Analisis Prediksi Rasio Solvabilitas Pada Bank Mega Syariah Menggunakan Metode ARIMA BOX-JENKINS
Bibliographic record
Abstract
In 2023 it is estimated that there will be a world economic recession which will affect all countries, including Indonesia. Islamic banking companies must anticipate and minimize the risks that will occur as a result of the global recession. What can be done by Islamic banking is to make a plan. This type of research used is quantitative research. The method used in this study is the Box-Jenkins ARIMA method using secondary data obtained from quarterly financial reports of Bank Mega Syariah quarter I 2008 to quarter IV 2022 with a total of 60 data and forecasting for quarter I 2023 to quarter IV 2024. The solvency ratio forecasting model at Bank Mega Syariah for the Debt to Asset Ratio obtains the ARIMA forecasting model (0.1.4) with a significance value of 0.0003 <0.05 and forecasting results for the next 8 quarters will experience fluctuating movements with an average value of 38.82% and can be said to be good because the value of the Debt to Asset Ratio is still relatively low. Meanwhile, for the Debt to Equity Ratio, get the ARIMA forecasting model (4,1,0) with a significance value of 0.0484 <0.05 and the forecasting results for the next 8 quarters will continue to decline with an average value of 50.52% and it can be said good because the Debt to Equity Ratio is still relatively low.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".