Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa

Wijiyanto Wijiyanto; Afu Ichsan Pradana; Sopingi Sopingi; Vihi Atina

doi:10.33364/algoritma/v.21-1.1618

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Feb 19, 2024

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Mar 26, 2024

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May 20, 2024

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Kemampuan seorang mahasiswa untuk menyelesaikan perkuliahan dipengaruhi oleh berbagai faktor, termasuk aspek akademik dan non-akademik. Memahami faktor yang mempengaruhinya menjadi sangat penting untuk diketahui dalam rangka mengantisipasi dan mencegah kemungkinan kegagalan studinya. Faktor yang bersifat non-akademik ternyata juga berpengaruh besar terhadap keberhasilan mahasiswa terutama dari faktor keluarga, seperti status jenjang pendidikan yang diperoleh orang tua, status pekerjaan yang dimiliki orang tua dan penghasilan kedua orang tua. Untuk dapat memahami faktor tersebut diperlukan studi untuk memprediksi kinerja mahasiswa berdasarkan faktor yang berlatar belakang keluarga menggunakan model machine learning algoritma support vector machine, naÃ¯ve bayes, neural network dan decision tree. Data yang digunakan sebanyak 365 record dan 11 atribut dipisah untuk data train sebesar 70% dan untuk data test sebesar 30%, yang selanjutnya digunakan kfold cross validation untuk mengevaluasi hasil menggunakan parameter n_split=10 dan random_state=42. Pada parameter confusion matrix diperoleh nilai akurasi rata-rata (mean) untuk model support vector machine sebanyak 87,68%, naÃ¯ve bayes sebanyak 90,97%, neural network sebesar 87,95% dan decision tree sebesar 85,75%. Sedangkan hasil fold terbaik untuk SVM terletak pada fold ke-10 dengan akurasi 94,44%, untuk NB terletak pada fold ke-4 dengan nilai akurasi 97,29%, untuk NN terletak di fold ke-4 dengan nilai akurasi 94.59% serta untuk DT terletak di fold ke-5 dengan nilai akurasi 91,89%. Dengan demikian evaluasi menggunakan k-fold cross validation dapat digunakan dalam memprediksi kinerja mahasiswa berdasarkan atribut keluarga menggunakan fold ke-4 yang memiliki akurasi tertinggi sebesar 97,29% pada algoritma model naÃ¯ve bayes dalam rangka untuk lulus dengan tepat waktu.

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Akurasi Cross Validation Kfold Kinerja Mahasiswa Machine Learning Mean Accuracy Cross Validation Kfold Student Performance Machine Learning Mean

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[1]

W. Wijiyanto, A. I. Pradana, S. Sopingi, dan V. Atina, “Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa”, Jurnal Algoritma, vol. 21, no. 1, hlm. 239–248, Mei 2024.

