Pengembangan Aplikasi Berbasis Web untuk Pengelolaan Data Tekstual dan Otomatisasi Dokumen Pertimbangan Teknis Pertanahan
Bibliographic record
Abstract
Document preparation for land technical considerations, such as assignment letters, minutes, invitations, attendance lists, and minutes, has traditionally been done manually using word processing software. This process tends to be time-consuming and potentially leads to errors and formatting inconsistencies. This research aims to design and build a web-based application capable of automating the creation of land documents. The development method uses the waterfall model with stages of needs analysis, system design, implementation, and testing. Trials were conducted using the black box testing method to assess the system's functionality and reliability in producing documents according to standard formats. The results indicate that the application is capable of automatically producing documents with a high level of accuracy, maintaining format uniformity, and minimizing the potential for errors. This application has proven efficient, easy to use, and effective in supporting land administration. Scientifically, this research contributes to the modernization of services with information technology-based innovation. Penyusunan dokumen dalam kegiatan pertimbangan teknis pertanahan, seperti surat tugas, berita acara, undangan, daftar hadir, dan risalah, selama ini dilakukan manual menggunakan perangkat lunak pengolah kata. Proses tersebut cenderung memakan waktu lebih lama serta berpotensi menimbulkan kesalahan dan inkonsistensi format. Penelitian ini bertujuan merancang dan membangun aplikasi berbasis web yang mampu mengotomatisasi pembuatan dokumen pertanahan. Metode pengembangan menggunakan model waterfall dengan tahapan analisis kebutuhan, perancangan sistem, implementasi, dan pengujian. Uji coba dilakukan dengan metode black box testing untuk menilai fungsionalitas dan keandalan sistem dalam menghasilkan dokumen sesuai format standar. Hasil penelitian menunjukkan aplikasi mampu menghasilkan dokumen secara otomatis dengan tingkat akurasi tinggi, keseragaman format terjaga, serta meminimalkan potensi kesalahan. Aplikasi ini terbukti efisien, mudah digunakan, dan efektif dalam mendukung administrasi pertanahan. Secara ilmiah, penelitian berkontribusi terhadap modernisasi layanan dengan inovasi berbasis teknologi informasi.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".