Analisis pengaruh variabel mikro dan makro ekonomi terhadap pembiayaan bermasalah pada bank umum syariah di Indonesia: Periode 2015-2020
Bibliographic record
Abstract
INDONESIA: \n \nPeneitian ini bertujuan untuk mengidentifikasi faktor-faktor yang menentukan Non Performing Finnacing (NPF) bank umum syariah di Indonesia, serta menganalisis apakah terdapat efek spasial secara geografis pada Non Performing Financing (NPF) bank umum syariah di Indonesia. Objek pada penelitian ini adalah bank umum syariah selama kuartal pertama periode tahun 2015 hingga kuartal keempat tahun 2020. Variabel dalam penelitian ini adalah Loan to Deposit Ratio (LDR), Biaya Operasional Terhadap Pendapatan Operasional (BOPO), Capital Adequacy Ratio (CAR), Return On Asset (ROA), Inflasi, Gross Domestic Product (GDP), dan BI Rate. Metode penelitian yang digunakan adalah analisis regresi spasial data panel. Hasil penelitian menunjukkan bahwa secara simultan BOPO, ROA, Inflasi, GDP, dan BI Rate mempengaruhi tingkat NPF, sementara LDR dan CAR tidak mempengaruhi tingkat NPF bank umum syariah. Sedangkan secara parsial LDR, BOPO, CAR, ROA, Inflasi, GDP, dan BI Rate mempengaruhi tingkat NPF. \n \nENGLISH: \n \nThis study aims to identify the factors that determine the Non-Performing Financing (NPF) of Islamic commercial banks in Indonesia, as well as to analyze whether there is a geographically spatial effect on the Non-Performing Financing (NPF) of Islamic commercial banks in Indonesia. The object of this study is Islamic commercial banks during the first quarter of 2015 to the fourth quarter of 2020. The variables in this study are Loan to Deposit Ratio (LDR), Operating Costs to Operating Income (BOPO), Capital Adequacy Ratio (CAR), Return On Assets (ROA), Inflation, Gross Domestic Product (GDP), and BI Rate. The research method used is spatial regression analysis of panel data. The results showed that simultaneously BOPO, ROA, Inflation, GDP, and BI Rate affected the NPF level, while LDR and CAR did not affect the NPF level of Islamic commercial banks. While partially LDR, BOPO, CAR, ROA, Inflation, GDP, and BI Rate affect the level of NPF. \n \nARABIC: \n \nتهدف هذه الدراسة إلى تحديد العوامل التي تحدد التمويل المتعثر للبنوك التجارية الإسلامية في إندونيسيا ، وكذلك لتحليل ما إذا كان هناك تأثير جغرافي ومكاني على التمويل المتعثر للبنوك التجارية الإسلامية في إندونيسيا. الهدف من هذه الدراسة هو البنوك التجارية الإسلامية خلال الربع الأول من عام 2015 إلى الربع الرابع من عام 2020. والمتغيرات في هذه الدراسة هي نسبة القرض إلى الودائع (LDR) ، وتكاليف التشغيل إلى الدخل التشغيلي (BOPO) ، ونسبة كفاية رأس المال (CAR) ، والعائد على الأصول (ROA) ، والتضخم ، والناتج المحلي الإجمالي (GDP) ، ومعدل BI. طريقة البحث المستخدمة هي تحليل الانحدار المكاني لبيانات اللوحة. أظهرت النتائج أن BOPO و ROA والتضخم والناتج المحلي الإجمالي ومعدل BI أثرت في وقت واحد على مستوى NPF ، في حين لم يؤثر LDR و CAR على مستوى NPF للبنوك التجارية الإسلامية. وفي الوقت نفسه ، يؤثر LDR و BOPO و CAR و ROA والتضخم والناتج المحلي الإجمالي ومعدل BI جزئيًا على مستوى NPF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".