); an ecology study in 2018,†World
Cancer Research Journal, vol. 6, no. 1, pp. 1–11, Jul. 2020, https://doi.org/10.32113/wcrj 20197 1354.
[9] Y. Jusman, M. K. Anam, S. Puspita, E. Saleh, S. N. A. M. Kanafiah, and R. I. Tamarena, “Comparison of Dental
Caries Level Images Classification Performance using KNN and SVM Methods,†in 2021 IEEE International Conference
on Signal and Image Processing Applications (ICSIPA). Kuala Terengganu, Malaysia: IEEE, Sep. 2021, pp. 167–172,
https://doi.org/10.1109/ICSIPA52582.2021.9576774.
[10] N. Alyyu, Y. N. Fuadah, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit Ganas Dan Jinak Menggunakan Metode Convolutional
Neural Network,†eProceedings of Engineering, vol. 9, no. 6, pp. 3200–3206, 2022.
[11] S. Ulya, M. A. Soeleman, and F. Budiman, “Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan
Pembangunan Desa,†Techno.Com, vol. 20, no. 1, pp. 83–96, Feb. 2021, https://doi.org/10.33633/tc.v20i1.4215.
[12] R. Agustina, R. Magdalena, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural
Network dengan Arsitektur VGG-16,†ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik
Elektronika, vol. 10, no. 2, pp. 446–457, Apr. 2022, https://doi.org/10.26760/elkomika.v10i2.446.
[13] V. Radhika and B. S. Chandana, “Skin Melanoma Classification from Dermoscopy Images using ANU-Net Technique,â€
International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, pp. 928–938, 2022,
https://doi.org/10.14569/IJACSA.2022.01310109.
[14] D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion
matrix,†Computers & Operations Research, vol. 152, no. 1, pp. 1–12, Apr. 2023, https://doi.org/10.1016/j.cor.2022.106131.
[15] D. Boi, B. Runje, D. Lisjak, and D. Kolar, “Metrics Related to Confusion Matrix as Tools for Conformity Assessment
Decisions,†Applied Sciences, vol. 13, no. 14, pp. 8187–8197, Jul. 2023, https://doi.org/10.3390/app13148187.
[16] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and
Prospects,†IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999–7019, Dec. 2022,
https://doi.org/10.1109/TNNLS.2021.3084827.
MobileNet v2 model,†International Journal of Advances in Intelligent Informatics, vol. 6, no. 2, pp. 135–148, Jul. 2020,
https://doi.org/10.26555/ijain.v6i2.492.
[2] J. Setiabudi, I. G. A. A. E. Indira, and N. M. D. Puspawati, “Profil Pra Kanker dan Kanker Kulit di RSUP Sanglah Periode
2015-2018,†E-Jurnal Medika Udayana, vol. 10, no. 3, pp. 83–88, Mar. 2021, https://doi.org/10.24843/MU.2021.V10.i3.P13.
[3] M. Miftahurrohmah, S. Fatimah, and I. Subarkah, “Metode Al-Miftah Lil ’Ulum sebagai Upaya Meningkatkan Motivasi
dan Kemampuan Siswa dalam Membaca Kitab Kuning di SMP Ar-Raudhah,†Social, Humanities, and Educational Studies
(SHES): Conference Series, vol. 6, no. 1, pp. 169–176, Feb. 2023, https://doi.org/10.20961/shes.v6i1.71074.
[4] A. Hayati, “The Use of Digital Guessing Game to Improve Students Speaking Ability,†Journal of English Education and
Teaching, vol. 4, no. 1, pp. 115–126, Mar. 2020, https://doi.org/10.33369/jeet.4.1.115-126.
[5] M. Z. Katili, L. N. Amali, and M. S. Tuloli, “Implementasi Metode AHP-TOPSIS dalam Sistem Pendukung
Rekomendasi Mahasiswa Berprestasi,†Jambura Journal of Informatics, vol. 3, no. 1, pp. 1–10, Apr. 2021,
https://doi.org/10.37905/jji.v3i1.10246.