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References

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J. M. Gorriz, F. Segovia, J. Ramirez, A. Ortiz, and J. Suckling, “Is K-fold cross validation the best model selection method for Machine Learning?,” Jan. 29, 2024, arXiv: arXiv:2401.16407. Accessed: Feb. 07, 2024. [Online]. Available: http://arxiv.org/abs/2401.16407
Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana, I. K. Nti, O. Nyarko-Boateng, and J. Aning, “Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation,” Int. J. Inf. Technol. Comput. Sci., vol. 13, no. 6, pp. 61”“71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.
D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 2019, pp. 542”“545. doi: 10.1016/B978-0-12-809633-8.20349-X.
F. Rios-Avila, CV_KFOLD: Stata module to implement k-fold cross-validation procedures. econpapers.repec.org, 2022. [Online]. Available: https://econpapers.repec.org/software/bocbocode/s458798.htm
A. Tholib, M. N. Fadli Hidayat, S. Yono, R. Wulanningrum, and E. Daniati, “Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms,” Int. J. Eng. Comput. Sci. Appl. IJECSA, vol. 2, no. 2, pp. 65”“72, Sep. 2023, doi: 10.30812/ijecsa.v2i2.3364.
Z. Lyu et al., “Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam,” Materials, vol. 15, no. 4, p. 1477, Feb. 2022, doi: 10.3390/ma15041477.
M. K. Mayangsari, I. Syarif, and A. Barakbah, “Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, Jul. 2023, doi: 10.22219/kinetik.v8i3.1740.
O. Oyedele, “Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment,” Res. Math., vol. 10, no. 1, p. 2201015, Dec. 2023, doi: 10.1080/27684830.2023.2201015.
J. White and S. D. Power, “k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation,” Sensors, vol. 23, no. 13, p. 6077, Jul. 2023, doi: 10.3390/s23136077.
R. Tuntun, K. Kusrini, and K. Kusnawi, “Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 4, p. 2111, Oct. 2022, doi: 10.30865/mib.v6i4.4681.
S. Linawati, S. Nurdiani, K. Handayani, and L. Latifah, “Prediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma Random Forest Dan C4.5,” J. Khatulistiwa Inform., vol. 8, no. 1, Jun. 2020, doi: 10.31294/jki.v8i1.7827.
D. Selvida and P. H. Putra, “Optimization of cross-validation testing on the decision tree and k-nearest neighbor in classifying election data,” vol. 7, no. 3, 2023.
O. Chamorro-Atalaya et al., “K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students,” Int. J. Online Biomed. Eng. IJOE, vol. 19, no. 11, Aug. 2023, doi: 10.3991/ijoe.v19i11.39887.
N. Nurainun, E. Haerani, F. Syafria, and L. Oktavia, “Penerapan Algoritma NaÃ¯ve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation,” J. Comput. Syst. Inform. JoSYC, vol. 4, no. 3, pp. 578”“586, May 2023, doi: 10.47065/josyc.v4i3.3414.
H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81”“86, Aug. 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi NaÃ¯ve Bayes Classifier (NBC) Pada Klasifikasi Status Gizi Balita Stunting Dengan Pengujian K-Fold Cross Validation,” J. Gaussian, vol. 11, no. 1, pp. 130”“139, May 2022, doi: 10.14710/j.gauss.v11i1.33991.
A. Desiani et al., “Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris,” Indones. J. Appl. Inform., vol. 7, no. 1, p. 12, Apr. 2023, doi: 10.20961/ijai.v7i1.61486.
Y. N. Fuadah, I. D. Ubaidullah, N. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. A. Pramuditho, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, p. 728, Jul. 2022, doi: 10.26760/elkomika.v10i3.728.
Agung Nugroho and Agit Amrullah, “Evaluasi Kinerja Algoritma K-Nn Menggunakan K-Fold Cross Validation Pada Data Debitur KSP Galih Manunggal,” J. Inform. Teknol. Dan Sains Jinteks, vol. 5, no. 2, pp. 294”“300, May 2023, doi: 10.51401/jinteks.v5i2.2506.
N. Alifiah, D. Kurniasari, A. Amanto, and W. Warsono, “Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation,” J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 16”“27, Apr. 2023, doi: 10.20473/jisebi.9.1.16-27.
X. Zhang and C.-A. Liu, “Model averaging prediction by K -fold cross-validation,” J. Econom., vol. 235, no. 1, pp. 280”“301, Jul. 2023, doi: 10.1016/j.jeconom.2022.04.007.
S. N. Cahyani and G. W. Saraswati, “Implementation Of Support Vector Machine Method In Classifying School Library Books With Combination Of TF-IDF And WORD2VEC,” J. Tek. Inform. Jutif, vol. 4, no. 6, pp. 1555”“1566, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1536.
E. K. Nurnawati, M. Sholeh, R. Y. Ariyana, and E. Almuntaha, “Comparison Of Decision Tree And NaÃ¯ve Bayes Algorithms In Classification Models To Determine Lecturer Performance Using K Fold Cross Validation,” vol. 14, no. 2, 2023.
S. A. Satriotomo, U. A. Ahmad, and P. Abadi, “Simulasi Prediksi Sintilasi Ionosfer Menggunakan Aplikasi Matlab Dengan Metode Neural Network,” vol. 2, no. 1, 2022.
S. Widodo, H. Brawijaya, and S. Samudi, “Stratified K-fold cross validation optimization on machine learning for prediction,” Sinkron, vol. 7, no. 4, pp. 2407”“2414, Oct. 2022, doi: 10.33395/sinkron.v7i4.11792.
M. Shiddiq, F. Candra, B. Anand, and M. F. Rabin, “Neural network with k-fold cross validation for oil palm fruit ripeness prediction,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 22, no. 1, p. 164, Oct. 2023, doi: 10.12928/telkomnika.v22i1.24845.
H. Hafid, “Penerapan K-Fold Cross Validation untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor pada Data Kasus Covid-19 di Indonesia,” 2023.

