PENERAPAN K-NEAREST NEIGHBORS PADA PERCEPTRON UNTUK KLASIFIKASI DATASET KECIL DENGAN TIGA FITUR

Arjuna Ranma Putra; Julya Rahmah Shanty; Maria Lodiana Leki; Muhammad Aqshal Hidayat Fizhillillah; Rifky Firmansyah; Devi Yunita

Authors

Arjuna Ranma Putra Universitas Pamulang
Julya Rahmah Shanty Universitas Pamulang
Maria Lodiana Leki Universitas Pamulang
Muhammad Aqshal Hidayat Fizhillillah Universitas Pamulang
Rifky Firmansyah Universitas Pamulang
Devi Yunita Universitas Pamulang

Keywords:

Single Layer Perceptron, K-Nearest Neighbor, Cross Validation, Small Dataset Classification

Abstract

In developing machine learning models for small datasets, choosing the right method is key to producing accurate classification. This research applies the Single Layer Perceptron (SLP) algorithm to classify a small dataset with three main features, namely Feature1, Feature2, and Feature3. The SLP algorithm is used to learn patterns in the data, with model evaluation using the k-fold cross-validation technique. This technique ensures each piece of data is used as test and training data in turn, to obtain more accurate evaluation results. In addition, the k-Nearest Neighbor (k-NN) algorithm was used to find the optimal K parameter value to improve the accuracy of the model. This study used 13 sample data to train and test the model.

References

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