Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Teknimedia: Teknologi Informasi dan Multimedia
Dina Maulina
MTI Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
PEMETAAN LOKASI KEBAKARAN HUTAN DAN LAHAN DI NTB DENGAN MENGGUNAKAN ALGORITMA NAIVE BAYES Rachmawati Oktaria Mardiyanto; Fitriani Fitriani; Ridwan Joko Purnomo; Kusrini Kusrini; Dina Maulina
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 2 No. 2 (2021): Desember 2021
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (304.49 KB) | DOI: 10.46764/teknimedia.v2i2.44

Abstract

Forest and land fires are one of the environmental problems in terms of economic and ecological harm. The number of cases of forest fires in the province of NTB has increased dramatically, causing dangerous smog. The increasing incidence of forest and land fires is evidenced by the increasing area burned and the frequency of fires in the last few decades. This study aims to classify the locations of forest and land fires and the causes of fires that occur in NTB. This research has used 104 data in three years (2017-2019) taken from the website of the Department of Environment and Forestry of the province of NTB. The classification model for mapping forest and land fires and the causes of fires uses the Naïve Bayes algorithm with an accuracy value of 55.555%. Thus, it can be concluded that the classification model using Naïve Bayes has the potential to be used effectively so that it can classify the location of forest and land fires and the causes of fires.
NORMALISASI DATA UNTUK EFISIENSI K-MEANS PADA PENGELOMPOKAN WILAYAH BERPOTENSI KEBAKARAN HUTAN DAN LAHAN BERDASARKAN SEBARAN TITIK PANAS Ahmad Harmain; Paiman Paiman; Henri Kurniawan; Kusrini Kusrini; Dina Maulina
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 2 No. 2 (2021): Desember 2021
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (333.454 KB) | DOI: 10.46764/teknimedia.v2i2.49

Abstract

The Indonesian region is part of the tropics which has a very high fire potential, especially during the dry season, so it is necessary to take concrete steps to mitigate so that the potential for forest fires is minimized. To do this, a more advanced and up-to-date technological method is needed to map areas that have a high potential for forest fires. The imaging and information system from the satellite system (MODIS) is one of the information about the condition of the earth's surface, namely the parameters of Latitude, Longitude, Brightness, FRP (Fire Radiative Power), and Confidence, which can be used as the basis for grouping an area as having a fire potential or not. K-Means is a method in machine learning that can be used as a method for grouping these areas. Accuracy in testing the results of the K-Means grouping can be tested using the Davies Bouldin Index (DBI) and Silhouette Coefficient methods.
Co-Authors Ahmad Harmain Fitriani Fitriani Henri Kurniawan Kusrini Kusrini Paiman Paiman Rachmawati Oktaria Mardiyanto Ridwan Joko Purnomo