Evaluation of the Performance of the Investment and Integrated One-Stop Service Department (DPMPTSP) of Sidenreng Rappang Regency Through Public Satisfaction Survey
Bibliographic record
Abstract
Penelitian ini bertujuan melakukan evaluasi kinerja pelayanan Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu (DPMPTSP) Kabupaten Sidenreng Rappang melalui survei kepuasan Masyarakat. Metode yang digunakan pada penelitian ini adalah menggunakan pendekatan analisis deskriptif, analisis inferensial seperti analisis korelasi dan analisis kuadran, serta menambahkan analisis kualitatif menggunakan Word Cloud. Selain itu indikator yang digunakan serta tata cara pengukurannya mengacu pada pedoman Permenpan RB No 14 Tahun 2017. Teknik pengumpulan data dimulai dari pengambilan sampel secara Accidental sampling, yaitu memilih orang yang kebetulan ditemui di lokasi dan baru saja selesai dilayani oleh DPMPTSP, dilanjutkan dengan pengisian kuesioner dengan wawancara tatap muka. Berdasarkan hasil penelitian diperoleh nilai Indeks Kepuasan Masyarakat (IKM) DPMPTSP sebesar 3,101 berada pada kategori “Baik” atau nilai “B”, dengan nilai konversi 77,536. Berdasarkan analisis korelasi diperoleh korelasi besar antara perilaku petugas dan prosedur pelayanan dengan nilai 0,464 dengan kategori “korelasi cukup”. Sementara itu berdasarkan analisis kuadran diperoleh unsur layanan yang berada di kuadran II yang perlu dipertahankan yaitu unsur biaya/tarif dan sarana prasarana. Berdasarkan analisis kualitatif Word Cloud, masyarakat menilai kompetensi petugas dari cara melayani dengan cepat, mahir menggunakan komputer, dan melakukan pelayanan yang baik, sementara itu masyarakat menilai perilaku petugas berdasarkan murah senyum, sopan, dan ramah dalam berbicara.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".