PERSEPSI MASYARAKAT TERHADAP KEBERADAAN DAM WAE ELA DI DESA NEGERI LIMA, KABUPATEN MALUKU TENGAH
Bibliographic record
Abstract
This study aims to evaluate public perceptions of the existence of the Wae Ela Dam in Negeri Lima, Leihitu District, Central Maluku. The background of this study is based on the importance of understanding the attitudes and views of local communities towards strategic infrastructure development, especially dams that have a direct impact on daily life. The focus of the study was directed at assessing the benefits of the dam, such as irrigation, clean water provision, and tourism potential, as well as public concerns about the risk of disasters and environmental damage. Data collection was carried out through a closed questionnaire distributed to 50 local respondents, then analyzed descriptively quantitatively to obtain a general picture of public perception. The results showed that the majority of respondents gave a positive assessment of the benefits of the dam, although there were still concerns regarding the potential risks it posed. This finding indicates a supportive but vigilant public attitude. The main conclusion of this study recommends the need to improve dam management, socialize risks, and involve the community in the decision-making process in order to create sustainability and a sense of shared security. ABSTRAKPenelitian ini bertujuan untuk mengevaluasi persepsi masyarakat terhadap keberadaan Bendungan Wae Ela di Negeri Lima, Kecamatan Leihitu, Maluku Tengah. Latar belakang penelitian ini didasari oleh pentingnya pemahaman mengenai sikap dan pandangan masyarakat lokal terhadap pembangunan infrastruktur strategis, khususnya bendungan yang memiliki dampak langsung pada kehidupan sehari-hari. Fokus penelitian diarahkan pada penilaian manfaat bendungan, seperti irigasi, penyediaan air bersih, dan potensi wisata, serta kekhawatiran masyarakat terhadap risiko bencana dan kerusakan lingkungan. Pengumpulan data dilakukan melalui kuesioner tertutup yang disebarkan kepada 50 responden lokal, kemudian dianalisis secara deskriptif kuantitatif untuk memperoleh gambaran umum persepsi masyarakat. Hasil penelitian menunjukkan bahwa mayoritas responden memberikan penilaian positif terhadap manfaat bendungan, meskipun tetap terdapat kekhawatiran terkait potensi risiko yang ditimbulkan. Temuan ini mengindikasikan adanya sikap masyarakat yang mendukung namun tetap waspada. Simpulan utama penelitian ini merekomendasikan perlunya peningkatan pengelolaan bendungan, sosialisasi mengenai risiko, serta pelibatan masyarakat dalam proses pengambilan keputusan guna menciptakan keberlanjutan dan rasa aman bersama.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".