OPTIMASI SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR RANDOM FOREST DAN HYPERPARAMETER GRIDSEARCHCV UNTUK KLASIFIKASI RAISIN DATASET

Gregian Bayu Anugrah; Toni Arifin

doi:10.46576/djtechno.v6i2.7008

OPTIMASI SUPPORT VECTOR MACHINE MENGGUNAKAN SELEKSI FITUR RANDOM FOREST DAN HYPERPARAMETER GRIDSEARCHCV UNTUK KLASIFIKASI RAISIN DATASET

Gregian Bayu Anugrah, Toni Arifin

Abstract

Raisin (kismis) merupakan buah kering yang memiliki nilai kandungan gizi yang tinggi dan kaya akan serat. Seiring dengan meningkatnya produksi global, kebutuhan akan sistem klasifikasi otomatis yang akurat menjadi semakin krusial untuk efisiensi industri. Proses klasifikasi manual dinilai tidak efisien, memakan waktu, serta rawan kesalahan manusia, sehingga diperlukan pendekatan berbasis kecerdasan buatan. Penelitian ini bertujuan untuk mengoptimalkan model klasifikasi Support Vector Machine (SVM) dengan penerapan seleksi fitur menggunakan algoritma Random Forest dan optimasi hyperparameter menggunakan GridSearchCV. Dataset yang digunakan adalah Raisin Dataset dari UCI Machine Learning Repository yang terdiri dari 900 sampel dua jenis raisin, yaitu Kecimen dan Besni, masing-masing memiliki tujuh fitur numerik morfologis. Hasil evaluasi menunjukkan bahwa model SVM yang telah dioptimasi mampu mencapai akurasi sebesar 90.00% dan nilai AUC 0,9388. Temuan ini menunjukkan bahwa kombinasi seleksi fitur dan optimasi hyperparameter dapat meningkatkan kinerja model klasifikasi. Penelitian ini juga memiliki perbedaan dari studi sebelumnya dalam hal objek, waktu pelaksanaan, literatur, pendekatan teori, serta hasil akhir yang diperoleh. Pendekatan ini memperkuat pemanfaatan machine learning dalam sektor agroindustri dan membuka peluang pengembangan sistem klasifikasi pangan yang lebih efisien dan presisi.

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References

İ. Cinar, M. KOKLU, and Ş. TAŞDEMİR, “Kuru Üzüm Tanelerinin Makine Görüşü ve Yapay Zeka Yöntemleri Kullanılarak Sınıflandırılması,” Gazi J. Eng. Sci., vol. 6, no. 3, pp. 200–209, 2020, doi: 10.30855/gmbd.2020.03.03.

A. Bisri and M. Man, “Machine Learning Algorithms Based on Sampling Techniques for Raisin Grains Classification,” Int. J. Informatics Vis., vol. 7, no. 1, pp. 7–14, 2023, doi: 10.30630/joiv.7.1.970.

Y. Zhang, Y. Yang, C. Ma, and L. Jiang, “Identification of multiple raisins by feature fusion combined with NIR spectroscopy,” PLoS One, vol. 17, no. 7 July, pp. 1–13, 2022, doi: 10.1371/journal.pone.0268979.

H. Incekara, “Classification of Raisin Grains Using Different Artificial Neural Network Methods,” Intell. Methods Eng. Sci., Sep. 2024, doi: 10.58190/imiens.2024.104.

E. K. Yılmaz, T. Oğuz, and K. Adem, “A CNN-Based Hybrid Approach to Classification of Raisin Grains,” 1st Int. Conf. Front. Acad., no. February, pp. 18–21, 2023, [Online]. Available: https://as-proceeding.com/index.php/icfar/article/view/157/108

A. Tulchhia and M. Rathore, “RANDOM FOREST MODEL FOR CLASSIFICATION OF RAISINS USING,” OORJA, vol. 19, no. 1, pp. 79–86, 2021.

A. Polowczyk and A. Polowczyk, “The Effectiveness of PCA in KNN, Gaussian Naive Bayes Classifier and SVM for Raisin Dataset,” CEUR Workshop Proc., vol. 3695, pp. 9–16, 2023.

M. Dirik, “Improving Raisin Grains Classification with a Hybrid PSO-NN Approach,” All Sci. Proc., 2023, [Online]. Available: http://as-proceeding.com/:Konya,Turkeyhttps://as-proceeding.com/index.php/iccar

S. J. Sammut et al., “Multi-omic machine learning predictor of breast cancer therapy response,” Nature, vol. 601, no. 7894, pp. 623–629, 2022, doi: 10.1038/s41586-021-04278-5.

M. N. Raihen and S. Akter, “Prediction modeling using deep learning for the classification of grape-type dried fruits,” Int. J. Math. Comput. Eng., vol. 2, no. 1, pp. 1–12, 2024, doi: 10.2478/ijmce-2024-0001.

