Improving Quality of Land Data Towards Modern Land Administration in The Administrative City of West Jakarta
Bibliographic record
Abstract
Land data quality plays a pivotal role in advancing modern land administration, impacting both national economic growth and Indonesia’s competitive position in the global property index, where it currently ranks 106th in Ease of Doing Business. This research focuses on the need for systematic quality improvements in both physical (spatial) and juridical (textual) land data within the West Jakarta City Administration to address existing data deficiencies. Applying the Driver-Pressures-States-Impacts-Responses (DPSIR) framework, this study investigates key challenges, causal factors, and strategic interventions aimed at enhancing land data quality. In-depth interviews with stakeholders were conducted to elucidate these dynamics and inform the proposed strategies. The findings highlight the successful digitization and validation of 94.52% of 423,189 Land Book records, 69.03% of 494,071 Ownership Rights Plans/Measurement Letters, and the spatial data of 94.68% across 427,569 land parcels, thereby improving the Data Quality Classification (KW) across categories 4–6. This study underscores the critical importance of robust data validation processes in modernizing Indonesia’s Land Administration System (LAS), offering valuable insights for enhancing the efficiency, transparency, and reliability of land data, thereby supporting economic development and improving Indonesia’s standing in the global property market.Kualitas data pertanahan memainkan peran penting dalam memajukan administrasi pertanahan modern, yang berdampak pada pertumbuhan ekonomi nasional dan posisi kompetitif Indonesia dalam indeks properti global, di mana saat ini menduduki peringkat ke-106 dalam kemudahan berbisnis. Penelitian ini berfokus pada kebutuhan peningkatan kualitas data secara sistematis, baik fisik (spasial) maupun yuridis (tekstual), di Administrasi Kota Jakarta Barat untuk mengatasi kekurangan data yang ada. Dengan menerapkan kerangka Driver-Pressures-States-Impacts-Responses (DPSIR), studi ini menginvestigasi tantangan utama, faktor penyebab, dan intervensi strategis untuk meningkatkan kualitas data pertanahan. Wawancara mendalam dengan pemangku kepentingan dilakukan untuk menguraikan dinamika ini dan mendukung strategi yang diusulkan. Temuan menunjukkan keberhasilan digitalisasi dan validasi 94,52% dari 423.189 dokumen Buku Tanah, 69,03% dari 494.071 dokumen Rencana Hak Milik/Surat Ukur, serta data spasial dari 94,68% di 427.569 bidang tanah, sehingga meningkatkan Klasifikasi Kualitas Data (KW) di kategori 4–6. Penelitian ini menekankan pentingnya proses validasi data yang kuat dalam memodernisasi Sistem Administrasi Pertanahan (LAS) Indonesia, menawarkan wawasan berharga untuk meningkatkan efisiensi, transparansi, dan keandalan data pertanahan yang mendukung pembangunan ekonomi dan memperbaiki posisi Indonesia di pasar properti global.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".