References

A. Salam and J. Zeniarja, “Classification of deep learning convolutional neural network feature extraction for student graduation prediction,” Indones. J. Electr. Eng. Comput. Sci., vol. 32, no. 1, p. 335, Oct. 2023, doi: 10.11591/ijeecs.v32.i1.pp335-341.

J. M. Gorriz, F. Segovia, J. Ramirez, A. Ortiz, and J. Suckling, “Is K-fold cross validation the best model selection method for Machine Learning?,” Jan. 29, 2024, arXiv: arXiv:2401.16407. Accessed: Feb. 07, 2024. [Online]. Available: http://arxiv.org/abs/2401.16407

Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana, I. K. Nti, O. Nyarko-Boateng, and J. Aning, “Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation,” Int. J. Inf. Technol. Comput. Sci., vol. 13, no. 6, pp. 61”“71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.

D. Berrar, “Cross-Validation,” in Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 2019, pp. 542”“545. doi: 10.1016/B978-0-12-809633-8.20349-X.

F. Rios-Avila, CV_KFOLD: Stata module to implement k-fold cross-validation procedures. econpapers.repec.org, 2022. [Online]. Available: https://econpapers.repec.org/software/bocbocode/s458798.htm

A. Tholib, M. N. Fadli Hidayat, S. Yono, R. Wulanningrum, and E. Daniati, “Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms,” Int. J. Eng. Comput. Sci. Appl. IJECSA, vol. 2, no. 2, pp. 65”“72, Sep. 2023, doi: 10.30812/ijecsa.v2i2.3364.

Z. Lyu et al., “Back-Propagation Neural Network Optimized by K-Fold Cross-Validation for Prediction of Torsional Strength of Reinforced Concrete Beam,” Materials, vol. 15, no. 4, p. 1477, Feb. 2022, doi: 10.3390/ma15041477.

M. K. Mayangsari, I. Syarif, and A. Barakbah, “Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification,” Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, Jul. 2023, doi: 10.22219/kinetik.v8i3.1740.

O. Oyedele, “Determining the optimal number of folds to use in a K-fold cross-validation: A neural network classification experiment,” Res. Math., vol. 10, no. 1, p. 2201015, Dec. 2023, doi: 10.1080/27684830.2023.2201015.

J. White and S. D. Power, “k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation,” Sensors, vol. 23, no. 13, p. 6077, Jul. 2023, doi: 10.3390/s23136077.

R. Tuntun, K. Kusrini, and K. Kusnawi, “Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 4, p. 2111, Oct. 2022, doi: 10.30865/mib.v6i4.4681.

S. Linawati, S. Nurdiani, K. Handayani, and L. Latifah, “Prediksi Prestasi Akademik Mahasiswa Menggunakan Algoritma Random Forest Dan C4.5,” J. Khatulistiwa Inform., vol. 8, no. 1, Jun. 2020, doi: 10.31294/jki.v8i1.7827.

D. Selvida and P. H. Putra, “Optimization of cross-validation testing on the decision tree and k-nearest neighbor in classifying election data,” vol. 7, no. 3, 2023.

