Kajian Pengembangan Agroindustri Padi di Kabupaten Karawang
Bibliographic record
Abstract
Abstract. Karawang Regency has a high level of rice production and plays an important role in West Java agriculture, making it known as the national granary. However, despite the abundant rice production, its utilisation is not optimal due to limited advanced processing, which hampers the development of local industries. This study aims to assess the potential for developing rice-derived products to increase the added value of the regional economy. The methods used include surplus-deficit analysis and SWOT analysis. The calculation results show that from 2024 to 2031, Karawang has a sustainable surplus of raw materials. In 2024, there is a surplus of 295,132.58 tonnes of rice, 170,811.08 tonnes of husk, and 85,405.54 tonnes of rice bran. While in 2031, the surplus of rice is estimated to reach 253,834.58 tonnes, husk 163.870,34 tonnes, and rice bran 81.935,17 tonnes. This shows that the availability of raw materials is still sufficient until the end of the planning period. Abstrak. Kabupaten Karawang memiliki tingkat produksi padi yang tinggi dan berperan penting dalam pertanian Jawa Barat, sehingga dikenal sebagai lumbung padi nasional. Namun, meskipun produksi beras melimpah, pemanfaatannya belum optimal karena terbatasnya pengolahan lanjutan, sehingga menghambat perkembangan industri lokal. Penelitian ini bertujuan untuk mengkaji potensi pengembangan produk turunan beras untuk meningkatkan nilai tambah perekonomian daerah. Metode yang digunakan antara lain analisis surplus-defisit dan analisis SWOT. Hasil perhitungan menunjukkan bahwa dari tahun 2024 hingga 2031, Karawang memiliki surplus bahan baku yang berkelanjutan. Pada tahun 2024, terdapat surplus 295.132,58 ton beras, 170.811,08 ton sekam, dan 85.405,54 ton bekatul. Sedangkan pada tahun 2031, surplus beras diperkirakan mencapai 253.834,58 ton, sekam 163.870,34 ton, dan bekatul 81.935,17 ton. Hal ini menunjukkan bahwa ketersediaan bahan baku masih mencukupi hingga akhir periode perencanaan.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".