Prediksi Spasial Kerapatan Vegetasi Perkotaan dengan Pendekatan Algoritma Time Series Untuk Mendukung Pertumbuhan Ekonomi Hijau
-
Yudistira Bagus Pratama
*
Universitas Muhammdiyah Bangka Belitung, Pangkal Pinang,
Indonesia
https://orcid.org/0000-0003-0676-897X
- Nurzaidah Putri Dalimunthe Universitas Muhammdiyah Bangka Belitung, Pangkal Pinang, Indonesia
- Mega Sukma Universitas Muhammdiyah Bangka Belitung, Pangkal Pinang, Indonesia (*) Corresponding Author
Abstract
The urgency of this research is based on data from the Pangkalpinang City Population and Civil Registration Service in 2020, the population reached 218,569 people and continued to grow to 232,915 people in 2023. The importance of monitoring vegetation density in the context of green economic growth, which requires careful evaluation of the balance between economic development and environmental conservation. With rapid urban growth, the Pangkalpinang city government requires a variety of approaches to accurately predict vegetation density. By identifying the factors that affect land vegetation density, this study aims to develop a machine learning model that can predict land vegetation density conditions over a certain period of time in the future. This research method involves collecting spatial vegetation density data over a period of 11 years using remote sensing technology or remote monitoring, such as satellite imagery. Furthermore, time series data will be analyzed and modeled using machine learning techniques, focusing on algorithms that can overcome the spatial and temporal dynamics of vegetation density. Machine learning algorithms, especially time series algorithms such as Autoregressive Integrated Moving Average (ARIMA) will be used to build a spatial prediction model for vegetation density. The results of this study indicate that the use of ARIMA is able to produce an accurate prediction model in projecting vegetation density in Pangkalpinang City. The ARIMA model shows strong performance with low error metrics, indicating its effectiveness in making accurate predictions for the given data set. The results of this study are expected to provide valuable information for the Pangkalpinang city government in making decisions related to environmental management and green economic development. By involving collaboration between researchers with three complementary expertise including computer science, civil engineering and natural resource conservation and policy makers.
Downloads
References
S. R. Shams, A. Jahani, S. Kalantary, M. Moeinaddini, and N. Khorasani, “The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration,” Urban Clim., vol. 37, p. 100837, 2021.
Z. Zhou et al., “Analysis and prediction of vegetation dynamic changes in China: Past, present and future,” Ecol. Indic., vol. 117, p. 106642, Oct. 2020, doi: 10.1016/j.ecolind.2020.106642.
A. Y. Helmi, K. M. Harianto, and B. G. Dimmera, “Pemanfaatan Ruang Terbuka Hijau Dalam Meningkatkan Kualitas Lingkungan Kabupaten Bengkayang” Jurdian, 2023.
Z. Zhang, X. Wan, K. Sheng, H. Sun, L. Jia, and J. Peng, “Impact of Carbon Sequestration by Terrestrial Vegetation on Economic Growth: Evidence from Chinese County Satellite Data,” Sustainability, vol. 15, no. 2, 2023, doi: 10.3390/su15021369.
Y. B. Pratama and N. P. Dalimunthe, “Implementasi Teknik Computer Vision Untuk Deteksi Viridiplantae Pada Lahan Pasca Tambang,” Bulletin of Computer Science Research, vol. 3, no. 1, pp. 64–72, 2022, doi: 10.47065/bulletincsr.v3i1.193.
Q. Qin, D. Xu, L. Hou, B. Shen, and X. Xin, “Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment,” Ecol. Indic., vol. 133, p. 108363, Dec. 2021, doi: 10.1016/J.ECOLIND.2021.108363.
A. Hardianto, P. U. Dewi, T. Feriansyah, N. F. S. Sari, N. S. Rifiana, and others, “Pemanfaatan Citra Landsat 8 Dalam Mengidentifikasi Nilai Indeks Kerapatan Vegetasi (NDVI) Tahun 2013 dan 2019 (Area Studi: Kota Bandar Lampung),” J. Geosains Dan Remote Sens., vol. 2, no. 1, pp. 8–15, 2021.
I. K. Putrajaya and I. P. H. M. Martayana, “Analysis of Vegetation Density Using Landsat 8 Imagery for Mapping Characteristics of Green Open Space in Denpasar City,” EAI, 2021. doi: 10.4108/eai.10-11-2020.2303425.
