Analisis Peramalan Inflasi Di Kota Balikpapan Menggunakan Metode ARIMA
Bibliographic record
Abstract
Inflasi yang tidak terkendali merupakan salah satu permasalahan dalam perekonomian suatu negara. Hal ini disebabkan karena inflasi dijadikan acuan untuk kebijakan moneter. Akan tetapi pengendalian laju inflasi relatif sulit dilakukan. Oleh karena itu diperlukan suatu peramalan laju inflasi yang akurat sehingga mampu memprediksi inflasi di masa yang akan datang. Penelitian ini bertujuan meramalkan inflasi di masa yang akan datang menggunakan metode ARIMA. Data yang digunakan dalam penelitian ini adalah inflasi Kota Balikpapan Januari 2016 sampai dengan Desember 2022. Dari hasil analisis metode ARIMA terbaik untuk meramalkan inflasi Kota Balikpapan adalah ARIMA([1,2,12],0,[6]) yang mempunyai nilai RMSE sebesar 0,22886. Penelitian lanjutan yang dapat dilakukan untuk memperbaiki akurasi peramalan inflasi Kota Balikpapan adalah penggunaan metode gabungan ataupun menambahkan variabel independen yang mampu menjelaskan inflasi Kota Balikpapan di masa yang akan datang.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".