Pengaruh Metode Pengukuran Jarak pada Algoritma k-NN untuk Klasifikasi Kebakaran Hutan dan Lahan

Abstract

Forest and land fires are a serious and recurring problem in Indonesia. The high intensity of forest fires is caused by the distribution of hotspots in fire-prone areas. One of the efforts to prevent and minimize the risk of forest fires is to identify the types of hotspots using a classification approach. One of the most popular classification algorithms is k Nearest Neighbor (k-NN). The algorithm uses a distance calculation approach in classifying objects. The purpose of this study is to classify the types of hotspots scattered in Indonesia using the k-NN algorithm and to analyze the effect of the distance calculation method on the k-NN algorithm. The types of distance measurement methods analyzed include Euclidean, Canberra, Chebyshev, and Manhattan. The dataset used is the distribution of hotspots in Indonesia obtained from Global Forest Watch (GFW). The study designed a dataset with two conditions, through the pre-processing stage and not. In general, the model accuracy of the k-NN combination with various distance measurement methods is above 90%. The pre-processing stage can increase the model's performance 1-8 times. The combination of k-NN with Manhattan is the best choice to identify the types of hotspots with an accuracy of 92.6%.

Determinants of User Trust in Financial Technology Usage in Indonesia

“…This study uses quantitative methods, which is a scientific approach that uses quantitative procedures or techniques systematically (Karo-karo, 2022). Quantitative methods are defined as a fundamental method for studying certain populations or samples, research tools are also used for data collection, data analysis is statistical / quantitative, the aim is to test predetermined hypotheses (Hutasuhut, 2023).…”

K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection

“…The K-Nearest Neighbor method is one of the ten most popular K-NN algorithms for finding the set of k nearest objects from the training data and organizing the data into groups of objects with a high degree of similarity. The method of determining similarity is based on the results of calculating the smallest distance between objects [9]. Various feature selection methods are used to select irrelevant features, such as forward selection, backward elimination, and optimize selection.…”

Optimizing Stroke Mortality Prediction: A Comprehensive Study on Risk Factors Analysis and Hyperparameter Tuning Techniques

“…Hyperparameters are used to improve algorithm performance, and this has a significant impact on a variety of model tests. The hyperparameter adjustment process can be done manually or by testing a group of hyperparameter on a previously specified parameter [34].…”