PENGGUNAAN REMOTE SENSING DAN GOOGLE TRENDS UNTUK ESTIMASI PRODUK DOMESTIK BRUTO INDONESIA

Firhand Yusuf Kamal; Mutiara Indryan Sari; Maulidya Fan Ghul Udzan Utami; Fitri Kartiasih

doi:10.25134/equi.v21i02.9455

Firhand Yusuf Kamal Politeknik Statistika STIS
Mutiara Indryan Sari Politeknik Statistika STIS
Maulidya Fan Ghul Udzan Utami Politeknik Statistika STIS
Fitri Kartiasih Politeknik Statistika STIS

DOI: https://doi.org/10.25134/equi.v21i02.9455

Keywords: google trends, gross domestic product, nighttime light, remote sensing

Abstract

Abstrak

Pembangunan ekonomi merupakan salah satu topik yang penting untuk dikaji karena memberi gambaran tingkat kesejahteraan suatu negara. Akan tetapi, kebutuhan data yang menggambarkan pembangunan ekonomi, khususnya Produk Domestik Bruto (PDB) belum tersedia secara real-time. Penggunaan big data, seperti Night Time Light (NTL) dan Google Trends, untuk mengestimasi PDB menjadi salah satu solusi permasalahan tersebut. Meskipun begitu, kedua jenis big data tersebut masih memiliki kekurangan sebagai proxy untuk variabel PDB. NTL memiliki kekurangan karena data yang dihasilkan tidak mampu membedakan apakah cahaya yang ditangkap merupakan cahaya yang berasal dari listrik atau cahaya temporal, seperti api, pantulan cahaya, dan lain sebagainya. Begitu pula dengan Google Trends yang masih memiliki kekurangan dimana kata kunci yang digunakan tidak selalu merepresentasikan pola perilaku masyarakat secara konsisten. Metode penelitian ini bersifat kuantitatif dengan menggunakan data NTL dan Google Trends yang dievaluasi dan dibandingkan untuk melihat jenis data mana yang terbaik dalam menghasilkan estimasi PDB. Hasil penelitian menunjukkan bahwa penggunaan NTL, Google Trends, dan gabungan keduanya dapat digunakan untuk memprediksi PDB. Hal tersebut dapat terlihat dari hasil pemodelan yang tidak bersifat overfitting dan memiliki nilai MAPE di bawah 10%. Selain itu, penggunaan gabungan kedua data tersebut menjadi pilihan terbaik dalam mengestimasi PDB yang ditunjukkan dengan hasil evaluasi terbaik, yakni nilai RMSE sebesar 15792,73 dan nilai MAPE sebesar 0,52%.

Kata kunci: google trends; produk domestik bruto; night time light; remote sensing

Abstract

Economic development is an important topic to study because it provides an overview of the level of welfare of a country. However, the need for data that describes economic development, especially Gross Domestic Product (GDP), is not yet available in real-time. The use of big data, such as Night Time Light (NTL) and Google Trends, to estimate GDP is one solution to this problem. Even so, both types of big data still have shortcomings as proxies for GDP variables. NTL has shortcomings because the data produced is unable to distinguish whether the light captured is light originating from electricity or temporal light, such as fire, reflected light, and so on. Likewise, Google Trends still has shortcomings in that the keywords used do not always consistently represent people's behavior patterns. This research method is quantitative using NTL and Google Trends data which are evaluated and compared to see which type of data is best in producing GDP estimates. The research results show that the use of NTL, Google Trends, and a combination of both can be used to predict GDP. This can be seen from the modeling results which are not overfitting and have a MAPE value below 10%. Apart from that, using a combination of these two data is the best choice in estimating GDP as shown by the best evaluation results, namely an RMSE value of 15792.73 and a MAPE value of 0.52%.

