Strategi Peningkatan Nilai Manfaat Ekonomi Sumber Daya Alam di Kecamatan Seruyan Hulu
Bibliographic record
Abstract
Abstract. Indonesia is rich in natural resources, and economic growth theory suggests that natural resource endowment significantly influences regional economic development. Theoretically, regions with abundant resources should experience advanced economic growth. However, in Seruyan Regency, particularly Seruyan Hulu Sub-District, this theory does not hold. Despite its wealth in agriculture, plantations, fisheries, and forestry, Seruyan Regency has the highest poverty rate in Central Kalimantan Province at 7.08% in 2024, with Seruyan Hulu recording the highest poverty figure of 5,037 individuals. This study aims to formulate strategies to enhance the economic benefits of natural resources in Seruyan Hulu. Using the market price method, the economic value of natural resources in Seruyan Hulu is estimated at IDR 89,559,855,393. Proposed strategies to increase this value include improving basic infrastructure, enhancing natural resource management, and improving human resource quality and access to capital. Abstrak. Indonesia merupakan salah satu negara yang kaya akan sumber daya alam, menurut teori pertumbuhan ekonomi menjelaskan bahwasanya potensi kekayaan sumber daya alam, juga dikenal sebagai endowment sumber daya alam, faktor ini sangat mempengaruhi dan menentukan perkembangan ekonomi sebuah wilayah. Secara teori wilayah dengan kekayaan sumber daya alam yang potensial memiliki perkembangan ekonomi yang lebih maju. Namun pada kenyataanya kondisi yang terjadi di Kabupaten Seruyan terkhusus Kecamatan Seruyan Hulu tidak sesuai dengan teori ini, Kabupaten Seruyan sebagai wilayah yang memiliki kekayaan sumber daya alam mulai dari pertanian, perkebunan, perikanan dan kehutanan namun menjadi kabupaten yang memiliki persentase kemiskinan paling besar di Provinsi Kalimantan Tengah sebesar 7,08% pada tahun 2024 dan kecamatan di Kabupaten Seruyan dengan angka kemiskinan tertinggi adalah Kecamatan Seruyan Hulu sebanyak 5.037. Maka dari itu tujuan dari penelitian ini yaitu merumuskan strategi untuk meningkatkan nilai manfaat ekonomi sumber daya alam di Kecamatan Seruyan Hulu. Pada hasil analisis market price method diketahui bahwa nilai manfaat Sumber Daya Alam yang ada di Kecamatan Seruyan Hulu adalah sebesar Rp. 89.559.855.393, maka dirumuskan strategi untuk meningkatkan nilai manfaat sumber daya alam yang terdiri dari upaya peningkatan sarana dan prasana dasar, upaya peningkatan sumber daya alam dan upaya peningkatan kualitas SDM dan akses modal.
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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 | 0.000 |
| 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".