Afala, Iswari (2022) EVALUASI ALGORITMA K-NEAREST NEIGHBORS, RANDOM FOREST DAN XGBOOST PADA PREDIKSI ENERGI ATOMISASI MOLEKUL. Undergraduate thesis, UNIMED.
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Abstract
Penelitian ini bertujuan untuk menyelesaikan nilai dari energi atomisasi molekul tanpa menggunakan metode analitik maupun numerik, dengan berkembang pesatnya teknologi memungkinkan mengetahui nilai energi atomisasi molekul secara cepat dan efisien dengan algoritma Machine Learning dan Matrix Coulomb sebagai deskriptornya dengan membandingkan dataset asli dari database PubChem sebanyak 16242 molekul dari unsur penyusun C ,H, N ,O, P dan S dengan hasil prediksi masing – masing algoritma. Penelitian ini dilakukan di Laboratorium Komputer Jurusan Fisika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Negeri Medan yang dilaksanakan pada bulan Juni hingga Juli 2022. Algoritma yang digunakan ialah K-Nearest Neighbors, Random Forest, dan XGBoost dengan bantuan library dari Scikit-Learn. Variabel yang diuji ialah nilai RMSE (Root Mean Square Error), R2 (Koefisien Determinasi), lama waktu, penggunaan resource dan hyperparameter masing – masing algoritma. Hasil penelitian menunjukkan bahwa algoritma K-Nearest Neighbors buruk dalam memprediksi dengan nilai RMSE 2,0226, nilai R2 0,6989 dan lama waktu yang dibutuhkan selama 5,7 detik. Untuk algoritma Random Forest nenghasilkan nilai RMSE 0,0089, nilai R2 0,9999 dan lama waktu yang dibutuhkan selama 343 detik, sedangkan algoritma XGBoost menghasilkan nilai RMSE 0,0647, R2 0,9996 dan lama waktu yang dibutuhkan selama 99 detik. Penggunaan resource dari ketiga algoritma ini menyentuh 90 % dari CPU dan RAM nya
Item Type: | Thesis (Undergraduate) | ||||||
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Call Number: | 2022-FIS-077 / 518.1 Afa e | ||||||
Keywords: | Machine Learning; Matrix Colomb; RMSE; R2; K-Nearest Neighbors; Random Forest; XGBoost; hyperparameter; Scikit-Learn | ||||||
Subjects: | Q Science > QC Physics | ||||||
Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Fisika | ||||||
Depositing User: | Mrs Gusti Lisa Utami | ||||||
Date Deposited: | 07 Dec 2022 01:20 | ||||||
URI: | http://digilib.unimed.ac.id/id/eprint/49468 |
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