Penerapan Algoritma Random Forest dalam Menganalisa Perubahan Suhu Permukaan Wilayah Kota Salatiga
DOI:
https://doi.org/10.30865/mib.v6i4.4603Keywords:
NDVI, NDBI, LST, Albedo, Pearson Correlation, Random Forest.Abstract
The population increase in Salatiga city is growing rapidly from 2010 to 2020. This change affects the area with vegetation cover, increasing building density and increasing land surface temperatures. The rising of land surface temperature can affect climate change, air quality, human health quality and energy usage. The purpose of this research is to find out the effect of the area with built-up land and area with vegetation cover to land surface temperature by exploring the values of NDVI, NDBI, LST and Albedo. This research shows that the NDVI value has decreased while the NDBI, LST and Albedo values have increased from 2014 to 2021. The values of NDVI, NDBI and Albedo are the components used as validation of the value of the land surface temperature (LST) change in the study area. The results of the correlation between indices show that the highest correlation occurs between NDVI and NDBI with a value of -0.979 which has a negative correlation because vegetation density is always inversely proportional to the density of built up land. The classification results show that there are 7 villages in Salatiga City with high temperature increases, the villages name are Cebongan, Mangunsari, Ledok, Kutowinangun Kidul, Gendongan, Salatiga and Kalicacing. The results of the accuracy and kappa values in the Random Forest algorithm are quite accurate with an accuracy value of 90% and a kappa value of 73%. The usability test in this study was carried out by distributing questionnaires to city planning department in Salatiga City who had a recapitulation result of 3.62 with the criteria "quite useful". From these results, this research is in accordance with its objectives, the result can be used as one of the city government's recommendations for policy making, especially in Salatiga city planning department.
References
P. K. Salatiga, “Selayang Pandang Kota Salatiga,†2019.
BPS Salatiga, “Tabel Dinamis,†https://salatigakota.bps.go.id/site/pilihdata.html , 2022.
W. Ningrum and I. Narulita, “Deteksi Perubahan Suhu Permukaan Menggunakan Data Satelit Landsat Multi-Waktu Studi Kasus Cekungan Bandung,†Jurnal Teknologi Lingkungan, vol. 19, no. 2, p. 145, 2018, doi: 10.29122/jtl.v19i2.2250.
S. Nugroho, A. Wijaya, and A. Sukmono, “Analisis Pengaruh Perubahan Vegetasi Terhadap Suhu Permukaan Di Wilayah Kabupaten Semarang Menggunakan Metode Penginderaan Jauh,†Jurnal Geodesi Undip, vol. 5, no. 1, pp. 253–263, 2016.
D. Kosasih, I. Nasihin, and E. R. Zulkarnain, “Deteksi Kerapatan Vegetasi dan Suhu Permukaan Tanah Menggunakan Citra Landsat 8 (Studi Kasus : Stasiun Penelitian Pasir Batang Taman Nasional Gunung Ciremai,†Konservasi untuk Kesejahteraan Masyarakat, vol. 1, pp. 162–173, 2019.
M. N. Handayani, B. Sasmito, and A. Putra, “ANALISIS HUBUNGAN ANTARA PERUBAHAN SUHU DENGAN INDEKS KAWASAN TERBANGUN MENGGUNAKAN CITRA LANDSAT (STUDI KASUS : KOTA SURAKARTA) Mutiah,†Jurnal Geodesi Undip, vol. 2, no. Sistem Informasi Geografis, pp. 240–252, 2017.
H. A. Effat and O. A. K. Hassan, “Change detection of urban heat islands and some related parameters using multi-temporal Landsat images; a case study for Cairo city, Egypt,†Urban Climate, vol. 10, no. P1, pp. 171–188, 2014, doi: 10.1016/j.uclim.2014.10.011.
G. M. Foody, “Status of land cover classification accuracy assessment,†Remote Sensing of Environment, vol. 80, no. 1, pp. 185–201, 2002, doi: https://doi.org/10.1016/S0034-4257(01)00295-4.
D. R. Amliana, Y. Prasetyo, and A. Sukmono, “ANALISIS PERBANDINGAN NILAI NDVI LANDSAT 7 DAN LANDSAT 8 PADA KELAS TUTUPAN LAHAN (Studi Kasus : Kota Semarang, Jawa tengah),†2016.
