PERAMALAN BEBAN LISTRIK JANGKA MENENGAH DI WILAYAH TELUK KUANTAN DENGAN METODE FUZZY TIME SERIES CHENG
Bibliographic record
Abstract
Kebutuhan terhadap energi listrik saat ini semakin meningkat karena sebagian besar aspek kehidupan manusia bergantung pada ketersediaan energi listrik. Akibatnya pihak penyalur listrik harus mempersiapkan kebutuhan energi listrik yang semakin meningkat tersebut. Pihak penyalur listrik harus memiliki perencanaan yang baik dan tepat dalam pendistribusian energi listrik. Salah satu upaya yang dapat dilakukan untuk membantu perencanaan tersebut adalah melakukan peramalan beban listrik untuk waktu yang akan datang. Metode fuzzy time series (FTS) Cheng merupakan salah satu metode yang dapat dilakukan untuk peramalan data time series yang menggunakan prinsip-prinsip fuzzy sebagai dasarnya. Pada penelitian ini dilakukan peramalan beban listrik jangka menengah di wilayah Taluk Kuantan dengan metode FTS Cheng untuk beberapa bulan ke depan. Hasil peramalan yang diperoleh tersebut dihitung tingkat akurasi peramalannya dengan menggunakan Mean Absolute Percentage Error (MAPE) sehingga diperoleh tingkat akurasi sebesar 4.45%, yang artinya hasil peramalan beban listrik jangka menengah di wilayah Taluk Kuantan dengan metode FTS Cheng dikatakan sangat baik karena tingkat akurasi yang kurang dari 10%.Kata Kunci: Time Series, Beban Listrik, Fuzzy Time Series Cheng
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".