Pemodelan Wilayah Titik Api Kebakaran Hutan Menggunakan Deep Learning
DOI:
https://doi.org/10.22441/fifo.2024.v16i1.001Keywords:
Hotspot, Kebakaran hutan, Pembelajaran Mendalam, Convolutional Neural NetworkAbstract
Indonesia merupakan negara tropis yang mengalami kebakaran hutan setiap tahunnya. Kebakaran hutan terjadi disebabkan oleh durasi musim panas yang terlalu lama dari waktu semestinya. Hutan merupakan tempat tinggal berbagai jenis satwa dan fauna yang memiliki banyak kekayaan hayati yang dapat membuat mereka bertahan hidup. Sering terjadinya kebakaran hutan menjadi isu lingkungan yang dianggap krusial dan mendapatkan perhatian baik dari tingkat lokal maupun internasional. Penelitian yang dilakukan ini menyajikan kajian klasifikasi wilayah titik api kebakaran hutan menggunakan salah satu algoritma Deep Learning (DL) yaitu metode Convolutional Neural Network (CNN), hal ini sangat dibutuhkan untuk pendahuluan mengenai peringatan dini kebakaran hutan yang ada di daerah tersebut. Wilayah titik api kebakaran hutan yang digunakan dalam penelitian ini dikumpulkan dari daerah Nusa Tenggara Timur (NTT), terutama pulau-pulau seperti Sumba dan Timor. Metode CNN melibatkan dua langkah utama. Langkah pertama adalah pengklasifikasian gambar melalui proses feedforward. Langkah kedua adalah fase pembelajaran menggunakan teknik backpropagation. Model CNN yang digunakan dalam proses pelatihan dataset menguji citra dengan beberapa pengoptimal dan diperoleh hasil akurasi yang tinggi. Kemiripan area yang terbakar dengan fitur terang lainnya mengurangi kepastian deteksi kebakaran hutan. Hasil penelitian menunjukkan bahwa Model CNN yang digunakan Untuk deteksi dan segmentasi area terbakar menggunakan algoritma terpilih, kinerja terbaik dengan pembelajaran mendalam yang dilaporkan dalam literatur adalah 89%.Teknik yang diusulkan dilatih menggunakan wilayah varian (kumpulan data) dan mengevaluasi presisi berdasarkan ambang recall, dengan akurasi keseluruhan 89%.
Downloads
References
aka
KLHK (2019) ‘Rekapitulasi Luas Kebakaran Hutan dan Lahan (Ha) Per Provinsi Di Indonesia Tahun 2014-2019’, SiPongi - Karhutla Monitoring Sistem, (1), pp. 26– 27. doi: 10.1103/PhysRevB.65.020504.
Rashkovetsky, D., Mauracher, F., Langer, M., Schmitt, M., 2021. Wildfire detection from multisensor satellite imagery using deep semantic segmentation. IEEE J. Sel. Top Appl. Earth Obs. Remote Sens. 14, 7001–7016.
Van Lierop, P., Lindquist, E., Sathyapala, S. and Franceschini, G., 2015. Gangguan kawasan hutan global akibat kebakaran, hama serangga, penyakit dan kejadian cuaca buruk. Ekologi dan Pengelolaan Hutan, 352,hlm.78-88.
Pinem A, Yulianto S, Dwiastuti R. 2022. Karakteristik spasial data hotspot modis tahun 2019 di Kota Palangka Raya Provinsi Kalimantan Tengah. Jurnal Hutan Tropika. 17(148):104–113.
Tacconi, L. (2003). Kebakaran hutan di Indonesia: Penyebab, Biaya dan Implikasi Kebijakan. Center for International Forestry Research, 38(i)
Cahyono SA, P Warsito S, Andayani W, H Darwanto D. 2015. Faktor-faktor yang mempengaruhi kebakaran hutan di Indonesia dan implikasi kebijakannya. Jurnal Sylva Lestari. 3(1):103-112.
Mardiani D. 2014. Hubungan curah hujan dan titik panas (hotspot) dalam kaitannya dengan terjadinya kebakaran di Provinsi Aceh [skripsi]. Bogor (ID): Institut Pertanian Bogor.
Saruni Dwiasnati and Yudo Devianto, “Classification of forest fire areas using machine learningalgorithm,” World Journal of Advanced Engineering Technology and Sciences, vol. 3, no. 1, 2021, doi: 10.30574/wjaets.2021.3.1.0048
Bambang Hero Saharjo dan Uswatun Hasanah. Analisis Faktor Penyebab Terjadinya Kebakaran Hutan Dan Lahan Di Kabupaten Pulang Pisau, Kalimantan Tengah. Jurnal Sulvikurtur Tropika. Vol 14. No 01, April. 2023
Gao, L., Wang, X., Johnson, B.A., Tian, Q., Wang, Y., Verrelst, J., Mu, X., Gu, X., 2020. Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review. ISPRS J. Photogramm. Remote Sens. 159, 364–377.
Langman B. (2014) The effects of vegetation and peat fire emissions in Indonesia on air pollution and global climate. Asian Journal of Water, Environment and Pollution 11(1): 3–11.
