Pengendalian Agrokimia Dalam Pertanian Karet Berbasis Pengelolaan Sampah (Pakar Balam): Inovasi CSR PT Pertamina EP Prabumulih Field
Bibliographic record
Abstract
Penggunaan agrokimia yang berlebihan dalam pertanian karet menimbulkan risiko bagi lingkungan maupun kesehatan petani, terutama akibat penggunaan asam format sebagai penggumpal lateks. Penelitian ini menganalisis Program PAKAR BALAM (Pengendalian Agrokimia dalam Pertanian Karet Berbasis Pengelolaan Sampah), sebuah inisiatif pemberdayaan masyarakat oleh PT Pertamina EP Prabumulih Field. Program ini mengintegrasikan pengelolaan sampah organik dan anorganik melalui sub-program: Mokusaku (produksi cuka kayu), Rindu Resik (daur ulang plastik), dan Muda Bersama (kompos organik). Hasil penelitian menunjukkan bahwa Mokusaku efektif menggantikan asam format berbahaya dalam proses penggumpalan lateks, sehingga mengurangi risiko kerja bagi petani. Selain itu, pengolahan limbah organik meningkatkan produktivitas pertanian lokal, sedangkan daur ulang plastik mendukung ekonomi sirkular. Berdasarkan analisis Social Return on Investment (SROI), diperoleh nilai sebesar 1,73, yang menunjukkan adanya dampak sosial-ekonomi positif. Nilai Indeks Kepuasan Masyarakat (IKM) juga tinggi, yakni 86,12% ('Sangat Baik'). Dengan demikian, PAKAR BALAM terbukti sebagai model CSR yang berkelanjutan melalui kombinasi pengelolaan sampah, substitusi agrokimia, dan pemberdayaan masyarakat untuk meningkatkan kesejahteraan petani serta ketahanan lingkungan di Kota Prabumulih.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".