ANALISIS KINERJA METODE K-NEAREST NEIGHBORS PADA DATA JOB FAIR
Abstract
This study examines the performance of the K-Nearest Neighbors (KNN) method for classifying applicant data in data-driven job fair activities. The challenges faced include managing large volumes of applicant data and identifying optimal parameters for classification. The study uses a dataset containing 20,000 entries from Kaggle, with attributes such as skills, work experience, and completed projects. After data preprocessing, experiments were conducted using the KNN method with the Euclidean Distance algorithm, within a range of k values from 3 to 9. The results show that k = 3 provides the best performance with an accuracy of 65.00%, precision of 63.78%, recall of 71.88%, and an F1-score of 67.64%. The conclusion indicates that smaller k values capture local patterns better, while larger k values tend to reduce performance. This research contributes to the development of data-driven recruitment systems by enhancing the efficiency and accuracy of applicant selection. Further studies are recommended to explore additional optimization methods and feature combinations to improve classification accuracy.
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[2] G. Abebe et al., “Matching Frictions and Distorted Beliefs: Evidence from a Job Fair Experiment,” Unpubl. Manuscr., no. 958, 2023.
[3] I. S. Lesmana, T. Tabroni, P. Harsono, and A. Bahits, “Inovasi dalam Proses Seleksi Karyawan Untuk Meningkatkan Kompetitivitas Divisi Marketing PT. Sumber Alfariya Trijaya Tbk. Branch Serang,” J. Manaj. STIE Muhammadiyah Palopo, vol. 9, no. 2, p. 364, 2023, doi: 10.35906/jurman.v9i2.1904.
[4] A. H. S. Hadi and S. Agustin, “PENENTUAN KALORI MAKANAN BERDASARKAN FITUR WARNA DAN BENTUK MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOUR) BERBASIS ANDROID,” vol. 4, no. 9, 2024.
[5] N. M. Putry, “Komparasi Algoritma Knn Dan Naïve Bayes Untuk Klasifikasi Diagnosis Penyakit Diabetes Mellitus,” EVOLUSI J. Sains dan Manaj., vol. 10, no. 1, 2022, doi: 10.31294/evolusi.v10i1.12514.
[6] A. V. Miceli-Barone, I. Konstas, F. Barez, and S. B. Cohen, “The Larger They Are , the Harder They Fail : Language Models do not Recognize Identifier Swaps in Python,” 2023.
[7] M. B. Nendya, E. Mulyanto Yuniarno, and S. Sumpeno, “Matahari Bhakti Nendya: Clustering Titik Fitur Model… Clustering Titik Fitur Model Wajah 3D Menggunakan K-Nearest Neighbour,” Juisi, vol. 07, no. 01, 2021.
[8] D. Zhao et al., “k-means clustering and kNN classification based on negative databases,” Appl. Soft Comput., vol. 110, p. 107732, 2021, doi: 10.1016/j.asoc.2021.107732.
[9] T. Khaerani Janubiya, S. Andryana, I. Diana Sholihati, U. Nasional Jl Sawo Manila No, and J. Selatan, “E-Recruitment Menggunakan Metode Simple Additive Weighting dan Algoritma K-Nearest Neighbor,” J. Sains Komput. Inform. (J-SAKTI, vol. 6, no. 1, pp. 161–171, 2022.
[10] Z. Zulfachmi, A. F. Syahputra, B. Indra Prasetyo, and A. Elsa Shafira, “Klasifikasi Tingkat Dehidrasi Berdasarkan Warna Urin Menggunakan Metode KNN,” J. Bangkit Indones., vol. 12, no. 1, pp. 43–48, 2023, doi: 10.52771/bangkitindonesia.v12i1.228.
[11] V. Diranisha, Agung Triayudi, and Ratih Titi Komalasari, “Implementation of K-Nearest Neighbour (KNN) Algorithm and Random Forest Algorithm in Identifying Diabetes,” SAGA J. Technol. Inf. Syst., vol. 2, no. 2, pp. 234–244, 2024, doi: 10.58905/saga.v2i2.253.
[12] S. A. Laga, “Perbandingan Metode K-NN dan SVM Berdasarkan Kinerja Pegawai,” J. Sist. Komput. dan Inform., vol. 4, no. 3, p. 420, 2023, doi: 10.30865/json.v4i3.5816.
[13] R. P. Nugraha and B. Soewito, “Detection of false positive accuracy in intrusion detection systems using knn (k nearest neighbor),” vol. 7, pp. 4321–4329, 2024.
[14] V. Malathi, M. P. Gopinath, M. Kumar, S. Bhushan, and S. Jayaprakash, “Enhancing the Paddy Disease Classification by Using Cross-Validation Strategy for Artificial Neural Network over Baseline Classifiers,” J. Sensors, vol. 2023, 2023, doi: 10.1155/2023/1576960.
[15] Y. Wiratama and R. Z. A. Aziz, “Perbandingan Prediksi Penyakit Stunting Balita Menggunakan Algoritma Support Vektor Machine dan Random Forest,” vol. 6, no. 2, 2024, doi: 10.47065/bits.v6i2.5543.
[16] A. Surya Firmansyah, A. Aziz, and M. Ahsan, “Optimasi K-Nearest Neighbor Menggunakan Algoritma Smote Untuk Mengatasi Imbalance Class Pada Klasifikasi Analisis Sentimen,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3341–3347, 2024, doi: 10.36040/jati.v7i6.7257.
[17] S. S. Priscila, S. S. Rajest, R. Regin, S. T, and Steffi. R, “CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY Classification of Satellite Photographs Utilizing the K-Nearest Neighbor Algorithm,” vol. 9, no. 2660–5309, pp. 55–71, 2023.
[18] M. Raihan, R. Allaam, and A. T. Wibowo, “Klasifikasi Genus Tanaman Anggrek Menggunakan Metode Convolutional Neural Network (CNN),” e-Prceeding Eng., vol. 8, no. 2, pp. 1–1153, 2021.
[19] Y. Li, J. Wang, and C. Wang, “Systematic Testing of the Data-Poisoning Robustness of KNN,” pp. 1207–1218, doi: 10.1145/3597926.3598129.
[20] M. A. Hadi and F. H. Fard, “Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews,” 2022.
Published
2024-12-31
How to Cite
PURNAMAWATI, Annida; WINNARTO, Monikka Nur; MAILASARI, Mely.
ANALISIS KINERJA METODE K-NEAREST NEIGHBORS PADA DATA JOB FAIR.
Jurnal Tekinkom (Teknik Informasi dan Komputer), [S.l.], v. 7, n. 2, p. 962-968, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1617>. Date accessed: 07 feb. 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1617.
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