Y. B. Pratama and A. Setiawan, “Implementasi Machine Learning Menggunakan Algoritma K-Means Untuk Klasifikasi Sekolah Dasar,” Resolusi Rekayasa Tek. Inform. dan Inf., vol. 4, no. 3, pp. 249–257, 2024.
Y. A. Singgalen, “Tourism infrastructure development and transformation of vegetation index in Dodola Island of Morotai Island Regency,” J. Inf. Syst. Informatics, vol. 4, no. 1, pp. 130–144, 2022.
O. I. Gifari, K. Kusrini, and K. A. Yuana, “Analisis Perubahan Tutupan Lahan Menggunakan Metode Klasifikasi Terbimbing Pada Data Citra Penginderaan Jauh Kota Samarinda-Kalimantan Timur,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 18, no. 2, pp. 71–77, 2023.
D. Marlina, “Klasifikasi Tutupan Lahan pada Citra Sentinel-2 Kabupaten Kuningan dengan NDVI dan Algoritme Random Forest,” STRING (Satuan Tulisan Ris. Dan Inov. Teknol., vol. 7, no. 1, pp. 41–49, 2022.
A. Y. Isnaeni and S. Y. J. Prasetyo, “Klasifikasi Wilayah Potensi Risiko Kerusakan Lahan Akibat Bencana Tsunami Menggunakan Machine Learning,” J. Tek. Inform. Dan Sist. Inf., vol. 8, no. 1, pp. 33–42, 2022.
F. Ramadhani, R. Pullanagari, G. Kereszturi, and J. Procter, “Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning,” Int. J. Remote Sens., vol. 41, no. 21, pp. 8428–8452, 2020.
A. S. Stepanov, E. A. Fomina, L. V Illarionova, K. N. Dubrovin, and D. V. Fedoseev, “Vegetation indices (NDVI and EVI) time series approximation for monitoring crops of Khabarovsk territory,” Informatics Autom., vol. 22, no. 6, pp. 1473–1498, 2023.
S. T. C. Wirawan and H. P. Chernovita, “Analysis of Vegetation Changes Using Satellite Imagery and Normalized Difference Vegetation Index (NDVI): A Case Study in Tuntang District, Semarang District,” J. Inf. Syst. Informatics, vol. 5, no. 4, pp. 1809–1820, 2023.
B. Haq et al., “Tech-Driven Forest Conservation: Combating Deforestation With Internet of Things, Artificial Intelligence, and Remote Sensing,” IEEE Internet Things J., 2024.
R. Kumar, P. Kumar, and Y. Kumar, “Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms,” Int. J. Inf. Technol., vol. 14, no. 1, pp. 359–373, 2022.
H. Rakuasa and D. A. Sihasale, “Analysis of Vegetation Index in Ambon City Using Sentinel-2 Satellite Image Data with Normalized Difference Vegetation Index (NDVI) Method based on Google Earth Engine,” J. Innov. Inf. Technol. Appl., vol. 5, no. 1, pp. 74–82, 2023.
S. M. Al-Hedny and A. S. Muhaimeed, “Drought monitoring for Northern Part of Iraq using temporal NDVI and rainfall indices,” Environ. Remote Sens. GIS Iraq, pp. 301–331, 2020.
M. G. Ghebrezgabher, T. Yang, X. Yang, and T. E. Sereke, “Assessment of NDVI variations in responses to climate change in the Horn of Africa,” Egypt. J. Remote Sens. Sp. Sci., vol. 23, no. 3, pp. 249–261, 2020.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Trans. neural networks Learn. Syst., vol. 32, no. 1, pp. 4–24, 2020.
H. Wang, J. Zhang, and J. Yang, “Time series forecasting of pedestrian-level urban air temperature by LSTM: Guidance for practitioners,” Urban Clim., vol. 56, p. 102063, Jul. 2024, doi: 10.1016/J.UCLIM.2024.102063.
R. Ahmad, B. Yang, G. Ettlin, A. Berger, and P. Rodr’iguez-Bocca, “A machine-learning based ConvLSTM architecture for NDVI forecasting,” Int. Trans. Oper. Res., vol. 30, no. 4, pp. 2025–2048, 2023.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Prediksi Spasial Kerapatan Vegetasi Perkotaan dengan Pendekatan Algoritma Time Series Untuk Mendukung Pertumbuhan Ekonomi Hijau
Pages: 1499-1511
Copyright (c) 2025 Yudistira Bagus Pratama, Nurzaidah Putri Dalimunthe, Mega Sukma
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).
