PERAMALAN INDEKS HARGA PROPERTI RESIDENSIAL MENGGUNAKAN METODE BAYES
Bibliographic record
Abstract
Residential property is a property in the form of building which serves as residence or house. House has function as a place whether to take a rest, to take cover and to get together with family. Residential property price indices (RPPIs) forecasting has aim as a development planning by the developer to avoid shortages or excess of home supplies. This research aims to model and predict the RPPIs using the Bayes method for 2020 to 2021. The data used in this research is the data from RPPIs of Denpasar city from 2012 in the first quarter to 2019 in the fourth quarter. Then, the method which is used is Bayes method with autoregression (AR) model in forecasting RPPIs. Therefore, it obtained mean absolute percentage error (MAPE) for forecasting the next one period with () equal to 0,4049416%. For the result of RPPIs forecasting in Denpasar city from 2020 the first quarter to 2021 the fourth quarter has an insignificant increase with an average difference for each quarter increased by 0,3568%.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".