Keywords: google trends; gross domestic product; nighttime light; remote sensing

References

Abidin, M. I., Notodiputro, K. A., & Sartono, B. (2021). Improving Classification Model Performances using an Active Learning Method to Detect Hate Speech in Twitter: Peningkatan Kinerja Model Klasifikasi dengan Pembelajaran Aktif dalam Mendeteksi Ujaran Kebencian di Twitter. Indonesian Journal of Statistics and Its Applications, 5(1), 26–38. https://doi.org/10.29244/ijsa.v5i1p26-38

Adwendi, S. J., & Kartiasih, F. (2016). Penggunaan Error Correction Mechanism dalam Analisis Pengaruh Investasi Langsung Luar Negeri Terhadap Pertumbuhan Ekonomi Indonesia. Statistika: Journal of Theoretical Statistics and Its Applications, 16(1), 17–27. https://doi.org/10.29313/jstat.v16i1.1767

Afrianto, F., & Graha, D. (2023). Morfologi Kota Malang: Sebuah Tinjauan dari Nighttime Light Satellite Imagery. 3, 68–76. https://doi.org/10.36456/jpb.v3i2.7002

Aguilar, M., & Xavier, K. (2022). A comparison between machine learning and classic algorithms for GDP forecast [Master thesis, Universitat Politècnica de Catalunya]. https://upcommons.upc.edu/handle/2117/375350

Akbar, I. A., & Kurniawan, R. (2020). Pemodelan Nowcasting Tingkat Pengangguran Terbuka Menggunakan Data Google Trends Dengan Metode Antlion Optimization-Support Vector Regression. Seminar Nasional Official Statistics, 2020(1), 60-770. https://doi.org/10.34123/semnasoffstat.v2020i1.504.

Arora, V. S., McKee, M., & Stuckler, D. (2019). Google Trends: Opportunities and limitations in health and health policy research. Health Policy, 123(3), 338-341. https://doi.org/10.1016/j.healthpol.2019.01.001.

Badan Pusat Statistik. (2022). Produk Domestik Bruto Indonesia 2018-2022. Jakarta: BPS RI.

Bank Indonesia. (2019). Perkembangan Ekonomi Global. Perkembangan Ekonomi Keuangan dan Kerja sama Internasional, Jakarta, Bank Indonesia.

Bantis, E., Clements, M. P., & Urquhart, A. (2022). Forecasting GDP growth rates in the United States and Brazil using Google Trends. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2022.10.003.

Bouayad, I., Zahir, J., & Ez-Zetouni, A. (2022). Nowcasting and Forecasting Morocco GDP growth using Google Trends data. IFAC-PapersOnLine, 55(10), 3280–3285. https://doi.org/10.1016/j.ifacol.2022.10.129

Bro, R., & Smilde, A. K. (2014). Principal Component Analysis. Analytical methods, 6(9), 2812-2831.

Brown, R. H., Vitullo, S. R., Corliss, G. F., Adya, M., Kaefer, P. E., & Povinelli, R. J. (2015). Detrending Daily Natural Gas Consumption Series to Improve Short-Term Forecasts. 2015 IEEE Power & Energy Society General Meeting. doi:10.1109/pesgm.2015.7286138

Chen, J., Zhao, F., Sun, Y., & Yin, Y. (2020). Improved XGBoost model based on genetic algorithm. International Journal of Computer Applications in Technology, 62(3), 240. https://doi.org/10.1504/IJCAT.2020.106571

Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785

Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Coefficient of Determination R-Squared is More Informative Than SMAPE, MAE, MAPE, MSE and RMSE in Regression Analysis Evaluation. PeerJ Computer Science, 7, 1–24. https://doi.org/10.7717/PEERJ-CS.623

Choi, H. & Varian, H. (2012). Predicting the Present with Google Trends. The Economic Record, 88, 2-9. DOI: 10.1111/j.1475-4932.2012.00809.x.

