EFEKTIVITAS PROGRAM SISTEM ADUAN LANSIA TERLANTAR (Si AduLT) PADA PANTI PELAYANAN SOSIAL LANJUT USIA SUDAGARAN BANYUMAS
Bibliographic record
Abstract
Sistem Aduan Lansia Terlantar (Si AduLT) merupakan inovasi dari pelayanan kesejahteraan sosial lansia terlantar pada Panti Pelayanan Sosial Lanjut Usia Sudagaran Banyumas Dinas Sosial Provinsi Jawa Tengah. Si AduLT menyediakan layanan aduan berbasis teknologi informasi dan penjangkauan terhadap kasus lansia terlantar di Jawa Tengah, khususnya di Banyumas, Purbalingga, dan Banjarnegara. Sejak diluncurkan pada tahun 2022 hingga tanggal 31 Agustus 2024, Si AduLT telah menangani 153 aduan kasus lansia terlantar. Dalam perjalanannya, Si AduLT menemui keberhasilan dan juga kendala di lapangan. Oleh karena itu, penelitian ini bertujuan untuk mengkaji efektivitas program Si AduLT menggunakan pendekatan service effectiveness. Penelitian menggunakan metode kualitatif. Pengumpulan data menggunakan wawancara mendalam dan studi dokumentasi. Teknik analisis data yang digunakan yaitu analisis tematik. Hasil penelitian menunjukkan bahwa terdapat perubahan pada aspek kondisi Penerima Manfaat Si AduLT ke arah peningkatan keberfungsian. Aspek kualitas layanan mengindikasikan bahwa para petugas Si AduLT telah melaksanakan program secara optimal. Pada aspek kepuasan Penerima Manfaat, diketahui bahwa persepsi Penerima Manfaat terhadap Si AduLT sangat baik. Oleh karena itu, dapat disimpulkan bahwa pelaksanaan program Si AduLT sudah efektif. Manfaatnya telah dirasakan secara luas oleh masyarakat. Namun, peningkatan efektivitas tetap perlu dilakukan khususnya pada aspek kualitas layanan dan kepuasan Penerima Manfaat.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".