Identifikasi Faktor yang Berpengaruh dalam Transformasi Nilai Lahan di SWK Gedebage
Bibliographic record
Abstract
Abstract. The land transformation in the Sub-City Area (SWK) of Gedebage, as a new growth center of Bandung City, has triggered significant changes in land value. This study aims to identify and map the variables influencing land value transformation before and after development, using the Matrix of Cross Impact Multiplications Applied to Classification (MICMAC) method. Data were collected through questionnaires and interviews with key stakeholders, and analyzed based on the influence and dependence levels among variables. The results indicate that variables such as the Availability of Green Open Space (KRTH) and Quality of Regional Infrastructure (KIW) are key factors in future land value shifts. The changing positions of variables between pre- and post-development phases highlight a complex systemic dynamic. These findings provide a foundation for formulating more adaptive and sustainable spatial policies for future development in the Gedebage area. Abstrak. Transformasi lahan di Sub Wilayah Kota (SWK) Gedebage sebagai pusat pertumbuhan baru Kota Bandung telah memicu perubahan signifikan terhadap nilai lahan. Penelitian ini bertujuan mengidentifikasi dan memetakan faktor-faktor yang memengaruhi perubahan nilai lahan sebelum dan sesudah pengembangan, menggunakan metode Matrix of Cross Impact Multiplications Applied to Classification (MICMAC). Data diperoleh melalui kuesioner dan wawancara dengan aktor kunci serta dianalisis berdasarkan tingkat pengaruh dan ketergantungan antar faktor. Hasil menunjukkan bahwa faktor seperti Ketersediaan Ruang Terbuka Hijau (KRTH) dan Kualitas Infrastruktur Wilayah (KIW) menjadi faktor kunci dalam perubahan nilai lahan di masa depan. Pergeseran posisi faktor antara pra dan pasca pengembangan menandakan adanya dinamika sistem yang kompleks. Temuan ini memberikan dasar perumusan kebijakan spasial yang lebih adaptif dan berkelanjutan untuk pengembangan kawasan Gedebage ke depan.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".