Monitoring Dan Evaluasi Kinerja Pada Badan Perencanaan Pembangunan Daerah Kota Makassar Pada Program Penanganan Stunting
Bibliographic record
Abstract
Penelitian bertujuan untuk menganalisis faktor-faktor yang memengaruhi efektivitas pelaksanaan monitoring dan evaluasi (monev) dalam program penurunan stunting di Kota Makassar, dengan fokus pada peran Badan Perencanaan Pembangunan Daerah (Bappeda). Metode penelitian yang digunakan adalah kualitatif deskriptif, dengan teknik pengumpulan data melalui observasi, wawancara mendalam, dan studi dokumentasi. Data diperoleh dari informan kunci, termasuk pejabat struktural dan fungsional Bappeda, serta operator Aksi Bangda. Hasil penelitian menunjukkan bahwa pelaksanaan monev stunting di Kota Makassar telah berjalan dengan baik, didukung oleh alokasi anggaran yang memadai dan pelatihan kader posyandu. Namun, tantangan utama meliputi ketidakakuratan data, ego sektoral antar-organisasi perangkat daerah (OPD), serta rendahnya kesadaran masyarakat tentang pola asuh anak. Studi ini mengidentifikasi lima faktor kunci yang memengaruhi efektivitas monev, yaitu ketersediaan data, komitmen pemimpin, penggunaan teknologi informasi, koordinasi lintas sektor, dan partisipasi masyarakat. Rekomendasi yang diajukan meliputi penguatan sistem pendataan terintegrasi, peningkatan kapasitas kader, dan sosialisasi kebijakan yang lebih efektif. Temuan ini memberikan kontribusi praktis bagi perbaikan kebijakan penurunan stunting di tingkat daerah, khususnya dalam konteks pembangunan kesehatan berbasis bukti. This study aims to analyze the factors influencing the effectiveness of monitoring and evaluation (monev) implementation in stunting reduction programs in Makassar City, focusing on the role of the Regional Development Planning Agency (Bappeda). The research employs a descriptive qualitative method, with data collected through observation, in-depth interviews, and documentation studies. Key informants include structural and functional officials of Bappeda, as well as Aksi Bangda operators. The results indicate that stunting monev in Makassar City has been implemented effectively, supported by adequate budget allocation and posyandu cadre training. However, major challenges include data inaccuracy, sectoral egos among regional apparatus organizations (OPD), and low public awareness of child-rearing practices. This study identifies five key factors affecting monev effectiveness: data availability, leadership commitment, information technology utilization, cross-sector coordination, and community participation. Recommendations include strengthening integrated data systems, enhancing cadre capacity, and improving policy socialization. The findings provide practical contributions to refining stunting reduction policies at the regional level, particularly in evidence-based health development contexts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".