Analysis of Financial Performance of Pt. Bank Muamalat Indonesia for the 2020-2024 Period (Horizontal and Ratio Methods)
Bibliographic record
Abstract
This study aims to analyze the financial performance of PT. Bank Muamalat Indonesia for the 2020–2024 period using horizontal analysis methods and financial ratios. Horizontal analysis is carried out on the balance sheet statement as well as comprehensive income and income statements, while financial ratio analysis refers to the provisions of Bank Indonesia through the CAMEL approach which focuses on capital, asset, earning, and liquidity aspects. The results showed that in terms of balance sheet, total assets and investments in securities experienced a significant increase, while current accounts, savings, deposits, third-party funds, liabilities, equity, and investments in net associated entities showed fluctuating developments. Horizontal analysis of the income statement shows unstable conditions, where the comprehensive profit for the current period increased only in 2022, while in 2021, 2023, and 2024 it actually decreased. Furthermore, the results of the financial ratio analysis show that Bank Muamalat has a capital adequacy ratio (CAR) which is in the very healthy category throughout the research period, as well as good asset quality with a controlled Non-Performing Financing (NPF) level. However, the bank's profitability is still low with a return on asset (ROA) ratio that is in the unhealthy category, as well as the BOPO efficiency ratio which is in the unhealthy category due to high operating expenses. On the other hand, the level of bank liquidity measured through the financing to deposit ratio (FDR) is in the very healthy category, although it shows indications of less than optimal financing distribution. Overall, Bank Muamalat Indonesia has major strengths in terms of capital and liquidity, but still faces serious challenges in increasing profitability and operational efficiency in order to maintain competitiveness in the Islamic banking industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".