Perbandingan Akurasi Algoritma Svm Dan Algoritma K-Nn Pada Dataset Hasil Panen Kelapa Sawit

Kevin Yoga, Pratama (2022) Perbandingan Akurasi Algoritma Svm Dan Algoritma K-Nn Pada Dataset Hasil Panen Kelapa Sawit. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

Oil palm (Elaeis Guineensis) is one of the largest export commodities in the plantation sector in Indonesia. Technological advances in computer science have contributed to various fields, one of which is in oil palm plantations. The application of this technology is described in one of the fields in data mining. Data mining is grouped into 5 (five) groups, namely estimation, prediction, classification, clustering, association. Some of the algorithms that can be used include Decision Tree, K-NN, and SVM with their respective advantages and disadvantages. In this study, SVM and K-NN algorithms were used to predict oil palm yields. The initial step taken is the process of finding the MAX and normal values for the oil palm harvest dataset to convert the data into a form that is suitable for the data mining process. Testing this system consists of testing the accuracy of the SVM and K-NN algorithms with the estimated time required to execute the program. The results of this study that the algorithm SVM produces an accuracy value of 70,588% while the K-NN algorithm produces an accuracy value of 88,235% at a value of K=3. The time required to execute the dataset of each algorithm, namely the SVM algorithm takes 4 seconds to execute while the K-NN algorithm takes 2 seconds to execute. Keywords: Accuracy, K-NN, Data Mining, Prediction, SVM.

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