Analisis Spasio-Temporal Perkembangan Lahan Terbangun Menggunakan Pendekatan Machine Learning
Bibliographic record
Abstract
Abstract. Bekasi City is a buffer city for DKI Jakarta, Bogor, and Tangerang, which is a destination for urbanization and the search for new jobs. This condition causes an increase in population every year, which has a direct impact on the increasing need for space and the expansion of built-up land. This study aims to identify changes and developments in built-up land in Bekasi City from 1998 to 2023. The method used is a descriptive quantitative approach by utilizing secondary data in the form of Landsat 5 and Landsat 7 satellite imagery. Data analysis was carried out by classifying built-up land using a machine learning algorithm in the form of random forest, and testing accuracy using a confusion matrix. The results show that the area of built-up land in Bekasi City has increased by 4,264 Ha over 25 years. The development of built-up land tends to expand to the south, especially in the districts of Pondok Melati, Jatiasih, Jatisampurna, Mustika Jaya, and Bantar Gebang. Abstrak. Kota Bekasi merupakan kota penyangga DKI Jakarta, Bogor, dan Tangerang yang menjadi tujuan urbanisasi serta pencarian lapangan pekerjaan baru. Kondisi ini menyebabkan terjadinya pertambahan jumlah penduduk setiap tahunnya, yang berdampak langsung pada peningkatan kebutuhan ruang dan perluasan lahan terbangun. Penelitian ini bertujuan untuk mengidentifikasi perubahan dan perkembangan lahan terbangun di Kota Bekasi dari tahun 1998 hingga 2023. Metode yang digunakan adalah pendekatan kuantitatif deskriptif dengan memanfaatkan data sekunder berupa citra satelit Landsat 5 dan Landsat 7. Analisis data dilakukan dengan klasifikasi lahan terbangun menggunakan algoritma machine learning berupa random forest, serta pengujian akurasi menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa luas lahan terbangun di Kota Bekasi mengalami peningkatan sebesar 4.264 Ha selama 25 tahun. Perkembangan lahan terbangun cenderung meluas ke bagian selatan, terutama di Kecamatan Pondok Melati, Jatiasih, Jatisampurna, Mustika Jaya, dan Bantar Gebang.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".