PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO

Authors

  • Yessica Fara Desvia Politeknik Jatiluhur
  • Febryawan Yuda Pratama Politeknik Jatiluhur
  • Ganda Wijaya Universitas Nusa Mandiri

DOI:

https://doi.org/10.33480/inti.v20i1.7076

Keywords:

Classification, Feature Selection, Graduation Prediction, K-NN, PSO

Abstract

Predicting student graduation is a crucial aspect in supporting academic planning and ensuring timely completion of studies. However, no prior research has specifically applied the integration of K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) for graduation prediction using student data. This study aims to evaluate the effectiveness of combining KNN and PSO in improving classification accuracy. The KNN algorithm is used for classification, while PSO is implemented as a feature selection technique to identify the most relevant attributes. A dataset of 750 student records was processed through data preprocessing and attribute weighting using PSO, followed by model training and evaluation with 10-fold cross-validation. The evaluation results show that the KNN+PSO model improves accuracy from 80.91% to 84.31%, along with increases in precision and recall. These findings indicate that PSO enhances the performance of KNN, particularly in identifying students likely to graduate on time

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References

Hamid, A., & Ridwansyah. (2024). Optimizing Heart Failure Detection : A Comparison between Naive Bayes and Particle Swarm Optimization. Paradigma, 26(1), 30–36. https://doi.org/https://doi.org/10.31294/p.v26i1.3284

Iqbal, M., Yumi Novita Dewi, Lisnawanty, Maisyaroh, & Suhardjono. (2025). Optimalisasi Prediksi Dalam Kelulusan Berbasis Deep Learning : Perbandingan Kinerja Multi- Layer Perceptron dan Deep Neural Network. Infotek : Jurnal Informatika Dan Teknolog, 8(2), 630–641. https://doi.org/DOI : 10.29408/jit.v8i2.30756

Kahfi, A. H., Prihatin, T., Yudhistira, Sudradjat, A., & Wijaya, G. (2024). THE RIGHT STEPS TOWARDS GRADUATION: NB-PSO SMART COMBINATION FOR STUDENT GRADUATION PREDICTION. Jurnal Teknik Informatika (Jutif), 5(2), 607–614. https://doi.org/https://doi.org/10.52436/1.jutif.2024.5.2.1889

Karmagatri, M., Kurnianingrum, D., Suciana, M. R., & Utami, S. A. (2023). Predicting Factors Related to Student Performance Using Decision Tree Algorithm. International Conference on Cybernetics and Intelligent System (ICORIS). https://doi.org/10.1109/ICORIS60118.2023.10352269

Kurniawati, G., & Maulidevi, N. U. (2022). Multivariate Sequential Modelling for Student Performance and Graduation Prediction. International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). https://doi.org/10.1109/ICITACEE55701.2022.9923971

Liao, X. (2023). Research and enlightenment on the Graduation Rate of American Universities Based on Panel Data. International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS). https://doi.org/10.1109/ICMSS56787.2023.10118079

Lin, K. (2023). Research on Graduation Destination Prediction Algorithm Based on Students’ Learning Behavior Data. Asian Conference on Artificial Intelligence Technology (ACAIT). https://doi.org/10.1109/ACAIT60137.2023.10528473

Nurdin, H., Sartini, Sumarna, Maulana, Y. I., & Riyanto, V. (2023). Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2353–2362. https://doi.org/10.33395/sinkron.v8i4.12973

Ong, D. P., & Pedrasa, J. R. I. (2021). Student Risk Assessment: Predicting Undergraduate Student Graduation Probability Using Logistic Regression, SVM, and ANN. TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON). https://doi.org/10.1109/TENCON54134.2021.9707322

Pangesti, W. E., Ariyati, I., Priyono, Sugiono, & Suryadithia, R. (2024). Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 276–284. https://doi.org/https://doi.org/10.33395/sinkron.v9i1.13161 e-ISSN

Rawatlal, R., Chetty, M., & Naicker, A. K. (2022). Latent Factors for Consistently Predicting Student Success. World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC). https://doi.org/10.1109/WEEF-GEDC54384.2022.9996209

Ridwansyah, Andharsaputri, R. L., Yudhistira, Irmawati Carolina, & Suharjanti. (2025). K-Nearest Neighbors Optimization using Particle Swarm Optimization in Selection Digital Payments. Jurnal Teknologi Informasi Dan Terapan (J-TIT), 12(1), 1–8. https://doi.org/https://doi.org/10.25047/jtit.v12i1.5911

Ridwansyah, Iqbal, M., Destiana, H., Sugiono, & Hamid, A. (2024). Data Mining Berbasis Machine Learning Untuk Analitik Prediktif Dalam Kelulusan. SemanTIK, 10(2), 1–10. https://doi.org/https://doi.org/10.55679/semantik.v10i2.67

Rukiastiandari, S., Luthfia Rohimah, Aprillia, Chodidjah, & Fara Mutia. (2025). Model Hibrida K-Nearest Neighbors Berbasis Genethic Algorithm untuk Prediksi Penyakit Ginjal Kronis. Infotek : Jurnal Informatika Dan Teknologi, 8(1), 44–55. https://doi.org/10.29408/jit.v8i1.27918

Rukiastiandari, S., Rohimah, L., Aprillia, A., & Mutia, F. (2024). Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm. Paradigma - Jurnal Komputer Dan Informatika, 26(1), 1–6. https://doi.org/10.31294/p.v26i1.3165

Sumarna, Nawawi, I., Suhardjono, Hari Sugiarto, & Yuliandari, D. (2024). MENINGKATKAN AKURASI PREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN METODE ALGORITMA GENETIKA. Jurnal Informatika, Manajemen Dan Komputer, 16(2). https://doi.org/http://dx.doi.org/10.36723/juri.v16i2.706

Widodo, S., Brawijaya, H., & Samudi, S. (2024). Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO. Paradigma - Jurnal Komputer Dan Informatika, 26(1), 58–64. https://doi.org/10.31294/p.v26i1.3282

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Published

2025-09-02

How to Cite

Desvia, Y. F., Pratama, F. Y., & Wijaya, G. (2025). PENINGKATAN AKURASI KNN DALAM PREDIKSI KELULUSAN MAHASISWA MELALUI OPTIMASI PARAMETER PSO. INTI Nusa Mandiri, 20(1), 126–133. https://doi.org/10.33480/inti.v20i1.7076