F. S. Wirandha, Marwan, and Nizamuddin, “Klasifikasi Penggunaan Lahan Menggunakan Citra Satelit Spot-6 di Kabupaten Aceh Barat Daya Dan Aceh Besar,†Seminar Nasional dan Expo Teknik Elektro 2015, 2015, Accessed: Jun. 08, 2022. [Online]. Available: http://snete.unsyiah.ac.id/2015/prosiding/Naskah%2018.pdf
R. Yudistira, A. Meha, and S. Prasetyo, “Perubahan Konversi Lahan Menggunakan NDVI, EVI, SAVI dan PCA pada Citra Landsat 8 (Studi Kasus : Kota Salatiga),†Indonesian Journal of Computing and Modeling, vol. 2, no. 1, Jun. 2019, [Online]. Available: https://ejournal.uksw.edu/icm/article/view/2537
R. M. S. P. A. Balqis Nailufar1*, “ANALISIS PERUBAHAN INDEKS KERAPATAN VEGETASI DENGAN METODE ANALISIS NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) DI KOTA BATU BERBASIS SISTEM INFORMASI GEOGRAFIS (GIS) DAN PENGINDRAAN JAUH.,†MINTAKAT Jurnal Arsitektur, vol. 19, pp. 59–67, Sep. 2018.
Y. Zha, J. Gao, and S. Ni, “Use of normalized difference built-up index in automatically mapping urban areas from TM imagery,†ï®ï¯, vol. 24, pp. 583–594, 2003, doi: 10.1080/01431160210144570.
J. R. H. D. F. T. Syahputra A, “Perbandingan Indeks Lahan Terbangun NDBI dan Land Surface Temperature Dalam Memetakan Kepadatan Bangunan di Kota Medan,†Journal of Science, Technology, and Virtual Science, vol. 1, pp. 16–22, Jul. 2021.
R. B. Smith, “The heat budget of the earth’s surface deduced from space,†2010.
I. L. M. Zahir, “Application of Geo-informatics Technology to Access the Surface Temperature Using LANDSAT 8 OLI/TIRS Satellite Data: A Case Study in Ampara District in Sri Lanka,†2020, doi: 10.20944/preprints202009.0289.v1.
F. Wang, Z. Qin, C. Song, L. Tu, A. Karnieli, and S. Zhao, “An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data,†Remote Sensing, vol. 7, no. 4, pp. 4268–4289, 2015, doi: 10.3390/rs70404268.
R. Zhibin, Z. Haifeng, H. Xingyuan, Z. Dan, and Y. Xingyang, “Estimation of the Relationship Between Urban Vegetation Configuration and Land Surface Temperature with Remote Sensing,†Journal of the Indian Society of Remote Sensing, vol. 43, no. 1, pp. 89–100, 2015, doi: 10.1007/s12524-014-0373-9.
U. Avdan and G. Jovanovska, “Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data,†Journal of Sensors, vol. 2016, p. 1480307, 2016, doi: 10.1155/2016/1480307.
T. K. Ho, “Random decision forests,†in Proceedings of 3rd International Conference on Document Analysis and Recognition, 1995, vol. 1, pp. 278–282 vol.1. doi: 10.1109/ICDAR.1995.598994.
N. Horning, “Random Forests : An algorithm for image classification and generation of continuous fields data sets,†2010.
A. Primajaya and B. N. Sari, “Random Forest Algorithm for Prediction of Precipitation,†Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 1, no. 1, pp. 27–31, 2018.
Y. Christianto, S. Prasetyo, and K. Hartomo, “Analisis Data Citra Landsat 8 OLI Sebagai Indeks Prediksi Kekeringan Menggunakan Machine Learning di Wilayah Kabupaten Boyolali dan Purworejo,†Indonesian Journal of Computing and Modeling, vol. 2, no. 2, Dec. 2019, [Online]. Available: https://ejournal.uksw.edu/icm/article/view/2954
S. Y. J. Prasetyo, K. D. Hartomo, M. C. Paseleng, D. W. Chandra, and E. Winarko, “Satellite imagery and machine learning for aridity disaster classification using vegetation indices,†Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, pp. 1149–1158, Jun. 2020, doi: 10.11591/eei.v9i3.1916.
M. Sholikhan, S. Prasetyo, and K. Hartomo, “Pemetaan Lokasi UMKM Kaligrafi Kabupaten Kudus dengan Metode Location Based Service sebagai Media Promosi Berbasis WebGIS,†Indonesian Journal of Computing and Modeling, vol. 2, no. 1, Jun. 2019, [Online]. Available: https://ejournal.uksw.edu/icm/article/view/2535
M. P. Prayoga, “Analisis Spasial Tingkat Kekeringan Wilayah Berbasis Penginderaan Jauh dan Sistem Informasi Geografisâ€, Accessed: Jun. 04, 2022. [Online]. Available: https://repository.its.ac.id/44527/1/3513100067-Undergraduate_Theses.pdf
United Nations Human Settlements Programme (UNHabitat), “Panduan lnternasional tentang Perencanaan Kota dan Wilayah,†2015. [Online]. Available: www.unhabitat.org
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).