Akbari, E., Boloorani, A. D., Samany, N. N., Hamzeh, S., Soufizadeh, S., & Pignatti, S. (2020). Crop mapping using random forest and particle swarm optimization based on multi-temporal sentinel-2. Remote Sensing, 12(9), 1–21. https://doi.org/10.3390/RS12091449

Andiko, J. A., . D., & Darmawan, A. (2019). Efisiensi Penggunaan Citra Multisensor untuk Pemetaan Tutupan Lahan. Jurnal Sylva Lestari, 7(3), 342. https://doi.org/10.23960/jsl37342-349

Badan Pusat Statistik. (2022). Jumlah Penduduk Menurut Provinsi di Indonesia (Ribu Jiwa), 2020-2022.

Ciptaningtyas, D., & Suhardiyanto, H. (2016). Sifat Thermo-Fisik Arang Sekam. Jurnal Teknotan, 10(2), 1–6. https://doi.org/10.24198/jt.vol10n2.1

Dimitrov, P., Dong, Q., Eerens, H., Gikov, A., Filchev, L., Roumenina, E., & Jelev, G. (2019). Sub-pixel crop type classification using PROBA-V 100 m NDVI time series and reference data from Sentinel-2 classifications. Remote Sensing, 11(11). https://doi.org/10.3390/rs11111370

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031

Gumma, M. K., Tummala, K., Dixit, S., Collivignarelli, F., Holecz, F., Kolli, R. N., & Whitbread, A. M. (2020). Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information. Geocarto International, 0(0), 1–17. https://doi.org/10.1080/10106049.2020.1805029

Kurniantoro, A., Uktoro, A. I., & Hermantoro. (2023). Pemanfaatan Drone Terintegrasi SIG Untuk Pemetaan Tanaman Jagung. 1(01), 47–60.

Moumni, A., & Lahrouni, A. (2021). Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area. Scientifica, 2021. https://doi.org/10.1155/2021/8810279

Muhammad, A. M., Rombanf, J. A., & Saroinsong, F. B. (2016). Tutupan lahan di KPHP Poigar terus menerus mengalami perubahan . Pemetaan dan identifikasi jenis tutupan lahan dengan metode Maximum Likelihood lebih akurat dari metode. Cocos, 7(2).

Nugroho, U. C., Kushardono, D., & Dewi, E. K. (2019). Identifikasi Kawasan Pertambangan Timah Menggunakan Data Satelit Sentinel – 1 dengan Metode Object Based Image Analysis (OBIA). Jurnal Ilmu Lingkungan, 17(1), 140. https://doi.org/10.14710/jil.17.1.140-148

Pádua, L., Adão, T., Hruška, J., Sousa, J. J., Peres, E., Morais, R., & Sousa, A. (2017). Very high resolution aerial data to support multi-temporal precision agriculture information management. Procedia Computer Science, 121, 407–414. https://doi.org/10.1016/j.procs.2017.11.055

Palchowdhuri, Y., Valcarce-Diñeiro, R., King, P., & Sanabria-Soto, M. (2018). Classification of multi-temporal spectral indices for crop type mapping: A case study in Coalville, UK. Journal of Agricultural Science, 156(1), 24–36. https://doi.org/10.1017/S0021859617000879

Purwaningsih, Y. (2008). Ketahanan pangan: Situations, Problems, Policies and Community Empowerment. Jurnal Ekonomi Pembangunan, 9(1), 1–27.

Ramanda, D., Abdi, S., dan Bambang., S. (2018). Analisis Kombinasi Citra Sentinel-1a Dan Citra Sentinel-2a Untuk Klasifikasi Tutupan Lahan (Studi Kasus: Kabupaten Demak, Jawa Tengah). Jurnal Geodesi Undip, 7(2), 85–96.

Rusdiana, S., & Maesya, A. (2017). Pertumbuhan sosial ekonomi dan kebijakan pertanian. Agriekonomika, 6(1), 12–25.

Sampurno, R., & Thoriq, A. (2016). Klasifikasi Tutupan Lahan Menggunakan Citra Landsat 8 Operational Land Imager (Oli) Di Kabupaten Sumedang. Jurnal Teknotan, 10(2), 61–70. https://doi.org/10.24198/jt.vol10n2.9

Sjarif, A. C., Sc, M., Lisangan, E. A., & Kom, S. (2014). Web-Based Visualization dengan Menggunakan Metode Stacking Images stacking images. January 2012, 2–6. https://doi.org/10.13140/2.1.5026.4325

Suryamin. (2015). Pedoman Pelaksanaan Uji Coba Sistem Kerangka Sampel Area (KSA). Badan Pusat Statistik.

Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144(August), 325–340. https://doi.org/10.1016/j.isprsjprs.2018.07.017

Tran, K. H., Zhang, H. K., McMaine, J. T., Zhang, X., & Luo, D. (2022). 10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product. International Journal of Applied Earth Observation and Geoinformation, 107(November 2021), 102692. https://doi.org/10.1016/j.jag.2022.102692

Widyaningsih, M. (2015). Segmentasi Canny Dan Otsu Pada Citra Daun Jeruk Tidak Sehat. Semnaskit, 43–48.

Wouthuyzen, S., & Ahmad, F. (2018). Pemetaan Mangrove Kepulauan Lease, Provinsi Maluku Menggunakan Data Multi-Temporal Dan Multi-Sensor Citra Satelit Landsat. Oseanologi Dan Limnologi Di Indonesia, 3(1), 19. https://doi.org/10.14203/oldi.2018.v3i1.134

Wulansari, H. (2017). Uji Akurasi Klasifikasi Penggunaan Lahan dengan Menggunakan Metode Defuzzifikasi Maximum Likelihood Berbasis Citra Alos Avnir-2. BHUMI: Jurnal Agraria Dan Pertanahan, 3(1), 98. https://doi.org/10.31292/jb.v3i1.96

Xie, G., & Niculescu, S. (2022). Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France. Remote Sensing, 14(18), 1–27. https://doi.org/10.3390/rs14184437

Zhao, H., Duan, S., Liu, J., Sun, L., & Reymondin, L. (2021). Evaluation of five deep learning models for crop type mapping using sentinel-2 time series images with missing information. Remote Sensing, 13(14). https://doi.org/10.3390/rs13142790