Problematika Penerbitan Sertipikat Elektronik di Kantor Pertanahan Kabupaten Sleman
Bibliographic record
Abstract
The issuance of electronic land certificates at the Sleman Land Office represents a key component of Indonesia’s national digital transformation agenda for land administration, formally initiated on May 31, 2024, through Ministerial Decree of ATR/BPN No. 285/SK-OT.01/III/2024. The implementation was carried out in two phases, each generating distinct implications for service performance and institutional readiness. This study aims to assess the implementation process and identify the challenges encountered in issuing electronic land certificates at the Sleman Land Office. A qualitative research method was applied using observations, interviews, and document analysis. The findings indicate several critical issues, including insufficient land data quality, limited human resources, inadequate information technology infrastructure, evolving policy frameworks, and budgetary constraints. Despite these challenges, the office has undertaken several corrective measures, such as improving data quality, enhancing human resource capacity, optimizing technological infrastructure, developing internal SOPs, and prioritizing budget allocation. This study underscores the importance of data readiness, human capital, institutional systems, and regulatory support as determining factors for the successful implementation of electronic land certificates. Keywords: Electronic Certificate, Digital Transformation, Data Quality, Land Services Digitalization, Kantor Pertanahan Kabupaten Sleman INTISARI Penerbitan sertipikat elektronik di Kantor Pertanahan Kabupaten Sleman merupakan bagian dari agenda nasional transformasi digital layanan pertanahan yang secara resmi dimulai pada 31 Mei 2024 melalui Keputusan Menteri ATR/BPN Nomor 285/SK-OT.01/III/2024. Implementasi dilakukan dalam dua fase yang masing-masing membawa konsekuensi terhadap kinerja pelayanan dan kesiapan kelembagaan. Penelitian ini bertujuan untuk menilai proses implementasi serta mengidentifikasi berbagai tantangan yang muncul dalam penerbitan sertipikat elektronik di Kantor Pertanahan Kabupaten Sleman. Metode penelitian yang digunakan adalah kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan studi dokumen. Hasil penelitian menunjukkan adanya sejumlah permasalahan utama, seperti kualitas data pertanahan yang belum memadai, keterbatasan sumber daya manusia, infrastruktur teknologi informasi yang kurang optimal, dinamika perubahan kebijakan, serta keterbatasan anggaran. Meskipun demikian, berbagai langkah perbaikan telah dilakukan, termasuk peningkatan kualitas data, penguatan kapasitas SDM, optimalisasi infrastruktur, penyusunan SOP internal, serta penajaman prioritas dalam manajemen anggaran. Penelitian ini menegaskan bahwa kesiapan data, sumber daya manusia, sistem kelembagaan, dan dukungan regulasi merupakan faktor kunci keberhasilan implementasi sertipikat elektronik. Kata Kunci: Sertipikat Elektronik, Transformasi Digital, Kualitas Data, Digitalisasi layanan Pertanahan, Kantor Pertanahan Kabupaten Sleman
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".