Dinamika Pengaruh Perubahan Guna Lahan terhadap Harga Lahan di Kawasan Wisata Perkotaan Pangandaran
Bibliographic record
Abstract
Abstract The growth of the tourism sector in Pangandaran Regency, designated as a National Tourism Strategic Area (KSPN), has driven significant land use changes that directly impact land value increases. This study aims to identify the dynamics of land use change and analyze its influence on land valuation based on market prices and Tax Object Sales Value (NJOP). The research employed spatial analysis (overlay) to examine land use changes over two periods (2016–2019 and 2020–2023) and applied land valuation methods using both market price and NJOP approaches.The results indicate substantial shifts in land use patterns, especially following the KSPN designation. Land previously used for agriculture and shrubland has been converted into commercial, service, and tourism-related uses. Market-based land valuation rose from IDR 418 billion to IDR 1.07 trillion, while NJOP-based valuation increased from IDR 79.7 billion to IDR 241.8 billion. The highest increase occurred in tourism and commercial land categories. In conclusion, the KSPN designation has accelerated land use conversion and significantly boosted land value. These findings are essential for informing spatial planning policies and controlling land price dynamics in strategic tourism areas. Abstrak. Pertumbuhan sektor pariwisata di Kabupaten Pangandaran sebagai Kawasan Strategis Pariwisata Nasional (KSPN) mendorong terjadinya perubahan guna lahan yang berdampak signifikan terhadap kenaikan harga lahan. Penelitian ini bertujuan mengidentifikasi dinamika perubahan guna lahan serta menganalisis pengaruhnya terhadap valuasi harga lahan berdasarkan harga pasar dan Nilai Jual Objek Pajak (NJOP). Metode yang digunakan meliputi analisis spasial (overlay) untuk melihat perubahan penggunaan lahan pada dua periode (2016–2019 dan 2020–2023) serta analisis valuasi lahan dengan pendekatan harga pasar dan NJOP.Hasil penelitian menunjukkan adanya perubahan signifikan dalam pola penggunaan lahan, terutama setelah penetapan KSPN. Terjadi konversi lahan dari pertanian dan semak belukar menjadi lahan perdagangan, jasa, dan pariwisata. Valuasi berdasarkan harga pasar meningkat dari Rp418 miliar menjadi Rp1,07 triliun, sementara berdasarkan NJOP meningkat dari Rp79,7 miliar menjadi Rp241,8 miliar. Kenaikan tertinggi tercatat pada lahan fasilitas pariwisata dan perdagangan. Kesimpulannya, penetapan KSPN mempercepat perubahan guna lahan dan meningkatkan nilai lahan secara drastis. Hasil ini penting sebagai acuan perumusan kebijakan penataan ruang dan pengendalian harga lahan di kawasan wisata strategis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".