Othman J., Sahani M., Mahmud M., Ahmad M. K. S. (2014) Transboundary smoke haze pollution in Malaysia: Inpatient health impacts and economic valuation. Environmental Pollution 189: 194–201. Crossref. PubMed.
Sunarya, A., Santoso, S., & Sentanu, W. (2015). Sistem Pakar Untuk Mendiagnosa Gangguan Jaringan Lan. CCIT Journal, 8(2), 1–11
Rahardja, U., Roihan, A., & others. (2017). Design of Business Intelligence in Learning Systems Using iLearning Media. Universal Journal of Management, 5(5), 227–235.Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.
Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited
Stowell, D., Wood, M. and Pamuła, H. (2018) ‘arXiv : 1807 . 05812v1 [ cs . SD ] 16 Jul 2018 Automatic acoustic detection of birds through deep learning : the fi rst Bird Audio Detection challenge’, (15), pp. 1–21.
Florentin, J., Dutoit, T., Verlinden, O., 2020. Detection and identification of European woodpeckers with deep convolutional neural networks. Ecol. Inf. 55, 101023. https://doi.org/10.1016/j.ecoinf.2019.101023.
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1–3), 489–501.
Zottesso, R.H.D., Costa, Y.M.G., Bertolini, D., Oliveira, L.E.S., 2018. Bird species identification using spectrogram and dissimilarity approach. Ecol. Inf. 48, 187– 197. https://doi.org/10.1016/j.ecoinf.2018.08.007.
E. N. Arrofiqoh and H. Harintaka, “Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Tanaman Pada Citra Resolusi Tinggi,” Geomatika, vol. 24, no. 2, p. 61, 2018,doi: 10.24895/jig.2018.24-2.810
J. Han, M. Kamber, and J. Pei, Data mining: Data mining concepts and techniques. 2014. doi: 10.1109/ICMIRA.2013.45
Suyanto, K. N. Ramadhani, and S. Mandala, “Deep Learning Modernisasi Machine Learning untuk Big Data,” in DEEP LEARNING Modernisasi Machine Learning untuk Big Data, Informatika, 2019, pp. 33–41
L. Alzubaidi et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, vol. 8, no. 1. Springer International Publishing, 2021. doi: 10.1186/s40537-021-00444-8.
Dhika, H., Rahma Kurnianda, N., Ananta, W., Matematika, F. I., Alam, P., Komputer, F. I., & Mercu Buana, U. (n.d.). Model Prediksi Jenis Hewan dengan Metode Convolution Neural Network. http://www.kaggle.com/c/dogs-vs. Format. Volume 9 Nomor 1 Tahun 2020 :: ISSN : 2089 - 5615 :: E-ISSN : 2722 - 7162
N. Padia, P. Siddharth, A. Hirpara, and D. Jani, “Cataract dataset.” https://www.kaggle.com/datasets/nandanp6/cataract-image-dataset
Q. Zhang, M. Zhang, T. Chen, Z. Sun, Y. Ma, and B. Yu, “Recent advances in convolutional neural network acceleration,” Neurocomputing, vol. 323, pp. 37–51, 2019, doi: 10.1016/j.neucom.2018.09.038.
D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” J. Physiol., vol. 160, no. 1, pp. 106–154, 1962, doi: 10.1113/jphysiol.1962.sp006837.
M. W. Ilahi, C. N. Apriyani, A. Desiani, N. Gofar, Y. Andriani, and M. R. Halim, “Classification of Geometric Batik Motif Typical of Indonesian Using Convolutional Neural Network,” J. Tek. Inform., vol. 15, no. 1, pp. 91–100, 2022, doi: 10.15408/jti.v15i1.24968
M. W. Ilahi1, C. N. Apriyani, A. Desiani, N. Gofar, Y. Andriani, and M. R. Halim, “Classification of Geometric Batik Motif Typical of Indonesian Using Convolutional Neural Network,” vol. 15, no. 1, pp. 91–100, 2022
A. K. Putra and H. Bunyamin, “Pengenalan Simbol Matematika dengan Metode Convolutional Neural Network ( CNN ),” vol. 2, no. November, pp. 426–433, 2020.
A. Farhah, A. L. Prasasti, and M. W. Paryasto, “Implementasi Recurrent Neural Network dalam Memprediksi Kepadatan Restoran Berbasis LSTM,” J. Media Inform. Budidarma, vol. 5, no. 2, p. 524, 2021, doi: 10.30865/mib.v5i2.2916.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021.
D. Putra and A. Wibowo, “Prediksi Keputusan Minat Penjurusan Siswa SMA Yadika 5 Menggunakan Algoritma Naïve Bayes,” vol. 2, pp. 84–92, 2020.
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
The copyright to this article is transferred to Universitas Mercu Buana (UMB) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to UMB. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment.
We declare that this paper has not been published in the same form elsewhere.
Furthermore, I/We hereby transfer the unlimited rights of publication of the above-mentioned paper as a whole to UMB. The copyright transfer covers the right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
Our Articles are licensed under CC BY-NC
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
