RANCANG BANGUNSISTEM INFORMASI VERIFIKASI JAMINAN BERBASI KOMPUTER(STUDI KASUS DI BANK BNI SYARIAH SEMARANG)
Bibliographic record
Abstract
ABSTRAK Tahapan verifikasi jaminan yang ada di Bagian Pembiayaan BNI Syariah meliputi, perhitungan total taksasi dan plotting jaminan. Dimana dalam melakukan pengolahan datanya masih manual sehingga berakibat pada lamanya pemrosesan pengajuan pembiayaan. Studi kasus yang diambil pada penelitian ini yaitu pada Bank BNI Syariah Cabang Semarang untuk membangun sistem informasi verifikasi jaminan. Perancangan sistem informasi verifikasi jaminan dilakukan melalui analisa Normalisasi. Dengan berbasiskan web, aplikasi Sistem Informasi verifikasi jaminan ini dapat digunakan sebagai sarana penyedia layanan dan informasi bagi penggunanya baik untuk staf pembiayaan selaku admin, dan pejabat bank yang berwenang, maupun nasabah dimanapun dan kapanpun mereka berada. Sistem verifikasi jaminan ini akan memiliki beberapa kelebihan dibandingkan sistem sebelumnya seperti efesiensi dalam pengolahan data, mempercepat pencarian, akses yang lebih cepat, data yang lebih terorganisir, otomasi laporan. Pengguna mendapatkan informasi yang akurat karena informasi yang tersedia senantiasa diperbaharui. Aplikasi ini akan lebih baik jika memiliki keamanan data yang lebih tinggi dan penambahan fitur. ABSTRACT Warranty verification stages in BNI Syariah Financing Section include, calculation and plotting of the total Assessed guarantee. Where in the manual data processing resulting in the length of processing the filing of financing. Case study taken in this research is on Bank BNI Syariah Semarang to build a system of verification of information assurance. The design of information systems security verification is done through analysis of the DFD (Data Flow Diagram) and Normalisation Table. With web-based, application security verification information system can be used as a means for service providers and users of information both to finance staff as admin, and bank officials, and customers wherever and whenever they are. This guarantees a verification system would have several advantages over previous systems such as efficiency in data processing, speed up the search, access is faster, more organized data, automated reporting. Users get accurate information because the information available constantly updated. This application would be better if it has a higher data security and enhanced features
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".