[6] W. Widyaningsih, I. I. Tritoasmoro, and N. C. Kumalasari, “Perbandingan Klasifikasi Kematangan Buah Kopi Menggunakan
Metode Fuzzy Logic Dan K-Nearest Neighbor Dengan Ekstraksi Ciri Gray Level Co-Occurrrence Matrix,†eProceedings of
Engineering, vol. 7, no. 2, pp. 40–60, Aug. 2020.
[7] P. A. Nugroho, I. Fenriana, and R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network
(CNN) pada Ekspresi Manusia,†ALGOR, vol. 2, no. 1, pp. 12–20, Nov. 2020.
[8] E. Goodarzi, M. Sohrabivafa, H. A. Adineh, L. Moayed, and Z. Khazaei, “Geographical distribution global incidence and
mortality of lung cancer and its relationship with the Human Development Index (HDI); an ecology study in 2018,†World
Cancer Research Journal, vol. 6, no. 1, pp. 1–11, Jul. 2020, https://doi.org/10.32113/wcrj 20197 1354.
[9] Y. Jusman, M. K. Anam, S. Puspita, E. Saleh, S. N. A. M. Kanafiah, and R. I. Tamarena, “Comparison of Dental
Caries Level Images Classification Performance using KNN and SVM Methods,†in 2021 IEEE International Conference
on Signal and Image Processing Applications (ICSIPA). Kuala Terengganu, Malaysia: IEEE, Sep. 2021, pp. 167–172,
https://doi.org/10.1109/ICSIPA52582.2021.9576774.
[10] N. Alyyu, Y. N. Fuadah, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit Ganas Dan Jinak Menggunakan Metode Convolutional
Neural Network,†eProceedings of Engineering, vol. 9, no. 6, pp. 3200–3206, 2022.
[11] S. Ulya, M. A. Soeleman, and F. Budiman, “Optimasi Parameter K Pada Algoritma K-NN Untuk Klasifikasi Prioritas Bantuan
Pembangunan Desa,†Techno.Com, vol. 20, no. 1, pp. 83–96, Feb. 2021, https://doi.org/10.33633/tc.v20i1.4215.
[12] R. Agustina, R. Magdalena, and N. K. C. Pratiwi, “Klasifikasi Kanker Kulit menggunakan Metode Convolutional Neural
Network dengan Arsitektur VGG-16,†ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik
Elektronika, vol. 10, no. 2, pp. 446–457, Apr. 2022, https://doi.org/10.26760/elkomika.v10i2.446.
[13] V. Radhika and B. S. Chandana, “Skin Melanoma Classification from Dermoscopy Images using ANU-Net Technique,â€
International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, pp. 928–938, 2022,
https://doi.org/10.14569/IJACSA.2022.01310109.
[14] D. Valero-Carreras, J. Alcaraz, and M. Landete, “Comparing two SVM models through different metrics based on the confusion
matrix,†Computers & Operations Research, vol. 152, no. 1, pp. 1–12, Apr. 2023, https://doi.org/10.1016/j.cor.2022.106131.
[15] D. Boi, B. Runje, D. Lisjak, and D. Kolar, “Metrics Related to Confusion Matrix as Tools for Conformity Assessment
Decisions,†Applied Sciences, vol. 13, no. 14, pp. 8187–8197, Jul. 2023, https://doi.org/10.3390/app13148187.
[16] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and
Prospects,†IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 6999–7019, Dec. 2022,
https://doi.org/10.1109/TNNLS.2021.3084827.
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2024-07-03
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[1]
M. N. Gunawan, T. Farhanah, S. U. Masruroh, A. M. Jundulloh, N. Z. Raushanfikar, and R. N. S. Amriza, “Accuracy of K-Nearest Neighbors Algorithm Classification For Archiving Research Publications”, MATRIK, vol. 23, no. 3, pp. 593–602, Jul. 2024, doi: 10.30812/matrik.v23i3.3915.
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