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Record W4411780283 · doi:10.35965/ijlf.v7i2.6092

ANALISIS PELAKSANAAN TANGUNG JAWAB KANTOR PERTANAHAN KABUPATEN MAMUJU TENGAH ATAS TERJADINYA SENGKETA TANAH YANG BERSERTIFIKAT GANDA

2025· article· id· W4411780283 on OpenAlexaff
Fredy Fredy, Baso Madiong, Andi Tira

Bibliographic record

VenueIndonesian Journal of Legality of Law · 2025
Typearticle
Languageid
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Penelitian ini bertujuan untuk menganalisis efektivitas pelaksanaan tanggung jawab Kantor Pertanahan dalam penyelesaian sengketa tanah bersertifikat ganda dan mengidentifikasi faktor-faktor penyebab terjadinya sertifikat ganda di Kabupaten Mamuju Tengah. Metode penelitian yang digunakan adalah penelitian hukum empiris dengan pendekatan kualitatif. Sumber data hukum dalam penelitian ini terdiri dari data primer, sekunder, dan tersier. Data primer mencakup hasil wawancara dari informan. Data sekunder berasal dari peraturan perundang-undangan, buku, dan jurnal, sedangkan data tersier berupa kamus dan media sebagai penunjang. Selain itu, data diperoleh melalui wawancara dengan pejabat Kantor Pertanahan Kabupaten Mamuju Tengah. Hasil penelitian menunjukkan bahwa dengan terjadinya sengketa tanah yang bersertifikat ganda saat ini Kantor Pertanahan Kabupaten Mamuju Tengah telah melakukan penelusuran, verifikasi kepemilikan dan penyelesaian sengketa tanah. Namun, masih terdapat kendala dalam akurasi data, koordinasi antarinstansi, dan pencatatan perubahan data. Pengawasan terhadap tanah bersertifikat juga masih memerlukan peningkatan. Faktor-faktor yang menyebabkan terjadinya sertifikat ganda meliputi kerumitan birokrasi, kurangnya koordinasi antar lembaga, keterbatasan sumber daya manusia dan teknologi, serta pengaruh mafia tanah. Konflik sering dipicu oleh dokumen yang tidak lengkap dan data yang tidak sinkron. Penyelesaian sengketa tanah bersertifikat ganda di Mamuju Tengah masih kurang optimal, dipengaruhi oleh keterbatasan teknologi, lemahnya koordinasi, dan tantangan dalam harmonisasi hukum formal dengan norma adat setempat. Pendekatan berbasis musyawarah namun efektivitasnya sangat bergantung pada kompetensi aparat dan ketersediaan sarana serta prasarana. This study aims to (1) analyze the effectiveness of the implementation of the Land Office's responsibilities in resolving dual-certified land disputes and (2) identify the factors causing dual certificates in Mamuju Tengah District. The research method used is empirical legal research with a qualitative approach. The sources of legal data in this study consist of primary, secondary, and tertiary data. Primary data includes the results of interviews with informants. Secondary data comes from laws and regulations, books, and journals, while tertiary data is in the form of dictionaries and media as supporting materials. In addition, data was obtained through interviews with officials of the Mamuju Tengah District Land Office. The results of the study indicate that with the occurrence of dual-certified land disputes, the Mamuju Tengah District Land Office has currently conducted tracing, verification of ownership, and resolution of land disputes. However, there are still obstacles in data accuracy, coordination between agencies, and recording data changes. Supervision of certified land also still needs improvement. Factors causing double certificates include bureaucratic complexity, lack of coordination between institutions, limited human resources and technology, and the influence of land mafia. Conflicts are often triggered by incomplete documents and unsynchronized data. The resolution of dual-certified land disputes in Central Mamuju is still less than optimal, influenced by limited technology, weak coordination, and challenges in harmonizing formal law with local customary norms. The approach is based on deliberation but its effectiveness is highly dependent on the competence of the apparatus and the availability of facilities and infrastructure.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.315
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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