Pemanfaatan Sistem Informasi Kependudukan Di Perumahan Villa Permata Regensi 2 Berbasis Web
Bibliographic record
Abstract
Residential Villa Permata Tangerang is one of the housing in Regensi 2 Sangiang area. The data collection process for residents who live is still being carried out using a data collection book so that the system that is running is not good enough. The current problem is that the RT head has difficulty knowing the number of residents living in Villa Tangerang Housing because the citizen data is not updated, the RT head does not know how many residents are contracting in the Villa Permata Tangerang area, the RT head has difficulty providing assistance to residents who are less fortunate because there is no data on poor people or data collection is done when there is an event giving assistance only so that many poor people do not get help, the head of the RT has difficulty finding information on residents who are married, unmarried, widows, widowers, orphans or orphans, and information on residents who died. Based on the current problems, a population data processing system was created that can help housing parties manage population data starting from citizen status, occupation, age, and family card data collection. This system is created using the PHP programming language and MySQL database. The analysis method uses PIECES, the development method uses the waterfall and the test uses the blackbox system. This research produces a population system that can assist housing in managing population data. Keywords—Population, Data Collection, Citizen Status, Housing Perumahan Villa Permata Tangerang adalah salah satu Perumahan yang berada di daerah Regensi 2 sangiang. Proses pendataan warga yang tinggal masih dilakukan dengan menggunakan buku pendataan sehingga sistem yang berjalan belum cukup baik. Permasalahan yang terjadi saat ini adalah Ketua RT kesulitan untuk mengetahui jumlah warga yang tinggal di Perumahan Villa Tangerang dikarenakan data warga yang tidak terupdate, ketua RT tidak mengetahui jumlah warga yang mengontrak di daerah Villa Permata Tangerang berjumlah berapa, ketua RT kesulitan untuk memberikan bantuan untuk warga yang kurang mampu karena tidak adanya data warga yang kurang mampu atau pendataan dilakukan pada saat ada even pemberian bantuan saja sehingga banyak warga yang kurang mampu tidak mendapatkan bantuan, ketua RT kesulitan mengetahui informasi warga yang berstatus sudah menikah, belum menikah, janda, duda, yatim atau piatu, dan informasi warga yang meninggal. Berdasarkan permasalahan yang terjadi saat ini maka dibuat sistem pengolahan data kependudukan yang dapat membantu pihak perumahan mengelola data kependudukan mulai dari status warga, pekerjaan, usia, dan pendataan kartu keluarga. Sistem ini dibuat menggunakan Bahasa pemrograman PHP dan database Mysql. Metode analisis menggunakan PIECES, metode pengembangan menggunakan waterfall dan pengujian menggunakan blackbox system. Penelitian ini menghasilkan sistem kependudukan yang dapat membantu pihak perumahan dalam mengelola data kependudukan. Kata Kunci—Kependudukan, Pendataan, Status Warga, Perumahan
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.020 |
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".