Das, S., Seferbekov, S., Datta, A., Islam, M. S., & Amin, M. R. (2021). Towards Solving the DeepFake Problem: An Analysis on Improving DeepFake Detection Using Dynamic Face Augmentation. 3776–3785. https://openaccess.thecvf.com/content/ICCV2021W/RPRMI/html/Das_Towards_Solving_the_DeepFake_Problem_An_Analysis_on_Improving_DeepFake_ICCVW_2021_paper.html

Fadillah, K., & Kartiasih, F. (2023). The effect of COVID-19 and population mobility on the underemployment rate in Indonesia. Jurnal Kependudukan Indonesia, 18(2), 217–236. https://doi.org/10.55981/jki.2023.2037

Fauzan, F. D., Rayhan, D. A., Putri, H. M., & Kartiasih, F. (2024). Peramalan Konsentrasi PM2.5 Menggunakan Model ARCH/GARCH dan Long Short-Term Memory (Studi Kasus Kota Jakarta Pusat). INFOMATEK: Jurnal Informatika, Manajemen Dan Teknologi, 26(1), 27–44. https://doi.org/10.23969/infomatek.v26i1.12603

Ferrara, L., & Simoni, A. (2023). When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage. Journal of Business and Economic Statistics, 41(4), 1188–1202. https://doi.org/10.1080/07350015.2022.2116025

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451

Ginting, C. P., & Kartiasih, F. (2019). Analisis Ekspor Kopi Indonesia ke Negara-Negara ASEAN. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(2), 143–157. https://doi.org/10.31849/jieb.v16i2.2922

Götz, T. B., & Knetsch, T. A. (2019). Google data in bridge equation models for German GDP. International Journal of Forecasting, 35(1), 45–66. https://doi.org/10.1016/j.ijforecast.2018.08.001

Guo, R., Zhao, Z., Wang, T., Liu, G., Zhao, J., & Gao, D. (2020). Degradation state recognition of piston pump based on ICEEMDAN and XGBoost. Applied Sciences, 10, 6593. https://doi.org/10.3390/app10186593

Hawari, R., & Kartiasih, F. (2017). Kajian Aktivitas Ekonomi Luar Negeri Indonesia Terhadap Pertumbuhan Ekonomi Indonesia Periode 1998-2014. Media Statistika, 9(2), 119. https://doi.org/10.14710/medstat.9.2.119-132

Hediyati, D., & Suartana, I. M. (2021). Penerapan Principal Component Analysis (PCA) Untuk Reduksi Dimensi Pada Proses Clustering Data Produksi Pertanian di Kabupaten Bojonegoro. JIEET (Journal of Information Engineering and Educational Technology), 5(2), 49-54. https://doi.org/10.26740/jieet.v5n2.p49-54.

Heikkinen, J. (2019). Nowcasting GDP Growth Using Google. Master’s Thesis.

Hindle, B. R., Keogh, J. W. L., & Lorimer, A. V. (2020). Validation of Spatiotemporal and Kinematic Measures in Functional Exercises Using a Minimal Modeling Inertial Sensor Methodology. Sensors, 20(16), Article 16. https://doi.org/10.3390/s20164586

Hodson, T. O. (2022). Root-Mean-Square Error (RMSE) or Mean Absolute Error (MAE): When to Use Them or Not. Geoscientific Model Development, 15(14), 5481–5487. https://doi.org/10.5194/gmd-15-5481-2022

Innayah, A. N., Sulistiana, D. I., Febrian, M. Y., & Kartiasih, F. (2024). Utilizing The Sarima Model and Support Vector Regression to Forecast Monthly Rainfall in Bandung City. JITTER (Jurnal Ilmiah Teknologi Informasi Terapan), 10(2), 88–104. https://doi.org/10.33197/jitter.vol10.iss2.2024.1663

Jange, B. (2022). Prediksi Harga Saham Bank BCA Menggunakan XGBoost. ARBITRASE: Journal of Economics and Accounting, 3(2), Article 2. https://doi.org/10.47065/arbitrase.v3i2.495

Jiang, Y., Tong, G., Yin, H., & Xiong, N. (2019). A Pedestrian Detection Method Based on Genetic Algorithm for Optimize XGBoost Training Parameters. IEEE Access, 7, 118310–118321. https://doi.org/10.1109/ACCESS.2019.2936454

Jolliffe, I. T., & Cadima, J. (2016). Principal Component Analysis: A Review and Recent Developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065). https://doi.org/10.1098/rsta.2015.0202

Jovic, A., Brkic, K., & Bogunovic, N. (2015). A review of feature selection methods with applications. 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 1200-1205. doi:10.1109/mipro.2015.7160458.