Q. H. Su and K. N. Chiang, “Predicting Wafer-Level Package Reliability Life Using Mixed Supervised and Unsupervised Machine Learning Algorithms,” Materials (Basel)., vol. 15, no. 11, 2022, doi: 10.3390/ma15113897.

T. Arifin, I. W. P. Agung, E. Junianto, R. Rachman, I. R. Wibowo, and D. D. Agustin, “Breast cancer identification using machine learning and hyperparameter optimization,” Indones. J. Electr. Eng. Comput. Sci., vol. 36, no. 3, pp. 1620–1630, 2024, doi: 10.11591/ijeecs.v36.i3.pp1620-1630.

W. U. Dewi, N. Khoirin, and U. Mustofa, “IMPLEMENTATION OF DECISION TREE AND SUPPORT VECTOR MACHINE ON RAISIN SEED CLASSIFICATION,” J. Progr. Stud. Pendidik. Mat., vol. 12, no. 1, pp. 1309–1321, 2023.

A. Mishra and A. Dasgupta, “Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints,” Forecasting, vol. 4, no. 4, pp. 787–797, 2022, doi: 10.3390/forecast4040043.

G. B. Anugrah, R. N. Z. Shafa, and A. Kurniawan, “Perbandingan Algoritma Machine Learning Untuk Klasifikasi Dry Bean Dataset,” J. Responsif Ris. Sains dan Inform., vol. 5, no. 2, pp. 407–415, 2023, doi: 10.51977/jti.v5i2.1292.

Q. Du and J. Zhai, “Application of artificial intelligence Sensors based on random forest algorithm in financial recognition models,” Meas. Sensors, vol. 33, no. June, p. 101245, 2024, doi: 10.1016/j.measen.2024.101245.

M. R. Reddy, “Implementation of SVM machine learning Algorithm to predict lung And Breast Cancer,” Turkish J. Comput. Math. Educ., vol. 12, no. 12, pp. 3050–3060, 2021.

J. Wu and C. Hicks, “Breast cancer type classification using machine learning,” J. Pers. Med., vol. 11, no. 2, pp. 1–12, 2021, doi: 10.3390/jpm11020061.

A. Selvan M, “AN INTRUSION DETECTION SYSTEM MODEL FOR DETECTING KNOWN AND INNOVATIVE CYBER ATTACKS,” J. Sci. Technol. Res., vol. 1, no. 2, pp. 151–157, 2021.

E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, “Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis,” Informatics, vol. 8, no. 4, pp. 1–21, 2021, doi: 10.3390/informatics8040079.

A. M. Rahmani et al., “Machine learning (Ml) in medicine: Review, applications, and challenges,” Mathematics, vol. 9, no. 22, pp. 1–52, 2021, doi: 10.3390/math9222970.

M. Daviran, M. Shamekhi, R. Ghezelbash, and A. Maghsoudi, “Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm,” Int. J. Environ. Sci. Technol., vol. 20, no. 1, pp. 259–276, 2023, doi: 10.1007/s13762-022-04491-3.

T. Yan, S. L. Shen, A. Zhou, and X. Chen, “Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm,” J. Rock Mech. Geotech. Eng., vol. 14, no. 4, pp. 1292–1303, 2022, doi: 10.1016/j.jrmge.2022.03.002.

T. Alshammari, “Using Artificial Neural Networks with GridSearchCV for Predicting Indoor Temperature in a Smart Home,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13437–13443, 2024, doi: 10.48084/etasr.7008.

Y. Ramdhani, D. F. Apra, and D. P. Alamsyah, “Feature selection optimization based on genetic algorithm for support vector classification varieties of raisin,” Indones. J. Electr. Eng. Comput. Sci., vol. 30, no. 1, pp. 192–199, 2023, doi: 10.11591/ijeecs.v30.i1.pp192-199.

A. Sofya et al., “Metode Survey : Explanatory Survey dan Cross Sectional dalam Penelitian Kuantitatif Survey Methods : Explanatory Survey and Cross Sectional in Quantitative Research,” J. Pendidikan, Ilmu Sos. dan Pengabdi. Kpd. Masy., vol. 4, no. 3, pp. 1695–1708, 2024.

M. Waruwu, S. Natijatul, P. R. Utami, and E. Yanti, “Metode Penelitian Kuantitatif : Konsep , Jenis , Tahapan dan Kelebihan,” J. Ilm. Profesi Pendidik., vol. 10, pp. 917–932, 2025.

R. Resmiati and T. Arifin, “Klasifikasi Pasien Kanker Payudara Menggunakan Metode Support Vector Machine dengan Backward Elimination,” Sistemasi, vol. 10, no. 2, p. 381, 2021, doi: 10.32520/stmsi.v10i2.1238.

DOI: https://doi.org/10.46576/djtechno.v6i2.7008