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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[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.
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https://doi.org/10.1109/ICSIPA52582.2021.9576774.
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Neural Network,†eProceedings of Engineering, vol. 9, no. 6, pp. 3200–3206, 2022.
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Network dengan Arsitektur VGG-16,†ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik
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https://doi.org/10.14569/IJACSA.2022.01310109.
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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.
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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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