O. Chamorro-Atalaya et al., “K-Fold Cross-Validation through Identification of the Opinion Classification Algorithm for the Satisfaction of University Students,” Int. J. Online Biomed. Eng. IJOE, vol. 19, no. 11, Aug. 2023, doi: 10.3991/ijoe.v19i11.39887.

N. Nurainun, E. Haerani, F. Syafria, and L. Oktavia, “Penerapan Algoritma NaÃ¯ve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation,” J. Comput. Syst. Inform. JoSYC, vol. 4, no. 3, pp. 578”“586, May 2023, doi: 10.47065/josyc.v4i3.3414.

H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81”“86, Aug. 2020, doi: 10.33096/ilkom.v12i2.507.81-86.

R. R. R. Arisandi, B. Warsito, and A. R. Hakim, “Aplikasi NaÃ¯ve Bayes Classifier (NBC) Pada Klasifikasi Status Gizi Balita Stunting Dengan Pengujian K-Fold Cross Validation,” J. Gaussian, vol. 11, no. 1, pp. 130”“139, May 2022, doi: 10.14710/j.gauss.v11i1.33991.

A. Desiani et al., “Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris,” Indones. J. Appl. Inform., vol. 7, no. 1, p. 12, Apr. 2023, doi: 10.20961/ijai.v7i1.61486.

Y. N. Fuadah, I. D. Ubaidullah, N. Ibrahim, F. F. Taliningsing, N. K. Sy, and M. A. Pramuditho, “Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 3, p. 728, Jul. 2022, doi: 10.26760/elkomika.v10i3.728.

Agung Nugroho and Agit Amrullah, “Evaluasi Kinerja Algoritma K-Nn Menggunakan K-Fold Cross Validation Pada Data Debitur KSP Galih Manunggal,” J. Inform. Teknol. Dan Sains Jinteks, vol. 5, no. 2, pp. 294”“300, May 2023, doi: 10.51401/jinteks.v5i2.2506.

N. Alifiah, D. Kurniasari, A. Amanto, and W. Warsono, “Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation,” J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 16”“27, Apr. 2023, doi: 10.20473/jisebi.9.1.16-27.

X. Zhang and C.-A. Liu, “Model averaging prediction by K -fold cross-validation,” J. Econom., vol. 235, no. 1, pp. 280”“301, Jul. 2023, doi: 10.1016/j.jeconom.2022.04.007.

S. N. Cahyani and G. W. Saraswati, “Implementation Of Support Vector Machine Method In Classifying School Library Books With Combination Of TF-IDF And WORD2VEC,” J. Tek. Inform. Jutif, vol. 4, no. 6, pp. 1555”“1566, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1536.

E. K. Nurnawati, M. Sholeh, R. Y. Ariyana, and E. Almuntaha, “Comparison Of Decision Tree And NaÃ¯ve Bayes Algorithms In Classification Models To Determine Lecturer Performance Using K Fold Cross Validation,” vol. 14, no. 2, 2023.

S. A. Satriotomo, U. A. Ahmad, and P. Abadi, “Simulasi Prediksi Sintilasi Ionosfer Menggunakan Aplikasi Matlab Dengan Metode Neural Network,” vol. 2, no. 1, 2022.

S. Widodo, H. Brawijaya, and S. Samudi, “Stratified K-fold cross validation optimization on machine learning for prediction,” Sinkron, vol. 7, no. 4, pp. 2407”“2414, Oct. 2022, doi: 10.33395/sinkron.v7i4.11792.

M. Shiddiq, F. Candra, B. Anand, and M. F. Rabin, “Neural network with k-fold cross validation for oil palm fruit ripeness prediction,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 22, no. 1, p. 164, Oct. 2023, doi: 10.12928/telkomnika.v22i1.24845.

H. Hafid, “Penerapan K-Fold Cross Validation untuk Menganalisis Kinerja Algoritma K-Nearest Neighbor pada Data Kasus Covid-19 di Indonesia,” 2023.

Teknik K-Fold Cross Validation untuk Mengevaluasi Kinerja Mahasiswa

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