Kapetanakis, D.S., Mangina, E., & Finn, D. P. (2017). Input variable selection for thermal load predictive models of commercial buildings. Energy and Buildings, 137, 13–26. doi:10.1016/j.enbuild.2016.12.016.

Kartiasih, F. (2019). Dampak Infrastruktur Transportasi Terhadap Pertumbuhan Ekonomi Di Indonesia Menggunakan Regresi Data Panel. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(1), 67–77. https://doi.org/10.31849/jieb.v16i1.2306

Kartiasih, F. (2019). Transformasi Struktural dan Ketimpangan Antardaerah di Provinsi Kalimantan Timur. INOVASI: Jurnal Ekonomi, Keuangan Dan Manajemen, 15(1), 105–113. https://doi.org/https://doi.org/10.30872/jinv.v15i1.5201

Kusumasari, A., & Kartiasih, F. (2017). Aglomerasi Industri dan Pengaruhnya Terhadap Pertumbuhan Ekonomi Jawa Barat 2010-2014. Jurnal Aplikasi Statistika & Komputasi Statistik, 9(2), 28–41. https://doi.org/https://doi.org/10.34123/jurnalasks.v9i2.143

Latifa, A., Primadani, A. D. P., Fitriyyah, N. R., & Kartiasih, F. (2023). Mapping and Estimating the Impact of Drought on Food Crop Farmers Using Remote Sensing in East Nusa Tenggara Province. TheJournalish: Social and Government, 4(5), 309–335.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014b). The Parable of Google Flu: Traps in Big data Analysis. Science, 343(6176), 1203–1205. https://doi.org/10.1126/science.1248506

Lever, J., Krzywinski, M., & Altman, N. (2017). Points of Significance: Principal Component Analysis. Nature Methods, 14(7), 641–642. doi:10.1038/nmeth.4346.

Li, M., Wang, F., Jia, X., Li, W., Li, T., & Rui, G. (2021). Multi-source data fusion for economic data analysis. Neural Computing and Applications, 33, 4729-4739.

Li, X., Ge, L., & Chen, X. (2014). Quantifying Contribution of Land Use Types to Nighttime Light Using an Unmixing Model. IEEE Geoscience and Remote Sensing Letters, 11(10), 1667–1671. https://doi.org/10.1109/LGRS.2014.2304496

Marcilio, W. E., & Eler, D. M. (2020). From explanations to feature selection: Assessing SHAP values as feature selection mechanism. 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 340–347. https://doi.org/10.1109/SIBGRAPI51738.2020.00053

Ningsih, Y. P., & Kartiasih, F. (2019). Dampak Guncangan Pertumbuhan Ekonomi Mitra Dagang Utama terhadap Indikator Makroekonomi Indonesia. Jurnal Ilmiah Ekonomi Dan Bisnis, 16(1), 78–92. https://doi.org/https://doi.org/10.31849/jieb.v16i1.2307

OECD. (2020). Tracking GDP growth in real time. https://www.oecd.org/economy/weekly-tracker-of-gdp-growth

Pandika Pinata, N. N., Sukarsa, I. M., & Dwi Rusjayanthi, N. K. (2020). Prediksi Kecelakaan Lalu Lintas di Bali dengan XGBoost pada Python. Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 188. https://doi.org/10.24843/JIM.2020.v08.i03.p04

Panigrahi, S., & Behera, H. S. (2013). Effect of Normalization Techniques on Univariate Time Series Forecasting Using Evolutionary Higher Order Neural Network. International Journal of Engineering and Advanced Technology, 3(2), 280-285.

Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S., & Mohammadian, A. (Kouros). (2020). Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accident Analysis & Prevention, 136, 105405. https://doi.org/10.1016/j.aap.2019.105405

Pemayun, A. A. G. R. B. D., Azizi, M. Z., Daulay, N. A., Apriliani, N. H., & Kartiasih, F. (2024). Estimation of Java GRDP in Regency/City Level: Satellite Imagery and Machine Learning Approaches. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), X(2), 379–386. http://dx.doi.org/10.33330/jurteksi.v10i2.2993

Putri, S. R., Wijayanto, A. W., & Sakti, A. D. (2022). Developing Relative Spatial Poverty Index Using Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study of East Java, Indonesia. ISPRS International Journal of Geo-Information, 11(5). https://doi.org/10.3390/ijgi11050275

Qureshi, S., Chu, B. M., & Demers, F. S. (2020). Forecasting Canadian GDP Growth Using XGBoost (No. 20-14). Carleton University, Department of Economics.

Shi, K., Yu, B., Huang, Y., Hu, Y., Yin, B., Chen, Z., Chen, L., & Wu, J. (2014). Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data. Remote Sensing, 6(2), Article 2. https://doi.org/10.3390/rs6021705

Sitepu, A. A., Tantular, B., Darmawan, G., Pontoh, R. S., & Faidah, D. Y. (2023). Pemodelan Produk Domestik Bruto (Pdb) Dengan Pendekatan Vector Error Correction Model (VECM). PRIMER : Jurnal Ilmiah Multidisiplin, 1(2), Article 2. https://doi.org/10.55681/primer.v1i2.50Song, F., Guo, Z., & Mei, D. (2010). Feature Selection Using Principal Component Analysis. 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization. 27-30. doi:10.1109/icsem.2010.14

Song, F., Guo, Z., & Mei, D. (2010). Feature Selection Using Principal Component Analysis. Proceedings - 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2010, 1, 27–30. https://doi.org/10.1109/ICSEM.2010.14

Subian, A. R., Mulkan, D. A., Ahmady, H. H., & Kartiasih, F. (2024). Comparison Methods of Machine Learning and Deep Learning to Forecast The GDP of Indonesia. SISTEMASI: Jurnal Sistem Informasi, 13(1), 149–166. https://doi.org/10.32520/stmsi.v13i1.3445

Tinungki, G. M., & Sunusi, N. (2019). Penerapan Sparse Principal Component Analysis dalam Menghasilkan Matriks Loading yang Sparse. Jurnal Matematika, Statistika Dan Komputasi, 15(2), 44-53. https://doi.org/10.20956/jmsk.v15i2.5713.

Umam, K., & Kartiasih, F. (2023). Resiliensi Wilayah terhadap Shock Pandemi COVID-19 : Studi Kasus Kabupaten/Kota di Pulau Jawa. Jurnal Ekonomi Indonesia, 12(3), 283–304. https://doi.org/10.52813/jei.v12i3.415

Weley, I. R., Kumenaung, A. G., & Sumual, J. I. (2019). Analisis Pengaruh Inflasi dan Produk Domestik Regional Bruto (PDRB) Terhadap Pendapatan Asli Daerah di Kota Manado. Jurnal Pembangunan Ekonomi dan Keuangan Daerah, 19(3). https://doi.org/10.35794/jpekd.16457.19.3.2017

Widayati, C. S. W. (2009). Komparasi Beberapa Metode Estimasi Kesalahan Pengukuran. Jurnal Penelitian Dan Evaluasi Pendidikan, 13(2), Article 2. https://doi.org/10.21831/pep.v13i2.1409

Zhang, C., Vinyals, O., Munos, R., & Bengio, S. (2018). A Study on Overfitting in Deep Reinforcement Learning (arXiv:1804.06893). arXiv. http://arxiv.org/abs/1804.06893.