Analisis Spasio-temporal Perubahan Tutupan Lahan Tahun 1990-2025 Menggunakan Metode GIS di Kota Cimahi
Bibliographic record
Abstract
Abstract. This study aims to analyze the spatio-temporal changes in land cover in Cimahi City for the years 1990, 2000, 2010, 2020, and 2025. Landsat satellite imagery was used as the primary data source and processed using the Google Earth Engine platform with the Maximum Likelihood Classifier algorithm and stratified random sampling technique. The classification results indicate a significant increase in built-up land area, particularly in the central and southern parts of the city, accompanied by a consistent decline in upright vegetation and water bodies. The classification validation produced a kappa value of 0.9143, indicating a very high level of accuracy. These findings illustrate a clear trend of increasing urban development, highlighting the need for spatial planning and sustainable land use control. Abstrak. Penelitian ini bertujuan untuk menganalisis perubahan spasio-temporal tutupan lahan di Kota Cimahi pada tahun 1990, 2000, 2010, 2020, dan 2025. Data citra satelit Landsat digunakan sebagai sumber utama dan diolah melalui platform Google Earth Engine dengan algoritma Maximum Likelihood Classifier dan teknik stratified random sampling. Hasil klasifikasi menunjukkan tren peningkatan luas lahan terbangun secara signifikan, terutama di wilayah tengah dan selatan kota, serta penurunan vegetasi tegak dan badan air. Validasi klasifikasi menghasilkan nilai kappa sebesar 0,9143 yang menandakan akurasi sangat baik. Temuan ini menggambarkan arah perubahan tutupan lahan yang semakin didominasi oleh pembangunan, sehingga menjadi dasar penting dalam perencanaan ruang dan pengendalian alih fungsi lahan yang berkelanjutan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".