Multi-criteria analysis of renewable energy alternatives in southwest Sumba using TOPSIS method with 5C framework
Bibliographic record
Abstract
Renewable energy development is important for improving energy security and economic growth in Indonesia. This study identifies the best renewable energy potential in Southwest Sumba, East Nusa Tenggara Province, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on 5C criteria: Consolidated, Controllable, Continue, Clean, and Cheap. The research uses a multi-criteria decision-making approach, using primary data from expert interviews and secondary data from literature reviews. The TOPSIS analysis shows that solar energy has the highest preference value, followed by bioenergy and hydropower. Technical assessments show important implementation requirements for each renewable energy option. The study recommends prioritizing solar energy development, supporting bioenergy projects, improving micro-hydro facilities, and creating clear renewable energy policies. Success depends on cooperation between stakeholders and aligning renewable energy development with regional sustainability and community needs. These efforts can help Southwest Sumba develop its renewable energy sector and contribute to national energy security goals.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".