Implementasi Extra Trees Classifier dengan Optimasi Grid Search CV pada Prediksi Tingkat Adaptasi

LISTYA NUR AINA; VINNA RAHMAYANTI SETYANING NASTITI; CHRISTIAN SRI KUSUMA ADITYA

doi:10.26760/mindjournal.v9i1.78-88

Implementasi Extra Trees Classifier dengan Optimasi Grid Search CV pada Prediksi Tingkat Adaptasi

LISTYA NUR AINA, VINNA RAHMAYANTI SETYANING NASTITI, CHRISTIAN SRI KUSUMA ADITYA

Sari

Abstrak

Teknologi terus maju, terutama dalam komunikasi, pendidikan, dan informasi. Pendidikan online semakin diminati di banyak lembaga pendidikan, mendorong perlunya pemahaman sejauh mana peserta didik dapat beradaptasi dengan lingkungan online. Memprediksi tingkat adaptasi peserta didik menjadi penting untuk meningkatkan efektivitas dan kualitas pengalaman belajar. Dalam penelitian ini, menggunakan dataset dari Kaggle, metode Extra Trees Classifier dioptimalkan dengan Hyperparameter Tuning Grid Search CV. Sebelum optimalsi, akurasi mencapai 95.85%, setelahnya meningkat menjadi 96.26%, menunjukkan peningkatan sebesar 0.41%. Implementasi metode Extra Trees Classifier dengan optimasi Hyperparameter Tuning Grid Search CV lebih unggul dibandingkan penggunaan algoritma tanpa optimasi.

Kata kunci: Prediksi, Extra Trees, Classifier, Hyperparameter, CV

Abstract

Technology continues to advance, especially in communication, education and information. Online education is increasingly in demand in many educational institutions, prompting the need to understand the extent to which learners can adapt to the online environment. Predicting learners' adaptation level is important to improve the effectiveness and quality of the learning experience. In this study, using a dataset from Kaggle, the Extra Trees Classifier method was optimized with Hyperparameter Tuning Grid Search CV. Before optimization, the accuracy reached 95.85%, after which it increased to 96.26%, showing an improvement of 0.41%. The implementation of the Extra Trees Classifier method with Hyperparameter Tuning Grid Search CV optimization is superior to the use of the algorithm without optimization.

Keywords: Prediction, Extra Trees, Classifier, Hyperparameter, CV

Teks Lengkap:

PDF

Referensi

Alexandropoulos, S. A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. Knowledge Engineering Review, 34. https://doi.org/10.1017/S026988891800036X

Ambesange, S., Vijayalaxmi, A., Sridevi, S., Venkateswaran, & Yashoda, B. S. (2020). Multiple Heart Diseases Prediction using Logistic Regression with Ensemble and Hyper Parameter tuning Techniques. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 827â€“832. https://doi.org/10.1109/WorldS450073.2020.9210404

Divva Meuthia Zulma, G., & Chamidah, N. (2021). Perbandingan Metode Klasifikasi Naive Bayes, Decision Tree Dan K-Nearest Neighbor Pada Data Log Firewall. In Seminar Nasional Mahasiswa Ilmu Komputer dan Aplikasinya (SENAMIKA) Jakarta-Indonesia.

Elgeldawi, E., Sayed, A., Galal, A. R., & Zaki, A. M. (2021). Hyperparameter tuning for machine learning algorithms used for arabic sentiment analysis. Informatics, 8(4). https://doi.org/10.3390/informatics8040079

Gbenga, F. O., Olusola, A. A., & Elohor, O. O. (2021). Towards Optimization of Malware Detection using Extra-Tree and Random Forest Feature Selections on Ensemble Classifiers. The International Journal of Recent Technology and Engineering (IJRTE), 9(6), 223â€“232. https://doi.org/10.35940/ijrte.F5545.039621

George, S., & Sumathi, B. (2020). Grid Search Tuning of Hyperparameters in Random Forest Classifier for Customer Feedback Sentiment Prediction. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 11, Issue 9). www.ijacsa.thesai.org

Jurnal, H., Fathurohman FKIP, A., & Fisika, P. (2021). JURNAL INFORMATIKA DAN TEKNOLOGI KOMPUTER MACHINE LEARNING UNTUK PENDIDIKAN: MENGAPA DAN BAGAIMANA. 1(3), 57â€“62.

Mathew, T. E. (2022). AN OPTIMIZED EXTREMELY RANDOMIZED TREE MODEL FOR BREAST CANCER CLASSIFICATION. Journal of Theoretical and Applied Information Technology, 31(16). www.jatit.org

Md, A. Q., Kulkarni, S., Joshua, C. J., Vaichole, T., Mohan, S., & Iwendi, C. (2023). Enhanced Preprocessing Approach Using Ensemble Machine Learning Algorithms for Detecting Liver Disease. Biomedicines, 11(2). https://doi.org/10.3390/biomedicines11020581

Muhamad Malik Matin, I. (2023). Hyperparameter Tuning Menggunakan GridsearchCV pada Random Forest untuk Deteksi Malware. MULTINETICS, 9(1), 43â€“50. https://doi.org/10.32722/multinetics.v9i1.5578

Padmaja, B., Prasad, V. V. R., & Sunitha, K. V. N. (2020). A novel random split point procedure using extremely randomized (Extra) trees ensemble method for human activity recognition. EAI Endorsed Transactions on Pervasive Health and Technology, 6(22), 1â€“10. https://doi.org/10.4108/eai.28-5-2020.164824

Qisthiano, M. R., Prayesy, P. A., & Ruswita, I. (2023). Penerapan Algoritma Decision Tree dalam Klasifikasi Data Prediksi Kelulusan Mahasiswa. G-Tech: Jurnal Teknologi Terapan, 7(1), 21â€“28. https://doi.org/10.33379/gtech.v7i1.1850

Rai, N., Kaushik, N., Kumar, D., Raj, C., & Ali, A. (2022). Mortality prediction of COVID-19 patients using soft voting classifier. International Journal of Cognitive Computing in Engineering, 3, 172â€“179. https://doi.org/10.1016/j.ijcce.2022.09.001

Ranjan, G. S. K., Kumar Verma, A., & Radhika, S. (2019, March 1). K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019. https://doi.org/10.1109/I2CT45611.2019.9033691

Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A., & Choi, G. S. (2021). A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. PLoS ONE, 16(2). https://doi.org/10.1371/journal.pone.0245909

Shaaque, R., Mehmood, A., Choi, G. S., Shafique, R., & Ullah, S. (2019). Cardiovascular Disease Prediction System Using Extra Trees Classiier Cardiovascular Disease Prediction System Using Extra Trees Classifier. https://doi.org/10.21203/rs.2.14454/v1

Suzan, M. M. H., Samrin, N. A., Biswas, A. A., & Pramanik, M. A. (2021). Studentsâ€™ Adaptability Level Prediction in Online Education using Machine Learning Approaches. 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021. https://doi.org/10.1109/ICCCNT51525.2021.9579741

Yang, L., & Shami, A. (2020). On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing, 415, 295â€“316. https://doi.org/10.1016/j.neucom.2020.07.061

DOI: https://doi.org/10.26760/mindjournal.v9i1.78-88