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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".