Classification of forest fire areas using machine learning algorithm

Abstract

Forest fires that occur will cause various kinds of problems, both in terms of health, such as smoke that can interfere with the respiratory system, in terms of the economy such as the economic wheel cannot run as usual, in terms of the environment can damage the surrounding environment and the environment that is missed by smoke, and other disasters. Forest fires can also have an impact on the costs that will be incurred to resolve the problems that arise due to forest fires, so research is needed to find out and measure the area affected by forest fires that burned in the range of 1980 - 2019 using a dataset of approximately 10,000. The target in this research is to be able to generate the best percentage scenario and find out the model of using the algorithm used to explore the algorithm in the Machine Learning method for the model for estimating the area of forest fires, namely the Siak Kampar Peninsula in Riau Province. In this study, 7 parameters were used to create a forest and land fire hazard map, namely weather temperature, Burned Area Density, hotspot density, wind speed, land cover type, rainfall, and land use. The seven parameters will be searched for accuracy results using the Classification method with Machine Learning algorithms, including Naïve Bayes, SVM, and K-Nearest Neighbor (K-NN). In this study, comparisons were made to obtain the best algorithm for estimating forest fire areas. By generating each algorithm is 71.72% for the Naïve Bayes algorithm, 75.00% for the SVM algorithm, and 64.71% for the K-NN algorithm.

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

“…Penambangan data adalah suatu proses penambangan informasi penting dari suatu data yang tidak dapat dilakukan oleh metode tradisional [5]. Salah satu fungsi penambangan data yang dapat digunakan untuk mengidentifikasi jenis titik panas adalah klasifikasi [1,6] Satu dari sepuluh algoritma klasifikasi terpopuler adalah k-nearest neighbor (k-NN) [6,7]. Ide algoritma k-NN adalah menggunakan fungsi jarak dalam menghitung kemiripan antar data sedemikian hingga dikelompokkan berdasarkan kemiripan antar data.…”

“…Algoritma k-Nearest Neighbor merupakan satu dari sepuluh algoritma klasifikasi terpopuler [6,9] . Cara kerja k-NN adalah dengan mencari sekelompok k objek terdekat dari data latih dan mengelompokan data tersebut kedalam kelompok objek dengan tingkat kemiripan tertinggi [7,11].…”

“…Recall merupakan tingkat keberhasilan sistem dalam menemukan kembali sebuah informasi. Recall dapat dihitung menggunakan persamaan (6).…”

Perbandingan Metode Klasifikasi serta Analisis Faktor Akademis Pola Kelulusan Mahasiswa di Perguruan Tinggi

“…Cabang ilmu ini memfokuskan pada pembuatan sistem atau algoritma yang terus-menerus belajar dari data dan meningkatkan akurasinya. Dalam aplikasi machine learning, algoritma atau urutan proses statistik dilatih untuk dapat menemukan pola dan fitur tertentu dalam sejumlah data yang besar [8].…”

Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side

“…Classification through machine learning methods is becoming a commonly used approach in various fields. Many studies in the literature indicate that machine learning methods are efficient in classification problems, offering high accuracy and minimal deviation [46][47][48][49][50][51].…”