PENGARUH TINGKAT INFLASI, SUKU BUNGA BI, DAN NILAI TUKAR RUPIAH TERHADAP RETURN ON ASSET PERBANKAN
Bibliographic record
Abstract
ABSTRACT: This research aims to find out how inflation, BI interest rates, and rupiah exchange rates affect the return on assets of persero banks in Indonesia. The method used in this research is descriptive and verifikative research method. The data was obtained from the financial statements of persero banks, consisting of Bank Mandiri, Bank Tabungan Negara, Bank Negara Indonesia, and Bank Rakyat Indonesia which were published on the official website of the Financial Services Authority during the quarter of 2017 to the quarter of 2019. Sampling in this study is based on saturated sampling techniques, i.e. all members of the population are sampled. The data in this research is processed using SPSS software. Data processing and analysis techniques use multiple regression analysis. The results of this study showed that together (simultaneously) independent variables of Inflation Rate, BI Interest Rate, and Rupiah Exchange Rate had a significant effect on Bank Persero's ROA in Indonesia in 2017-2019. The test results partially showed that the Variable Inflation Rate negatively and significantly affects return on assets at the persero banks registered with the Financial Services Authority for the period 2017-2019, bi interest rate variables have no effect on Return On Assets on persero banks registered with the Financial Services Authority for the period 2017-2019, rupiah exchange rate variables have a positive and significant effect on Return On Assets on persero banks registered with the Financial Services Authority for the period 2017-2019. Based on the test results determining the amount of coefficient of determination of 18% while the remaining 82% is explained by other variables that are not included in the regression model equation. Keywords: Inflation Rate, Bi Interest Rate, Rupiah Exchange Rate, Return On Assets ABSTRAK: Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh inflasi, suku bunga BI, dan nilai tukar rupiah terhadap return on asset bank persero yang ada di Indonesia. Metode yang digunakan dalam penelitian ini adalah metode penelitian deskriptif dan verifikatif. Data diperoleh dari laporan keuangan bank persero, yang terdiri dari Bank Mandiri, Bank Tabungan Negara, Bank Negara Indonesia, dan Bank Rakyat Indonesia yang publikasi di website resmi Otoritas Jasa Keuangan selama triwulan tahun 2017 sampai dengan triwulan tahun 2019. Pengambilan sampel dalam penelitian ini didasarkan pada teknik sampling jenuh, yaitu semua anggota populasi dijadikan sampel. Data dalam penelitian ini diolah menggunakan software SPSS. Teknik pengolahan dan analisis data menggunakan analisis regresi berganda. Hasil dari penelitian ini menunjukkan hasil bahwa secara bersama-sama (simultan) variabel independen Tingkat Inflasi, Suku Bunga BI, dan Nilai Tukar Rupiah berpengaruh signifikan terhadap ROA Bank Persero di Indonesia tahun 2017-2019. Hasil pengujian secara parsial menunjukkan hasil bahwa variabel Tingkat Inflasi berpengaruh negatif dan signifikan terhadap Return On Asset pada bank persero yang terdaftar di Otoritas Jasa Keuangan periode 2017-2019, variabel Suku Bunga BI tidak memiliki pengaruh terhadap Return On Asset pada bank persero yang terdaftar di Otoritas Jasa Keuangan periode 2017-2019, variabel Nilai Tukar Rupiah berpengaruh positif dan signifikan terhadap Return On Asset pada bank persero yang terdaftar di Otoritas Jasa Keuangan periode 2017-2019. Berdasarkan hasil uji determinasi besarnya koefisien determinasi sebesar 18% sedangkan sisanya 82% dijelaskan oleh variabel lain yang tidak dimasukkan dalam persamaan model regresi. Kata Kunci: Tingkat Inflasi, Suku Bunga BI, Nilai Tukar Rupiah, Return On Assets.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".