Strategi Optimalisasi Pemulihan Fungsi Hutan di Kabupaten Bandung Barat
Bibliographic record
Abstract
Abstract. This study aims to formulate a strategy for optimizing reforestation and sustainable forest management in West Bandung Regency in response to increasing critical land and vegetation degradation. A mixed-methods approach (quantitative and qualitative) was used, with primary data collected through interviews with key stakeholders, including Perhutani, the Public Works and Spatial Planning Office, BAPPEDA, and LMDH. Secondary data were obtained from technical documents, spatial RTRW data, and forestry policy reviews. The analysis employed the SWOT (Strengths, Weaknesses, Opportunities, Threats) method using IFAS and EFAS matrices to determine the strategic position based on internal and external factor scores.The results show that internal strengths (total score 3.06) outweigh weaknesses (1.24), while external threats (1.88) slightly exceed opportunities (1.62). This places the strategy in Quadrant II (diversification), recommending the use of internal strengths to address external threats. The proposed strategy is plant diversification using a combination of pine, sengon, and coffee species, aimed at enhancing ecological functions while also supporting community income. Community participation and the integration of local wisdom are essential for ensuring long-term success in sustainable forest recovery efforts. Abstrak. Penelitian ini bertujuan untuk merumuskan strategi optimalisasi reboisasi dan pengelolaan hutan berkelanjutan di Kabupaten Bandung Barat sebagai respons terhadap meningkatnya lahan kritis dan degradasi vegetasi. Pendekatan penelitian menggunakan mix method (kuantitatif dan kualitatif) dengan pengumpulan data primer melalui wawancara terhadap stakeholder kunci, seperti Perhutani, Dinas PUTR, Bappeda, dan LMDH. Data sekunder diperoleh dari dokumen teknis, data spasial RTRW, serta kajian kebijakan kehutanan. Analisis dilakukan dengan metode SWOT (Strengths, Weaknesses, Opportunities, Threats) yang dikembangkan dalam bentuk matriks IFAS dan EFAS untuk menentukan posisi strategi terbaik berdasarkan nilai koordinat internal dan eksternal. Hasil analisis menunjukkan bahwa kekuatan internal (total skor 3,06) lebih dominan dibandingkan kelemahan (1,24), sedangkan dari sisi eksternal, ancaman (1,88) sedikit lebih tinggi dari peluang (1,62). Hal ini menempatkan strategi pada Kuadran II (diversifikasi), yang merekomendasikan penguatan potensi internal untuk menghadapi tantangan luar. Strategi yang disarankan adalah diversifikasi tanaman menggunakan sistem agroforestri berbasis kopi, pinus, dan sengon, yang tidak hanya memperkuat fungsi ekologi, tetapi juga meningkatkan pendapatan masyarakat. Pelibatan masyarakat dan kearifan lokal menjadi kunci keberhasilan jangka panjang pemulihan fungsi hutan secara berkelanjutan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".