PERBANDINGAN ALGORITMA SUPERVISED MACHINE LEARNING UNTUK SISTEM PENGHINDARAN HALANGAN PADA ROBOT ASSISTANT UDAYANA 02 (RATNA02)

Abstract

Supervised Machine Learning can make robots smarter by making decisions automatically. This study compares various Supervised Machine Learning algorithms to determine the best algorithm that can be used on Robot Assistant Udayana 02 (RATNA02). The algorithms to be compared are Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayesian (NB), and K-Nearest Neighbor (KNN). Models were created using the TensorFlow and SKLearn libraries. The model is trained using 100.000 data of left sensor, right sensor, front sensor, robot offset, and data label. Data preprocessing is done using MinMaxScalar and LabelEncoder. The comparisons that will be measured are accuracy, training duration, and file size of the model. DT and RF algorithms get 100% accuracy followed by ANN, KNN and SVM with 99.87%, 97.42% and 98.52% respectively, and NB with 87.34%. Fastest training duration was achieved by NB for 0.03 seconds, followed by DT for 0.08 seconds, while other algorithms took more than one second. The smallest file size is owned by NB with a size of 2kb and DT ranks second with 4kb, other algorithms have a file size of more than 25kb. Decision Tree Algorithm is the best because the duration of the model training is relatively fast, the file size is small, and the accuracy is high.